case study of a child with dyslexia

Case Study: Ten year old child with severe dyslexia

This study discusses a ten year old Elementary School student with significant levels of dyslexia. Reading through this case study will help you recognize typical concerns, and possibly identify approaches and techniques to help you with your student. You will notice the weighing of factors and the considerations discussed. Every child is unique. No single overall approach applies to each and every child.

Student Profile

18 March 2014 Eric (M) 10 (Grade 2) Student ID ER3445752M Dyslexia Test https://www.dynaread.com/index.php?cid=testresults&pmp_id=ER3445752M646464

Input by Elaine Benton MA, with additional comments by Hans J.A. Dekkers. Both Dynaread Team members.

INPUT BASED ON PROVIDED BACKGROUND INFORMATION

School-provided information in italics.

Eric has been with us since kindergarten. Already then, he expressed difficulty learning letters and sounds, so when he moved to grade 1 we hoped with extra one-on-one help he would thrive. He didn't. At parent request and with school approval, he repeated.

ELAINE: From our perspective, looks like this was a very good decision.

His second time through was more successful, but when he hit grade 2 and had to start reading more, identifying more sight words, and writing sentences and short paragraphs, it was obvious that he didn't have the skills yet.

ELAINE: How poor is his writing? We tend to forget manual writing as we concentrate on reading but it can be such a painful, and not unrelated, issue that needs kind but concerted and steady attention.

ELAINE adds...: [Topic: About composition work with the limitations of low reading and handwriting removed]. The child tells/dictates an experience story (it could be a phrase, sentence or even a whole short story that they want to share) to the adult who writes it down and then uses the material that has been created as a text to be read. It ensures that the reading text only uses language that the child already knows and it's an excellent approach as long as the child is not able to parrot the story back from memory. If this is the case, the tutor should let the story go stale in memory until the child can't 'read' it entirely from memory. This is called the Language Experience Approach (LEA) and it is used with very, very basic readers. Reading teachers should really know or learn how to use this approach. It's hard to write as fast as they talk but its' worth it because this is a reading instruction technique that also helps them to begin to develop and order their thoughts cogently before they would otherwise be able to do so. It is, effectively, composition work with the limitations of low reading and handwriting removed.

HANS: Eric's test demonstrates extremely marginal literacy (near illiterate). In language development, a child progresses from listening to speaking, to reading, to writing, to complex authoring.

It is unreasonable to expect a near illiterate dyslexic to write. Copying, as part of a multi-modal, multi-sensory approach in learning to read: Yes. But writing originally composed short paragraphs or even short sentences on his own: No. This is simply out of reach and ability (based on his demonstrated reading skills in our test).

So he started Orton-Gillingham for a minimum of two hours per week, which continued daily until he began with the Dynaread program.

ELAINE (Certified Orton-Gillingham Remediator): [HANS: To be effective, in the perfect world...] Orton-Gillingham should really be done for a minimum of three one-hour long lessons per week ... with practice in between. Also, see below for recommendations regarding the type of OG program that is most beneficial.

He has no other learning disabilites, is not ESL, and is a very strong oral learner. Like many other dyslexics, if he could get through life orally, no one would probably realize how much he struggles with reading and writing.

I've been working with him this school year now that he's in grade 3. I see a bright boy who is willing to try anything I suggest. We've been focusing on Orton-Gillingham yet, hoping to hammer those skills in more and more. Last year, his retention of new information had about a 50% carry-over to regular seat work. This year, it's about 70%.

But while the rest of his class has moved on at grade level, many of them reading books at the 3-3.5 level, he is beginning to realize that his books at 1.8 level are "too easy" for the others. He's becoming less brave in reading out loud in class or volunteering information.

I think this is the year that he's either going to start feeling successful or start shutting down and turn into an attitude case. I believe that's also the reason he was so keen to try a new program like Dynaread, because he wants to keep up.

ELAINE: I understand where you're coming from but I've just had so many students who've had severe reading problems but have never shut down or had attitude problems. It's just essential that they, and we, find and emphasize other things that they're good at. For some kids it's the arts, or sports and for some its things we wouldn't normally think of ... like class pets, other games or just the fact that they have a good friend and/or the ability to make a lot of friends or become a leader ... so many possibilities and all it takes is one.

Please talk to the teacher about the reading out loud. Is it being done in larger groups? If so ..., no go. Reading pairs ... ok. Triads ... ok. Many more ... not so much.

HANS: Though I fully agree with the power of identifying and help internalizing one (or more) skills that Eric may excel in, researched statistics overwhelmingly evidence the grave risks of emotional shut down. Part of the solution is what Elaine shared, but part of it is also helping Eric understand that Dyslexia is not a curse, not something to be ashamed of, and something that actually comes with many benefits (if managed well, by him and those who raise him, and educate him). It may be a very good idea for his parents to buy the following book, and read it together with Eric. Not instead of identifying and endorsing his unique talent area(s), but alongside it.

The Dyslexic Advantage: Unlocking the Hidden Potential of the Dyslexic Brain by Brock L. Eide M.D. M.A. Permalink: http://www.amazon.ca/dp/0452297923

His teacher is very aware of his strengths and limitations and teaches to them. But all the interventions now lie on my plate, and I'm hoping to help him achieve some more success. Since all our students bus in (he's on the bus about 40 minutes), before/after school programs are not an option. Generally, we focus on math and reading/writing as crucial life skills, and if needed we minimize the time spent on social/science to help them keep up with math and reading. We try not take them out of music and art, because there's lots of research to suggest that those subjects also help out academically.

ELAINE: 40 minutes on a bus is really unfortunate ... I guess it has to be social time, a good time for kid books on tape or music, learning apps or, if it isn't embarrassing, easier books that he can read alone or with a friend.

HANS: Public libraries often have offerings of audio books in their collection. I myself use Audible.com by Amazon, which offers a high quality audio experience. Some people demonstrate the ability to listen with comprehension at faster rates, and Audible.com allows this. They offer a three month trial subscription for little money. It may be a way for him to progress in academics and overall development, through listening on the bus.

ELAINE: I totally agree with the effort to keep music and art ... unless he hates them. Personally, I don't think there's much extra benefit if the child isn't interested. On the other hand, how about something physical? Sport or building/making things? Would he be interested? It's just as beneficial ... or more so.

HANS: I am also familiar with the research on the benefits of music and art to overall academic development. We are not linear-thinking creatures. Music and arts help us to broaden our perspectives. And with a current lack of reading skills, this may help compensate. And if he happens to be good at it, will also boost his sense of self-worth.

He would not be retained any more in elementary, regardless of what grade level he achieves this year or in years to come.

ELAINE: I'm really curious about why this is the case. Is there room for negotiation here?

ELAINE adds...: Regarding repeating more than one school year in elementary school, do check in with the Ministry of Education to see if such a rule can actually be imposed by a school. I don't know the rules here but I do know that, in Ontario, this would rule would never stand.

HANS: When I read that statement, I concluded that you were primarily stating it as a fact. But fact or not, retention in a Grade when peers move on is very tough on a child, especially if the child -- like Eric -- seems very very eager to stay at par with his friends.

Rather than retaining, my preference would go out to assistive technologies, like Text to Speech and Audio Books, plus selecting an academic path for him which suits his talents and abilities. But... most certainly continuing to help him to Learn to Read, with Dynaread and possible continued augmentation of OG Phonics. I categorically do not see assistive technologies as replacement for learning to read. AT's are merely a means, and most certainly not an end. You may want to watch this video (possibly even together with Eric), in which I talk about the role of AT and the balances in handling Dyslexia: http://youtu.be/0wOLl3ZRcw4

YOUR TOP THREE OF WHAT YOU HOPE TO RECEIVE FROM OUR TEAM

1. how to boost his reading performance.

ELAINE shares... I would recommend the following to help boost Eric's reading performance.

(1) Dynaread. It is really quite obvious that Eric needs to increase his sight word reading vocabulary and improve his reading speed for the words that he knows. Dynaread will help him to do this as well or better than other programs. Truthfully, no bias. Full stop.

(2) Make sure that Eric is getting the kind of Orton-Gillingham program that he needs. In my experience, OG fails when children are taught phonic information but are not given enough opportunity to use it i.e. to recode (read and spell) a good number and a wide variety of words with target phonemes in the initial, final and middle positions. (in that order if you can). Application is a skill that has to be taught explicitly (for accuracy) and drilled (for speed) with individual words, phrases, sentences and short paragraphs. Systematic, explicit phonics instruction has to go hand in hand with systematic, explicit 'application instruction'.

(3) It would be excellent if Dynaread words could be included among the words used to teach application. Doing this would, effectively, cement and 'back up' already acquired sight words and make application easier at the same time.

(4) This is going to sound obvious but ... find something that he really wants to read. Try out everything. Let him choose and let him stay with what he loves for as long as he wants. Fiction, non-fiction, many authors, many topics, many formats, graphic/cartoons, colorful characters ... anything and, if he wants to read something that is too hard, simplify sections of it and, together, do it anyway. I can't do enough to stress how important this is. It's not rocket science but it can make all the difference in the world. When they find the right things, they just take off and you wonder what on earth just happened.

ELAINE adds... : Teachers/tutors can 'level' a text by summarizing, paraphrasing and shortening it ... with simpler words that they can definitely use with the child. It's effortful on the part of a tutor. They have to be good at paraphrasing and summarizing ... but it is a pretty common and effective technique. The child still reads and learns the content that interested him but he isn't asked to read beyond his own level.

The analogy between physical and reading disabilities isn't always appropriate. I have one severely dyslexic child who wanted to run. He was only interested in, and would only try to read, books about animals. The books he wanted were way above his level but, initially, at least, he only wanted the pictures and the facts ... so we/I ended up cherry picking facts from quite difficult books. We used the pictures and captions to learn the facts together. Initially, I did almost all of the reading but then we would pull out the simpler words to work on and learn together. The level of learning kept him motivated but the level of reading instruction stayed very low. I credit this technique, however, for his remarkable improvements. He is extremely motivated to increase his knowledge on his own, read those hard 'fact' words and those books on his own and he is now (9 months later) reading vocabulary that is way above his grade level. Easy texts just always bored and de-motivated him. Now he's excited. (the principles of CLAD clear language and design can be of great assistance here ex. line breaking).

I think the main thing, is to remember that the child is not expected to do these things on their own. It's about essential teacher/student 'scaffolding'; a gradual shift/transfer of responsibility and skill from teacher to student.

HANS: Personally, I would like to add a little balance here as well. We all know the paradigm from which she is reasoning: Inner drive and motivation can do so much more than any 'external' force. Though this may be true, it never brought my friend Matthijs with his quadriplegic condition to walking. Eric did not demonstrate mild dyslexia (rather: severe dyslexia). The risk of toying with reading materials whilst not really being able to read is that they contextually guess their way through the text. In that process, the orthography of one word gets coupled with the semantics and pronunciation of another, which effectively results in polluting their reading system with inaccurate information. If a child is making progress and starts to be able to read, then I can follow Elaine's argument, but personally -- based on Eric's demonstrated abilities in his Dyslexia Test -- I would judge this too early.

ELAINE continues...

(5) Separate reading and reading comprehension as much as possible. Concentrate on one of these at a time. Unless a child is extremely motivated and willing to do a lot of start-stop-recap and rerun ... try to do word decoding before or after you've read the text. Learn problematic words in advance ... read them for the student as you go along ... or read them with the student if you can do it fluently together. Motivation goes asunder when decoding effort is painful.

2. HOW TO HELP HIM SUCCEED WITH INCREASINGLY COMPLEX READING MATERIALS AS WE PREPARE HIM FOR END-OF-YEAR GOVERNMENT PROVINCIAL ACHIEVEMENT TESTING AND BEYOND

ELAINE shares...

With increasingly complex reading materials ... remember that there are two kinds of texts; ones that a child can read on their own and those that they can only attempt with help. You have to use both. Learning comes from 'the new' while mastery and pride comes with the independent practice. So, it's ok if they want to read easier texts if, together, you are also reading things that are more difficult. Harder things move into the 'easy' category and we leap frog along in that fashion.

Also, don't forget that reading depends on basic language and listening skills. And reading is not the only way to improve and expand them. The richer the child's language, knowledge and story-telling environment the better.

HANS: This point of Elaine I cannot stress enough. There is significant research demonstrating that children who have been read to lots when young, and who grow up in a verbally rich environment enjoy a language development advantage. As shared earlier, reading is merely a stage in overall language development. But it is crucially important to recognize two things here:

1. Initial reading merely couples the orthography of words to the already present verbal vocabulary of the child. This is where the rich verbal environment and the being-read-to comes in as an advantage. Audio books, likewise, can help here as well.

2. ... and the following is something I would like to do more structured research in one day... When you study the works of Chomsky and other linguists, you come to realize the role of reading in our ability to grow intellectually as well. We can only 'merge' ideas and concepts if we know them. We cannot combine e.g. flour, salt, and water to come up with bread if we have never heard of flour. Reading plays a significant role in expanding our overall know-how and understanding, resulting in enriching our access to individual ideas and concepts, which we can subsequently 'merge' into original new thinking and ideas. This point is obviously a bit out of Eric's direct-needs context, but it does argue for two things: (a) It is of great value to him, if we succeed in becoming a functional reader, and (b) exposure to audio books and other non-reading materials can help make up for what he misses out in reading. And my preference would go out to audio-books over e.g. videos, because books cover subjects in so much more detail and a video.

ELAINE continues... I really wouldn't worry, at all, about preparing Eric for the PAT test (or any other standardized test until he reaches the final years of high school). Teachers are often encouraged to 'teach to the test' for these events but, especially in Eric's case, this would be counterproductive. These tests are more about evaluating schools and school systems than they are about testing individuals. Eric will, of course, have to take the test with everyone else but it won't yield any specific knowledge that will be of much use to you. Keep him on his usual program.

HANS: I could not agree more with Elaine. If at all possible and/or permissible, I would not have him involved. At this point in Eric's life it would be the equivalent of asking Matthijs to participate in the Athletics test on running a quarter mile. It only pains him, and does not yield any advantage for Eric.

3. HOW TO OPTIMIZE OUTCOME AND POTENTIAL FOR A STUDENT LIKE ERIC, EVEN UTILIZING ASSISTIVE TECHNOLOGIES IF NEEDED

Get him onto Dynaread and ensure that his Orton-Gillingham program is systematic and explicit and stresses phonics application in spelling as well as reading. Do and try anything and everything to (1) find material that really motivates him (even if it wouldn't be your choice for him) and (2) other activities and friends that make his life meaningful and fun at school and at home. More than this? I don't think you can do too much more than this. Don't forget to appreciate, congratulate and reward yourself for all of your efforts. Eric is lucky to have you.

HANS: Building on what Elaine closed her paragraph with, your school displays remarkable commitment and ability. Keep it up!

Regarding assistive technologies, well that's a thorny issue. When should we start using them? I recommend that you keep them on a backburner for a while. Voice recognition programs are becoming more and more popular but there is still room for them to improve. There are pens and other scanners that will read text aloud for you; tools that I'd suggest to any adolescent or adult. And one can ask for extra time for tests and assignments that are graded; something that's really important as soon as poor reading skills begin to mask displays of subject knowledge and other practical skill development. These are all good tools but, I have a lot of experience teaching adults as well as children so I'm acutely aware of the fact that the early years are the best learning years. Unfortunately, it rarely gets easier than it is now. It would be a terrible thing to miss any of the potential of these years by moving into adaptive technologies too quickly.

HANS: I point back to my video again. I do believe there is good use for AT, though, but... NEVER at the expense of full throttle efforts to help Eric learn to read. These AT are often rolled out as RT's (my coined term: Replacement Technologies). AT's should remain assistive and never replace the effort to learn to read.

Lastly, allow me to refer you to a white paper by the International Dyslexia Association, on Accomodating Students with Dyslexia in All Classroom Settings. https://www.dynaread.com/accommodating-students-with-dyslexia

End of Case Study

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The following are some case studies of dyslexics with whom we have worked over the past years. In each story, we provide background information, the course of therapy that integrates the individual's strengths and interests, and the outcomes—all of which are positive.

Case Studies for using strengths and interests

Case Study One:

Grace has a diagnosis of dyslexia. She has trouble with visual scanning, processing, and working memory. She also has difficulties with spelling and sequencing for problem solving. She has strong verbal skills and is artistic abilities. She learns well with color and when her hands are occupied.

Grace struggled with note taking because of her difficulties with spelling and visual scanning (looking from the board to her paper). Furthermore, she could not keep up and got "lost" in the lecture (particularly for subjects that were already difficult for her). Grace’s teachers thought that she was not putting forth the effort, because they often saw her daydreaming in class. When the therapist asked Grace about this, she admitted that sometimes she would daydream because she did not know where they were in the lecture. She also desperately wanted to blend in with her peers, so she looked to them to see what she was supposed to be doing. However, when she was permitted to follow along with a book that she could highlight in and make her own doodles and notes in the margins during the lecture, she was able to focus her energy on the teacher and have notes that she could refer back to later with all of the main points highlighted. Using Grace's kinesthetic learning style and preference for color, she was able to participate with her peers, decrease her anxiety in class, and develop a skill that will help her to learn better across the curriculum.

Due to her difficulties with sequencing, working memory, and reading, Grace struggled with numerical operations and story problems in math. Her problem solving skills were good when she could leverage her strengths: connecting abstract ideas and thinking at the macro level. Hence, when she could connect a concept to a real life problem, she could inevitably come up with a creative solution and grasp the concept; however, her poor numerical operations skills were still holding her back. The therapist remembered Grace's interest in color and tactile learning style and introduced her to a number of "hands-on" ways of solving the problem: calculating probability with colored marbles, using her fingers for multiplication, and solving equations with objects to represent the variables. In this manner, Grace not only grasped the concept that was presented at the macro-level, but using her love of color and keeping her hands moving she could reliably solve for the answer. Employing colored pencils for numbering steps or placing hash marks in multi-step directions helped Grace stay on point and not skip steps in complex problems. These strategies were incorporated into her 504 Plan and were communicated to her math teacher.

Case Study Two:

Amy has a diagnosis of dyslexia. She enjoys creative writing, fashion, and art. She is extremely bright and has a strong memory. She benefits from rule-based instruction. If you tell her a rule once, she will be able to recite it to you the next time you see her. She delights in being able to be the teacher and teach the rules herself or correct others’ errors.

Amy’s stories often jumped around without any cohesion or plot. The clinician suggested that Amy work on her stories on a daily basis. Amy drafted her stories about glamorous people and enjoyed illustrating their wardrobes. Her clinician helped her to expand and revise her story using a multi-sensory tool to teach her the parts of story grammar. She was able to revise her own story, by adding the components of a good plot (characters, setting, initiating event, internal response, plan, and resolution). With several revisions, she produced a well-developed story and colorful illustration that was framed and displayed. The combination of using Amy’s interests, learning style, and a powerful reinforcement (framing and displaying the finished product) lead Amy to become proficient in telling stories and in revising her own work.

Case Study Three:

Ryan has a diagnosis of PDD-NOS that affects his language, social, and literacy skills. He also struggles with anxiety. He has a number of interests including: pirates and treasure, cooking, watching his favorite TV shows, and drama. Ryan has a strong memory and conveys a great deal of social knowledge when he is acting or drawing.

Due to Ryan’s anxiety associated with reading and writing, he often protested and completely shut down when presented with something to read or write. Ryan watched a number of shows that taught lessons about friendship or had a “moral to the story.” He was able to take some of those themes and stories and modify them, inserting kids from his school as the characters, and adding himself as a character and narrator. Given his interest in drawing, he illustrated his story, and made it into a short book.

The clinician wanted to incorporate his interest in writing and illustrating stories to improve his social skills. The therapist suggested that Ryan make his story into a play, and that he could be the director. Through a series of role-plays, Ryan was able to overcome his social anxiety and invite a peer to act in his play. Numerous social skills were targeted: greetings, turn-taking, active listening, problem solving, and flexibility for handling unforeseen circumstances. Ryan has now directed four plays, and has written countless others. To date, five of his peers have come and acted in his plays. (It has become a “cool” thing to do in Ryan’s social circle). He has gained a great deal of confidence in relating to his peers and in his strength of writing and directing plays.

In addition to social skills, Ryan has struggled with reading and following directions, asking for clarification, and comprehending and using abstract vocabulary. These areas were addressed using his interests in cooking and treasure hunts. Ryan participated in a number of baking projects that required him to locate the directions on the package, sequence and follow each step in a sequence, and determine the meaning of new vocabulary. Since this was in a context that he enjoyed, his attention was high and his anxiety was non-existent. Furthermore, Ryan had the opportunity to learn a new recipe and build on his strength for baking. Since his learning was in context, he was able to remember the meanings of abstract vocabulary. Ryan’s social skills were targeted when he went to the various offices in the building and offered his baked treats. He inevitably received positive social feedback.

Another motivating context for boosting Ryan’s reading for directions and vocabulary skills was participating in scavenger hunts around the building. He enjoyed the challenge of complex directions because there was an element of surprise and adventure. There was a notable consequence if he incorrectly followed the directions. This created the opportunity for Ryan to ask for directions or seek clarification. Since his learning was in context (i.e., he was looking at a fire extinguisher when he was reading the word for the first time), it was memorable. Many conjunctions (but, therefore, so, if) and sequence words (when, at the same time, before, after, next) were targeted multiple times, which led to mastery. This multi-sensory activity was enjoyable for both Ryan and the clinician. For Ryan, it resulted in greater participation, gains, and retention than traditional teaching approaches.

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Dyslexia: A Student Case Study

When a child is diagnosed with dyslexia, parents often want to know what the road to reading and spelling success will look like.  While this road varies from child to child, there are certain landmarks that characterize the journey.  These include initial success with word attack which leads to improvement in automatic word recognition and eventually improvement in spelling.  The following case study describes one child and her experience.**

Karen’s mother Anna came to Commonwealth Learning Center when Karen was in the middle of second grade.  Karen’s speech was remarkable for phoneme reversals – She said the word  breakfast  as  bress-ket , the word  animal  as  aminal , and the word  especially  as  peshasly . Karen had not made expected progress in reading during her first grade year and her parents were growing restless with the Response to Intervention Model at the school. They decided to seek a private evaluation, and during the debrief following the evaluation, the neuropsychologist suggested that they seek private tutoring. Anna and I met and talked about Karen’s likes and dislikes and how she felt about possibly starting tutoring soon. This information is just as important as testing as it helps ensure a good teacher match and a smooth start to tutoring. I asked permission to speak with the neuropsychologist given the absence of a written evaluation. (The report was forthcoming but Karen’s parents did not want to wait.) The neuropsychologist described Karen as a lovely and bright child with severely compromised phonological awareness and rapid naming, hallmarks of dyslexia. Not surprisingly, her word attack skills, word identification, and single-word spelling were also below the 16th percentile. Her spelling was not phonetic; in other words, she did not represent each sound of the word with a letter. She wrote  luc  for  lunch ,  bet  for  best , and  sak  for  snack .

Karen began her twice weekly Orton-Gillingham tutorials the following week.  She enjoyed the one-to-one time with her teacher and relished the opportunity to play games that incorporated her interests – word cards with kitten stickers on the back and sentences written with purple marker. She wrote in sand and on shaving cream and in big letters in the air. Her ability to read words and eventually books grew alongside her confidence. After six months, Karen had some benchmark testing. Her phonological awareness was in the 42nd percentile and her word attack skills were now in the 34th percentile, but her word identification and spelling were below the 25th. This is common. Word attack is measured by giving the child phonetically regular words (words that can be “sounded out”); many of them are single-syllable words. This is just what she had been working on in tutoring. Word identification and spelling on most assessments is measured by giving a child a mix of phonetically regular and irregular words.

Karen continued with tutoring, learning syllable types, spelling generalizations, and syllable division strategies.  Karen had another set of benchmark testing a year later, one and a half years into tutoring, at the start of her fourth grade year. At that time, Karen was reading grade level text according to the Qualitative Reading Inventory. She had solidly average word attack and word identification skills (both hovering around the 50th percentile). Karen had made gains in spelling; her mistakes were so much better! She represented each sound she heard in words, but she had a terribly hard time knowing whether to spell  compete  as  compeet ,  compete , or  compeat …They all sounded right! The good news was that since Karen’s spelling mistakes were better, most of her errors were the type that could be corrected through spellcheck software. The other area that lagged behind was Karen’s reading fluency – While her accuracy was fantastic (98% or more of the words read correctly), her rate was below expectations for grade level. It is fairly common for students with dyslexia to read more slowly than their peers, and, for this reason, many access audiobooks when the reading load becomes too heavy to carry without support. While Karen does not yet need this support as a fourth grader, it is likely that she will as she progresses through the grades.

Karen no longer attends tutoring during the school year, but she plans to return during the summers to ensure that she maintains and improves upon the skills that she has worked so hard to obtain.  Oh, and she wants to talk to her tutor about her new favorite book series:  The Chronicles of Narnia !

Submitted by Shadi Tayarani, M.Ed Director of Commonwealth Learning Center, Danvers

** Names have been changed to protect the family’s privacy.

Jaydin Skinner says:

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Case Study: 3 siblings with severe dyslexia

Jan 31, 2014 | DM Case Studies , DM News Blog , Parent Reviews | 0 comments

The Problem

The story of Doresa and her three children’s struggles with learning to read began three years ago, when Javon was in Kindergarten and the twins Jordan and Makaila were in a private preschool.

Doresa and her husband were a little worried about Javon’s reading because he just seemed so inconsistent whenever he tried to decode words with them at home. As such they approached his teacher, who was frankly shocked by their suggestion that he had problems learning to read. Far from behind, she told them how Javon was actually the best reader in the class!

So why the problems with reading books at home? Well, fairly soon Doresa realised that the reason for the apparent contrast between her son’s baseline ability with individual words and his aptitude for devouring entire books at school, was that he was a highly gifted sight reader.  His intelligence was in no doubt, having been tested as being in the 99.8th percentile for intelligence. For that reason, he had discovered that there was no need to read using the phonetic structure; he could simply sight-memorize the text, and did this so effectively that the teachers never caught on.  At home meanwhile, where Javon didn’t have his throng of well-memorized books to hand, he could be seen making mistakes on even the simplest of words. Any given page was peppered with surprising mistakes.

Having made this discovery, Doresa took her Javon to see a psychologist who worked with gifted children. They confirmed not just a reading issue, but one of the most severe cases of dyslexia they had ever encountered. It was exactly because of his exceptionally high IQ that he still appeared to be reading above grade level.

Frankly, Doresa was unsure of what she should do next. It was a very strange position to be in, especially given that the school flat-out refused to give credence to her suggestions that he was unable to decode, even after they had the report from the psychologist.

Finally she made the decision to quit her job and homeschool Javon. That way she could ensure that he received the proper reading foundation he needed to be able to really progress. Doresa’s hope was that this was the start of a new and exciting change in the way Javon understood and processed written language…but unfortunately this could not have been further from the truth.

They had Javon tested again after twelve months and the results showed that despite Doresa’s very best personal efforts, he was reading only five additional words out of context compared with a year ago. A skilled sight reader he may be, but there was now no hiding from the fact that Javon was on a reading plateau. Doresa also decided to have the twins tested. They too were profoundly gifted (99.7th and 98th percentile), and also showing signs of dyslexia.

So, in an effort to pinpoint the issue, all three children had their eyes tested. The testing revealed them as having some tracking and convergence issues and so they subsequently underwent a year of vision therapy. However, while the vision issues improved, the reading did not. Feeling a bit deflated yet determined, Doresa’s quest to help her children continued.

From then on, the list of tried and tested interventions started to get ludicrously extensive! Reading Horizons, StarFall, Bob Books, All About Spelling, and lots of Scholastic worksheets… Javon would do the work without complaint, but aside from photographically memorizing pages at a time and therefore appearing to digest the material well, in her heart Doresa knew that he just wasn’t moving forward.

The Solution

When Doresa first stumbled across Easyread she instantly knew it would be perfect for one of her children in particular, Jordan, who was the least confident academically of all the kids. He was intensely frustrated and confused by all the different reading platforms they were trying to work through. For that reason, the idea of a game based system was something she thought would suit him very well indeed. The fact that the system had a focus on eye-tracking, and that the sessions were so short, were also excellent incentives for signing up her youngest son.

What’s more, while the cost of the program seemed high initially, compared to the $150 an hour Doresa was paying to have a certified reading tutor (who had been in place for quite some time and was yet to yield any positive change) this approach was ultimately much more reasonable. After watching her twin on the program on his first day, Makaila asked if she could start it too. Two days after that, Javon wanted to join his younger siblings, and so before they knew it they were all on board!

Everything about Easyread suited Doresa’s three very different but similarly bright children: the gentle approach,  the fun yet challenging games, David’s gentle words coaxing the children to stop for the day and try again tomorrow when they get stuck in was especially for Jordan, the least confident of the children. The fact that not all of the words are “easy” in the lessons was hugely beneficial too. Javon, Jordan and Makaila could easily fake their way through words like “hat, sat, bat, mat”, which had tended to be the focus of other programs they had tried. However they couldn’t do that when being confronted by words that were new to them.  The continued assessments allowed them to all clearly track their progress too, which was very useful. Doresa by this point had spent so much time and money on programs that appeared to be working because the kids were chomping through the material, but without some kind of formal assessment it was close to impossible to know for sure. Where Easyread was concerned, she was able to clearly map her children’s decoding ability as it flourished. And flourish it certainly did…

The Results

Mikaila, Jordan and Javon’s desire to read and confidence has experienced a drastic leap. Indeed Javon, the eldest, will now read to his younger siblings. Mikaila meanwhile has discovered that she loves reading books and writing stories. The biggest change for Javon is that he now not only enjoys decoding words, but has also started to write as well! This is a child that would never, ever write without being prodded to in the past. And yet now he will just pick up a pen and piece of paper and go for it.

Happily the three children have consulted one another and decided on what they would like their reward to be for finishing Easyread…space camp! They fully understand that this is a high pressure reading environment where every child is assigned a “job”, and that reading will be a part of every job. They will have to read publicly so that all team members can complete various phases of the mission.  Aside from all being great space enthusiasts, it seemed to be a great fit with the “Agent” theme of Easyread and as such Doresa gladly agreed to this prize.

And guess what – even with the cost of Easyread and Space Camp – it is still cheaper than what the family would have spent hiring a private tutor to work with each child weekly! They are all reading and writing so much better than before they started, and the twins are working at decoding even the most challenging of words. They no longer shy away from reading challenges, but embrace them as they know every success they have in reading gets them closer to the ultimate Space Camp goal.

These three children defy the stereotype that dyslexia is a learning disability, since it is their very intelligence that led them to sight-read words in the first place. So with that in mind, where to next? Well 8 year old Javon is currently reading chapter books that are written for young teens, and meanwhile the twins are growing in confidence and ability every day that they are on the Easyread course. For these three little astronauts, the sky really is the limit!

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The Case for Early Dyslexia Screening

• Posted October 19, 2023
• By Jill Anderson
• Cognitive Development
• Early Education
• Families and Community
• Language and Literacy Development
• Learning Differences and Accessibility

Associate Professor Nadine Gaab wants to see the whole system surrounding children and reading development change — starting in utero. Earlier intervention can be the ultimate game-changer when it comes to identifying children with dyslexia or other learning differences. 

“When it comes to learning differences such as dyslexia, we are largely focused on a reactive deficit-driven wait to fail model instead of the development of preventative approaches,” she says. Gaab is a neuroscientist who researches the development of typical and atypical language and literacy skills in the pediatric brain, and pre-markers of learning disabilities. 

"What we could show is that some of these brain alterations are already there in infancy, and toddlerhood, and preschool. So what we can conclude from this is that some children step into their first day of kindergarten with a less optimal brain for learning to read. So you want to find them right then, right? And that has tremendous implications for policy," she says. "You don't want to wait and let them fail if you already can determine who will struggle most likely and who will not."

While there have been some efforts to mandate universal dyslexia screening, it is only one small part of what needs to be done to take steps toward addressing the needs of children, something that Gaab envisions an entire community — beyond just the school walls — being a part of, from pediatricians to bus drivers to librarians.

In this episode of the EdCast, Gaab discusses what we know and don’t know about dyslexia and literacy development, and why the need for intervention — as early as preschool — could be the most impactful thing that happens. 

JILL ANDERSON: I'm Jill Anderson. This is the Harvard EdCast. 

Nadine Gaab knows early dyslexia screening and intervention could change outcomes for millions of children. She's a Harvard neuroscientist whose research focuses on language-based learning disabilities and typical and atypical reading development. An estimated 5% to 10% of Americans have dyslexia, but screening and diagnosis can be complicated and take time.

Many states have begun mandating universal dyslexia screening at an early grade, but it's only a small piece of what has to be done to move toward what she calls a prevention model. I wanted to know the potential impact of early screening and what's keeping it from happening. First, I asked Nadine to tell me what is dyslexia.

NADINE GAAB: This is actually a very complicated question. And I think if you would ask 50 people to define, it you would maybe get 50 different responses. So the field itself is not really sure how to define it. The way we define it is it's a specific learning disability with a neurobiological origin. So there is a brain correlate to it. And it's usually characterized by difficulties with accurate and/or fluent word reading.

And we want to put the emphasis here on word reading because it's the mechanics of learning to read that usually is the core symptom of developmental dyslexia. So we have poor decoding abilities. You could have poor spelling abilities. And so it's that deciphering of single words, the decoding of single words, that's the core symptom of dyslexia.

Subsequently, it can lead to problems with reading fluently and comprehending what you read. But I think it's really important that we define it at the word level in these children.

JILL ANDERSON: Is that partly why it is so hard for children to be identified as dyslexic?

NADINE GAAB: I think the reason why it's so hard is more a systems level problem than it is the definition of what it is.

JILL ANDERSON: Right.

NADINE GAAB: Often, we get hung up on the definitions. Or does the child have dyslexia? Or is it another reading disability? Maybe this child is only struggling because of environmental reasons or they were late talkers. There's all these different factors.

What's really important is that we find kids who struggle with reading early and help them early regardless of what the underlying cause is. And whatever diagnosis we give the child should be the one that helps them most within the systems they're living in.

JILL ANDERSON: We're going to talk a bit more about early screening and early intervention, but is there some sort of paradox between what we know in brain science about dyslexia and intervention versus what's actually happening on the ground in schools?

NADINE GAAB: Yes. The biggest problem right now is that, when it comes to learning differences such as dyslexia, we are largely focused on a reactive deficit-driven wait to fail model instead of the development of preventative approaches. So let me explain it a little bit more.

So as a society, we embrace preventative medicine, right? So we love vaccines and checkups. And we get mammograms. We all do this so that we don't get sick.

NADINE GAAB: So we are trying to do this preventative angle. We don't do that as much in education, where we more or less have the kids start school. Then we kind of observe who is struggling. And we observe a little bit more and observe a little bit more. And there may a bit of response to intervention, et cetera, et cetera. But overall, it's more a wait to fail model than it is a preventative model.

What we knew since probably the last 25 years is that the brains of children who are struggling with reading, including dyslexia, show some alterations. So their brain development is different from children who don't struggle with learning to read. What we didn't know for the longest time is whether this is a result of struggling on a daily basis since kindergarten.

So is it that they all kind of start the first day of kindergarten with a clean slate when it comes to brain development, and then the brain changes because they're struggling on a daily basis? Or do these brain alterations predate the onset of formal reading instruction? And what we could show is that some of these brain alterations are already there in infancy, and toddlerhood, and preschool.

So what we can conclude from this is that some children step into their first day of kindergarten with a less optimal brain for learning to read. So you want to find them right then, right? And that has tremendous implications for policy. You don't want to wait and let them fail if you already can determine who will struggle most likely and who will not.

JILL ANDERSON: When you hear that, the obvious question becomes, why has it been so hard to implement some form of earlier screening if we already know that many children can get early intervention and change the outcomes?

NADINE GAAB: Yeah. It's really a problem on the systems level, right? I want to just emphasize that we can't use brain imaging on the individual basis to determine this child will develop dyslexia and this child will not. And I don't think that's where we want to go, but we behaviorally know. In 3 and 4-year-olds, we can reliably identify based on a series of prereading tasks and milestones who will most likely struggle with learning to read.

But the educational system is not really a preventative system as we have in medicine. And I think it just takes a shift in mindset in order to move from that wait to fail model to a more preventative lens. And we need to make sure that we look into prekindergarten and other preschool educational settings. We need to train teachers in order to understand these milestones and being able to recognize, being able to intervene early.

So there's a lot of different levels related to policy, related to how we currently operate in terms of literacy development, and how early literacy development starts. So most people still think it starts maybe late preschool or midpreschool. But in my lab, we know that literacy development starts in utero because the fundamental milestones for learning to read are sound and language processing. And they start as early as in utero.

JILL ANDERSON: Wow. I mean, hearing all of this makes me think this is a huge issue. And there's so many layers to it. So we're seeing movement on one end in that many more states have begun to mandate universal dyslexia screening in children between kindergarten and second grade.

NADINE GAAB: Yes.

JILL ANDERSON: And that's a big step forward, but it sounds like that's still not necessarily going to be enough to respond and help these kids.

NADINE GAAB: Yeah. So there's many, many more layers to this. It's definitely a really good first step that we now have, that early screening legislation in most states in the United States. But often, educators don't know what kind of screeners to use. They don't know how to interpret the screening results. 

So there's the lack of data literacy in many educational settings is a problem. But also, often pre-K, kindergarten, first grade teachers are not trained to intervene and remediate some of these early signs that these screeners pick up. And so that's where higher education needs to come in where we need to have teachers learn these kinds of things in their teacher training.

Another set of issues is related to having assessments that are working well for multilingual learners, for dialect speakers, that are really culturally responsive and inclusive. And so there's many different layers to follow now. We can't just lean back and say, now we have legislation and everything. We'll automatically move to a more preventative model in education.

JILL ANDERSON: If you could snap your fingers and just change everything instantly, what do you think it would look like to have that preventative model in place? It would start in utero it sounds like.

NADINE GAAB: Yeah. So I think it's really important that we shift our mindset and say that reading development starts in utero, right? So if we think about this, then after the child is born the next steps of successful reading development are all within the framework of oral language, right?

So the child learns to distinguish which sounds belong to her or his or their native language versus other languages. We learn the meaning of the words and the rules of our language. And then years later, we learn to map the sounds of our language or languages onto the graphemes and start putting those together and start decoding. The ultimate goal is to read paragraphs and comprehending what you read. So if you see reading development as a very complex skill that starts in utero and develops all the way into late adolescence or adulthood and that we recognize that it needs a lot of explicit instruction and practice, I think that will bring us a long way. Because what that means is that, if we place reading development as starting in utero, then the first four, four and a half years of reading development are actually oral language development.

So if we think about early identification, if you think about how can we prepare children for successful reading acquisition, we want to focus as much on the oral language component, listening, comprehension, vocabulary, as much as on the mechanics of learning to read. So learning the letters, the sounds of the letters, and decoding, morphology, et cetera.

JILL ANDERSON: And we know-- one of the things I know from hosting the EdCast is just how we teach reading is such a hotly debated subject in America. I mean, it comes up regularly in episodes. Anything that has to do with literacy, it seems to come up. Because that's just something that not everyone seems to agree on. And not that I want to take us down a path of talking about the reading wars today.

NADINE GAAB: Thank you.

JILL ANDERSON: But it seems like that kind of plays into this.

NADINE GAAB: Yeah. I'm a little bit more optimistic. I think we have the science of reading. And I think we need to understand what the science of reading is and what it's not, right? So what it's not is it's not some ideology or philosophy. And it's not a political agenda, or a certain program of instruction, or a single specific component of instruction such as phonics. What it really is is the interdisciplinary body of all the scientifically-based research that many, many people around the globe have done over 50 years. And now, what we do is we are taking that body of knowledge and applying it into the school systems. And so the translational component is difficult as we know from many other disciplines.

And so I think we should see similar to climate, right? So we didn't know as much about climate changes. But now, we do. And there is a vast interdisciplinary body. And now, we have to implement it into policy changes and systems level changes. And we have to do that in reading as well.

So I see it very optimistic. We just need to start fighting and working together and bringing the different strands together, which includes the oral language piece and background knowledge as well as the mechanics of reading. So I think if we just manage to put it all together, it will be a really good addition to the field and will eventually, hopefully, move these reading scores up.

JILL ANDERSON: When I think about that data of approximately 65% of all fourth graders are not reading at grade level, I try to imagine what would that look like if we had some early intervention in place to actually identify challenges kids may have or learning disabilities that kids may have.

NADINE GAAB: Yeah. I mean, 65% of fourth graders are not reading at grade level. But I think it's important to mention that not 65% of these fourth graders have dyslexia, right? There's many—

NADINE GAAB: --different factors that contribute to atypical reading development. So you have genetics. You have brain development. You have perception and cognition. And you have the environment, right? 

Many years ago, I think the field thought, if you just find this one cause of dyslexia, we can just work on it. And then all children will miraculously read well. But I think the field has long moved away from this and now sees the multifactorial aspects and how these factors interact with each other.

I think what we need to do is really work on all of these components, so environmental factors such as neighborhood factors and stress related to maybe chronic illness of the parent, immigration status, socioeconomic status, trauma, et cetera, et cetera. We've had many good policies put in place. So we had the National Reading Panel, and No Child Left Behind, and the Individuals with Disabilities Education Act, and Decoding Dyslexia, and other aspects.

But that really has not moved the needle much if you look at the National Center for Education Statistics. So it's not that these 65% are suddenly showing up and we were reading fine 20 years ago or before COVID, right? No. It's been low all the way back to the '90s.

And so I think we need to really think, based on the science we now know, what are some of the things we are missing. And so I think moving to a preventative model and seeing reading development similar to math, or executive functioning, or social emotional learning happening much, much earlier than we currently think they are. And so with that lens into infancy and early childhood and thinking about early identification and who is teaching these children early and how much are they appreciated in the educational system and their training and compensation will really move the needle in my opinion. It's not going to be the only thing, but I think it will be a really important piece.

JILL ANDERSON: In the meantime, we have parents, caregivers. They often seem to be the folks who are really working on this at least within their schools. They're trying to get services that they need for their kids. And it seems like in a lot of ways, even with a lot of the mandated screening, the parents and advocates are the people who are pushing this forward in a lot of ways. What is your advice to them as they wait for either more legislation or wait for some more change to take place?

NADINE GAAB: I think parents have done or caregivers have done an incredible job in the last 10 plus years when it comes to reading disabilities, including dyslexia. My advice would be to know your rights, work with your educators, take an active role in your child's reading development, know what the milestones are, know how important home literacy is, but also work in a community setting. So we do a lot of work with pediatricians, and social workers, and libraries, and other stakeholders in the process.

And we think that, in order to optimize how we care for children who struggle with reading or have a learning disability is to really improve that working together on the systems level. That includes the general educator talking to the pediatrician, or it includes the advocacy of people in the community like after school teachers, and bus drivers, and maybe officials in churches and libraries to know more about learning disabilities and optimize care for children who are struggling and do this as early as possible.

And I think we often say, well, the educators don't give us access to screening, or they don't know. I think we have to give a lot of credit to educators who really want children to do well, but often they haven't been trained on prevention and early identification. That's not part of teacher training preservice. 

It's not often a big topic in higher education. And also, the quality of evidence-based professional development and how it's delivered is often suboptimal. So I feel like the whole system needs to change for us to embrace more of a preventative model.

And then I think what's also really important-- and I want to make sure we mention this-- is that we still don't know whether there are language-specific risk factors. We know now that we can screen multilinguals and dialect speakers and that they need to be included in the screening process, but that development of equitable screening tools and assessments and eliminating biases and reading curricula and screening early identification even in the intervention process or the support systems and awareness is really, really important.

So it really takes a whole village. And I know every child has the right to learn to read well. So we all have to work together in order to maximize the joy of learning to read.

JILL ANDERSON: Nadine, well, thank you so much. This was really eye-opening and informative.

NADINE GAAB: Thank you so much for having me today.

JILL ANDERSON: Nadine Gaab is an associate professor at the Harvard Graduate School of Education. She leads the Gaab Lab, which focuses on typical and atypical learning trajectories from infancy to adulthood with a special focus on language and reading development.

I'm Jill Anderson. This is the Harvard EdCast produced by the Harvard Graduate School of Education. Thanks for listening. 

Correction: The audio version of this podcast references an inaccurate statistic. The correct percentage of Americans with dyslexia is 5% to 10%.

An education podcast that keeps the focus simple: what makes a difference for learners, educators, parents, and communities

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Article Contents

Introduction, acknowledgements.

• < Previous

Theories of developmental dyslexia: insights from a multiple case study of dyslexic adults

• Article contents
• Figures & tables
• Supplementary Data

Franck Ramus, Stuart Rosen, Steven C. Dakin, Brian L. Day, Juan M. Castellote, Sarah White, Uta Frith, Theories of developmental dyslexia: insights from a multiple case study of dyslexic adults, Brain , Volume 126, Issue 4, April 2003, Pages 841–865, https://doi.org/10.1093/brain/awg076

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A multiple case study was conducted in order to assess three leading theories of developmental dyslexia: (i) the phonological theory, (ii) the magnocellular (auditory and visual) theory and (iii) the cerebellar theory. Sixteen dyslexic and 16 control university students were administered a full battery of psychometric, phonological, auditory, visual and cerebellar tests. Individual data reveal that all 16 dyslexics suffer from a phonological deficit, 10 from an auditory deficit, four from a motor deficit and two from a visual magnocellular deficit. Results suggest that a phonological deficit can appear in the absence of any other sensory or motor disorder, and is sufficient to cause a literacy impairment, as demonstrated by five of the dyslexics. Auditory disorders, when present, aggravate the phonological deficit, hence the literacy impairment. However, auditory deficits cannot be characterized simply as rapid auditory processing problems, as would be predicted by the magnocellular theory. Nor are they restricted to speech. Contrary to the cerebellar theory, we find little support for the notion that motor impairments, when found, have a cerebellar origin or reflect an automaticity deficit. Overall, the present data support the phonological theory of dyslexia, while acknowledging the presence of additional sensory and motor disorders in certain individuals.

Developmental dyslexia is traditionally defined as a discrepancy between reading ability and intelligence in children receiving adequate reading tuition. Since the definition is entirely behavioural, it leaves open the causes for reading failure. It is now well established that dyslexia is a neurological disorder with a genetic origin, which is currently being investigated. The disorder has lifelong persistence, reading retardation being merely one of its manifestations. Beyond this consensus, and despite decades of intensive research, the underlying biological and cognitive causes of the reading retardation are still hotly debated. Indeed, there are no less than three major theories of dyslexia. The goal of the present study is to produce evidence to decide between these theories.

The major theories of developmental dyslexia

We begin by providing a neutral overview of the different theories of dyslexia, as described by their proponents. Note that there are different versions of each theory in the literature, which we are not able to represent in detail. Instead, we have chosen to describe the currently most prominent version of each theory.

The phonological theory

The phonological theory postulates that dyslexics have a specific impairment in the representation, storage and/or retrieval of speech sounds. It explains dyslexics’ reading impairment by appealing to the fact that learning to read an alphabetic system requires learning the grapheme–phoneme correspondence, i.e. the correspondence between letters and constituent sounds of speech. If these sounds are poorly represented, stored or retrieved, the learning of grapheme–phoneme correspondences, the foundation of reading for alphabetic systems, will be affected accordingly ( Bradley and Bryant, 1978 ; Vellutino, 1979 ; Snowling, 1981 ; Brady and Shankweiler, 1991 ). While theorists have different views about the nature of the phonological problems, they agree on the central and causal role of phonology in dyslexia. The phonological theory therefore postulates a straightforward link between a cognitive deficit and the behavioural problem to be explained. At the neurological level, it is usually assumed that the origin of the disorder is a congenital dysfunction of left‐hemisphere perisylvian brain areas underlying phonological representations, or connecting between phonological and orthographic representations.

Support for the phonological theory comes from evidence that dyslexic individuals perform particularly poorly on tasks requiring phonological awareness, i.e. conscious segmentation and manipulation of speech sounds. However, evidence for poor verbal short‐term memory and slow automatic naming in dyslexics also points to a more basic phonological deficit, perhaps having to do with the quality of phonological representations, or their access and retrieval ( Snowling, 2000 ). Anatomical work ( Galaburda et al ., 1985 ; Geschwind and Galaburda, 1985 ) and functional brain imaging studies support the notion of a left perisylvian dysfunction as a basis for the phonological deficit ( Paulesu et al ., 1996, 2001 ; Shaywitz et al ., 1998 ; Brunswick et al ., 1999 ; McCrory et al ., 2000 ; Pugh et al ., 2000 ; Temple et al ., 2001 ; Shaywitz et al ., 2002 ).

In order to better differentiate the phonological theory from the others, we discuss here only the strong version of the theory: that the cognitive deficit is specific to phonology. Indeed, challengers of the phonological theory do not dispute the existence of a phonological deficit and its contribution to reading retardation; rather, they uphold that the disorder is much more extended, having its roots in general sensory, motor or learning processes, and that the phonological deficit is just one aspect or consequence of the more general disorder.

The rapid auditory processing theory

The most obvious way to challenge the specificity of the phonological deficit is to postulate that it is secondary to a more basic auditory deficit. This is the claim of the rapid auditory processing theory, which specifies that the deficit lies in the perception of short or rapidly varying sounds ( Tallal, 1980 ; Tallal et al ., 1993 ). Support for this theory arises from evidence that dyslexics show poor performance on a number of auditory tasks, including frequency discrimination ( McAnally and Stein, 1996 ; Ahissar et al ., 2000 ) and temporal order judgement ( Tallal, 1980 ; Nagarajan et al ., 1999 ) (see reviews by Farmer and Klein, 1995 ; McArthur and Bishop, 2001 ). Abnormal neurophysiological responses to various auditory stimuli have also been demonstrated ( McAnally and Stein, 1996 ; Nagarajan et al ., 1999 ; Kujala et al ., 2000 ; Temple et al ., 2000 ; Ruff et al ., 2002 ). The failure to correctly represent short sounds and fast transitions would cause further difficulties in particular when such acoustic events are the cues to phonemic contrasts, as in /ba/ versus /da/. There is indeed also evidence that dyslexics may have poorer categorical perception of certain contrasts ( Mody et al ., 1997 ; Adlard and Hazan, 1998 ; Serniclaes et al ., 2001 ). In this view, the auditory deficit is therefore the direct cause, in the course of development, of the phonological deficit, and hence of the difficulty in learning to read. The original version of the auditory theory made no particular claim at the biological level, but we will see below that this is now specified within the magnocellular theory.

The visual theory

The visual theory ( Lovegrove et al ., 1980 ; Livingstone et al ., 1991 ; Stein and Walsh, 1997 ) reflects another long‐standing tradition in the study of dyslexia, that of considering it as a visual impairment giving rise to difficulties with the processing of letters and words on a page of text. This may take the form of unstable binocular fixations, poor vergence ( Cornelissen et al ., 1993 ; Stein and Fowler, 1993 ; Eden et al ., 1994 ), or increased visual crowding ( Spinelli et al ., 2002 ). The visual theory does not exclude a phonological deficit, but emphasizes a visual contribution to reading problems, at least in some dyslexic individuals. At the biological level, the proposed aetiology of the visual dysfunction is based on the division of the visual system into two distinct pathways that have different roles and properties: the magnocellular and parvocellular pathways. The theory postulates that the magnocellular pathway is selectively disrupted in certain dyslexic individuals, leading to deficiencies in visual processing, and, via the posterior parietal cortex, to abnormal binocular control and visuospatial attention ( Stein and Walsh, 1997; Hari et al ., 2001 ). Evidence for magnocellular dysfunction comes from anatomical studies showing abnormalities of the magnocellular layers of the lateral geniculate nucleus ( Livingstone et al ., 1991 ), psychophysical studies showing decreased sensitivity in the magnocellular range, i.e. low spatial frequencies and high temporal frequencies, in dyslexics ( Lovegrove et al ., 1980; Cornelissen et al ., 1995 ), and brain imaging studies ( Eden et al ., 1996 ).

The cerebellar theory

Yet another view is represented by the automaticity/cerebellar theory of dyslexia ( Nicolson and Fawcett, 1990 ; Nicolson et al ., 2001 ) (henceforth referred to as the cerebellar theory). Here the biological claim is that the dyslexic’s cerebellum is mildly dysfunctional and that a number of cognitive difficulties ensue. First, the cerebellum plays a role in motor control and therefore in speech articulation. It is postulated that retarded or dysfunctional articulation would lead to deficient phonological representations. Secondly, the cerebellum plays a role in the automatization of overlearned tasks, such as driving, typing and reading. A weak capacity to automatize would affect, among other things, the learning of grapheme–phoneme correspondences. Support for the cerebellar theory comes from evidence of poor performance of dyslexics in a large number of motor tasks ( Fawcett et al ., 1996 ), in dual tasks demonstrating impaired automatization of balance ( Nicolson and Fawcett, 1990 ), and in time estimation, a non‐motor cerebellar task ( Nicolson et al ., 1995 ). Brain imaging studies have also shown anatomical, metabolic and activation differences in the cerebellum of dyslexics ( Rae et al ., 1998 ; Nicolson et al ., 1999 ; Brown et al ., 2001 ; Leonard et al ., 2001 ).

The magnocellular theory

Finally, there is a unifying theory that attempts to integrate all the findings mentioned above. A generalization of the visual theory, the magnocellular theory ( Stein and Walsh, 1997 ) postulates that the magnocellular dysfunction is not restricted to the visual pathways but is generalized to all modalities (visual and auditory as well as tactile). Furthermore, as the cerebellum receives massive input from various magnocellular systems in the brain, it is also predicted to be affected by the general magnocellular defect ( Stein et al ., 2001 ). Through a single biological cause, this theory therefore manages to account for all known manifestations of dyslexia: visual, auditory, tactile, motor and, consequently, phonological (for an attentional variant see Hari and Renvall, 2001 ). Beyond the evidence pertaining to each of the theories described previously, evidence specifically relevant to the magnocellular theory includes magnocellular abnormalities in the medial as well as the lateral geniculate nucleus of dyslexics’ brains ( Livingstone et al ., 1991 ; Galaburda et al ., 1994 ), poor performance of dyslexics in the tactile domain ( Grant et al ., 1999 ; Stoodley et al ., 2000 ), and the co‐occurrence of visual and auditory problems in certain dyslexics ( Witton et al ., 1998 ; Cestnick, 2001 ; van Ingelghem et al ., 2001 ).

Although the auditory and visual theories have been presented here separately for historical and logical reasons, their supporters now agree that visual and auditory disorders in dyslexia are part of a more general magnocellular dysfunction. We will therefore not discuss the visual and auditory theories independently. Rather, we will restrict the discussion to a comparison between the phonological, cerebellar and magnocellular theories.

A critical look

The major weakness of the phonological theory is its inability to explain the occurrence of sensory and motor disorders in dyslexic individuals. Supporters of the phonological theory typically dismiss these disorders as not part of the core features of dyslexia. They consider their co‐occurrence with the phonological deficit as potential markers of dyslexia, but do not see them as playing a causal role in the aetiology of reading impairment (e.g. Snowling, 2000 ).

The cerebellar theory also fails to account for sensory disorders, but its proponents entertain the idea of distinct cerebellar and magnocellular dyslexia subtypes ( Fawcett and Nicolson, 2001 ). Another problem for the cerebellar theory is that the causal link postulated between articulation and phonology relies on an outdated view of the motor theory of speech, according to which the development of phonological representations relies on speech articulation. This view has long been abandoned in the light of cases of normal phonological development despite severe dysarthria or apraxia of speech (for a discussion see Liberman and Mattingly, 1985 ; Ramus et al ., 2003 ). Finally, it remains uncertain what proportion of dyslexics are affected by motor problems. A number of studies have failed to find any ( Wimmer et al ., 1998; van Daal and van der Leij, 1999 ; Kronbichler et al ., 2002 ), others have found motor problems only in a subgroup of dyslexics ( Yap and van der Leij, 1994 ; Ramus et al ., 2003 ), and it has been suggested that motor dysfunction is found only in dyslexic children who also have attention‐deficit hyperactivity disorder (ADHD) ( Denckla et al ., 1985 ; Wimmer et al ., 1999 ).

The magnocellular theory, unique in its ability to account for all manifestations of dyslexia, is undoubtedly attractive. Nevertheless, it also has its problems and has been facing growing criticism in recent years (e.g. Ramus, 2001 ). One line of criticism emphasizes a number of failures to replicate findings of auditory disorders in dyslexia ( Heath et al ., 1999 ; Hill et al ., 1999 ; McArthur and Hogben, 2001 ). Other studies do find auditory deficits in dyslexics, but only in a subgroup, ranging from a few isolated individuals to 50% of the population studied ( Tallal, 1980 ; Reed, 1989 ; Manis et al ., 1997 ; Mody et al ., 1997 ; Adlard and Hazan, 1998 ; Lorenzi et al ., 2000 ; Marshall et al ., 2001 ; Rosen and Manganari, 2001 ). Another line of criticism focuses on results inconsistent with the idea that the auditory deficit lies in ‘rapid’ auditory processing, and therefore with magnocellular function: indeed, with some tasks ‘rapid’ auditory processing is found to be intact, while with others ‘slow’ auditory processing is found to be impaired ( Reed, 1989 ; McAnally and Stein, 1996 ; Adlard and Hazan, 1998 ; Schulte‐Körne et al ., 1998 b ; Witton et al ., 1998 ; Nittrouer, 1999 ; Lorenzi et al ., 2000 ; Rosen and Manganari, 2001 ; Share et al ., 2002 ). It is also argued that auditory deficits do not predict phonological deficits ( Mody et al ., 1997 ; Schulte‐Körne et al ., 1998 a ; Bishop et al ., 1999 ; Marshall et al ., 2001 ; Rosen and Manganari, 2001 ; Share et al ., 2002 ). Criticism of the visual side of the magnocellular theory also focuses on failures to replicate findings of a visual deficit ( Victor et al ., 1993; Johannes et al ., 1996 ), or on findings of such a deficit only in a subgroup ( Cornelissen et al ., 1995 ; Witton et al ., 1998 ; Amitay et al ., 2002 ), and on inconsistencies between predictions and empirical results. Most notably, visual impairments, when found, seem to be observed across a whole range of stimuli, not just those specifically tapping the magnocellular system ( Skottun, 2000 ; Amitay et al ., 2002 ; Farrag et al ., 2002 ). There is also negative evidence regarding cross‐modal sensory deficits ( Heim et al ., 2001 ). More generally, the idea that the magno‐/parvocellular distinction can be extended to non‐visual sensory systems remains controversial (personal communication, B. Skottun, 2002).

In summary, the phonological theory suffers from its inability to explain the sensory and motor disorders that occur in a significant proportion of dyslexics, while the magnocellular theory suffers mainly from its inability to explain the absence of sensory and motor disorders in a significant proportion of dyslexics. The cerebellar theory presents both types of problems.

Of course, it is possible that the three theories are true of different individuals. For instance, there could be three partially overlapping subtypes of dyslexia, each being an independent contribution to reading difficulties: phonological, auditory/visual, and cerebellar. Alternatively, it could also be that just one theory accounts for every case of dyslexia, and that the other manifestations observed are markers, i.e. they are associated without causation. In order to tease apart the many possible alternatives, we need to be able to answer such questions as: What proportion of dyslexics have a given deficit? Are there dissociations between certain deficits? Are there systematic associations between certain deficits? Unfortunately, the current literature does not contain answers to any of these questions. Indeed, virtually all studies have focused on just one or a few tasks within one modality, and most of them have only analysed group differences, making it impossible to assess what proportion of dyslexics are really affected by a deficit.

Three notable exceptions are worth mentioning. Witton et al . (1998 ) have shown significant differences between dyslexic and controls in frequency modulation (FM) detection at 2 Hz and coherent motion detection. The individual data reported suggest that four dyslexics out of 17 had abnormal performance in the visual task, nine out of 17 in the auditory task, and 15 out of 17 in non‐word reading. The absence of phonological and cerebellar tasks prevents the assessment of what might explain the reading impairment of the seven dyslexics who have normal visual and auditory performance, and to analyse the relationships between all the variables and their predictive power with respect to reading.

Van Ingelghem and colleagues tested both visual and auditory gap detection in dyslexic children and found significant group effects for both ( Van Ingelghem et al ., 2001 ). They report that nine dyslexics out of 10 were impaired in the auditory task and seven out of 10 in the visual task. However, their criterion for being impaired was that the individual’s threshold be above the 95% confidence interval for the control group, that is, for 10 individuals, >0.67 SD above the control mean. This makes it an extremely liberal criterion, since if the control group is normally distributed, ∼25% of the controls should also meet it (individual control data not available). Again, cerebellar and phonological performance was not tested. This study is also potentially undermined by the fact that the two groups were not matched in non‐verbal intelligence quotient (IQ), a factor that is known to affect performance significantly in psychophysical tasks ( Ahissar et al ., 2000 ).

It seems that only one study to date has assessed all the relevant modalities in a group of dyslexics ( Kronbichler et al ., 2002 ). The authors administered a battery of phonological tests and tests of auditory illusory movement perception, visual coherent motion detection, and peg moving. They report significant differences between the two groups in the phonological tests, but none in the auditory, visual or motor tasks. Unfortunately, no individual data are reported to allow assessment of whether some dyslexics could have sensory or motor disorders, and the relationships between the variables are not analysed. In all three studies, only one task for each modality was administered, leaving open the possibility that other, more sensitive tasks, might change the picture significantly.

The present study

Our aim was to produce data that would enable us to start answering questions concerning associations, dissociations and, eventually, causal relationships between sensory, motor, phonological and reading disorders. Our approach was that of a multiple case study: by having the most comprehensive neuropsychological profile for each individual, we sought to identify who had which combination of disorders and, crucially, who did not have a given disorder. We therefore created a battery of psychometric, phonological, auditory, visual and cerebellar tests to be administered to each subject. Within each domain, we selected several tasks that have, according to the literature, most consistently shown differences between dyslexics and controls.

Because we felt that dissociations between disorders would be the most informative, we selected a special dyslexic population, consisting of university students. Obviously, the few dyslexics who enter university are not representative of the whole population: they may be more intelligent, resourceful and socially privileged, and may have received better help with respect to reading. Most importantly, we hypothesized that they would be least likely to accumulate several types of disorders. For instance, if a phonological and a visual disorder can appear independently, an individual having both disorders should be less likely to succeed academically than an individual with just one of them. By studying a high‐achieving population, we therefore maximized our chances of finding pure cases of the different possible subtypes of dyslexia. For the same reason, we also minimized the chances of studying individuals with another comorbid developmental disorder, such as specific language impairment (SLI), ADHD and developmental coordination disorder, which would be an undesirable confound.

Seventeen dyslexic university students at University College London (UCL) volunteered for this study. They had all received a formal diagnosis of developmental dyslexia by a qualified educational psychologist in secondary school or earlier, and most of them had a documented history of reading difficulties. They were initially contacted via UCL’s Examination Section, where dyslexic students may apply for time concessions. With their agreement, their files were made available to us so that we could exclude at this stage all individuals who also suffered from another neurological or psychiatric disorder, with special attention to SLI, ADHD, developmental coordination disorder and autism. Additional inclusion criteria were checked after a first testing session; these were a full‐scale IQ >100 and reading and spelling standard scores <110 on average. One dyslexic subject was excluded after the first session because his reading and spelling scores averaged 114.5, thereby reducing the sample to 16.

Seventeen control subjects were recruited from the same university. Inclusion criteria were: no known developmental, neurological or psychiatric disorder; full‐scale IQ >100; and reading and spelling scores >100. One subject was excluded after the first session because his reading and spelling scores averaged 98.5 and he showed signs of phonological problems; this reduced the sample to 16.

It was checked a posteriori that the two groups were matched overall in age, sex and full‐scale IQ. All the subjects gave informed consent according to the Declaration of Helsinki and the study was approved by the Joint UCL/UCL Hospitals Committee on the Ethics of Human Research.

A battery of psychometric, phonological, auditory, visual and cerebellar tests amounting to ∼10 h of testing was administered to each individual in several sessions, lasting 1–2 h each.

Psychometric tests

Verbal intelligence and non‐verbal intelligence were assessed using the Wechsler Adult Intelligence Scale (WAIS‐III UK ; Wechsler, 1998 ). Reading and spelling were assessed using the Wide Range Achievement Test (WRAT3; Wilkinson, 1993 ), the National Adult Reading Test (NART; Nelson, 1991 ), concentrating mainly on rare and irregular words, and a reading speed test adapted from the Neale Analysis of Reading Ability (NARA; Neale, 1997 ). Non‐word reading was also assessed, using 20 non‐words from the Graded Nonword Reading Test (GNRT; Snowling et al ., 1996 ). Each non‐word was presented on a computer screen. Overall reading time was recorded as well as accuracy.

Screening for other disorders

To check for possible language impairments, two non‐phonological language tests were administered. These two tests have been shown previously to be sensitive to subtle impairments of syntax in SLI children and adolescents ( van der Lely, 1996 a ; van der Lely and Stollwerk, 1997 ).

Advanced Syntactic Test of Pronominal reference (ASTOP) (van der Lely, 1997). A sentence was played through headphones by a computer and a picture was displayed at the same time. The subject had to press one of two keys to indicate whether the sentence described the picture or not. The 96 items in this test assessed the understanding of pronominal reference and quantifiers in embedded phrases (such as ‘Minnie the Minx says every dancer is pinching herself’).

Test of active and passive sentences (TAPS) (van der Lely, 1996 b ). A sentence was played through headphones by a computer and four pictures were displayed at the same time. The subject had to press one of four keys to indicate which picture was best described by the sentence. The 48 items assessed the correct computation of agent–patient relationships in active and passive sentences (such as ‘The car is hit by the lorry’).

To determine the possible presence of attention deficit disorder, each subject completed the Brown attention deficit disorder questionnaire (Brown, 1996).

Phonological tests

Automatic picture naming.

The subject was asked to name 50 pictures of five objects (hat, ball, table, door, box) as fast as possible. A second measure was taken with a different ordering of the 50 pictures. Total naming time was recorded irrespective of accuracy. This task is taken from the Phonological Assessment Battery (PhAB; Frederickson et al ., 1997 ).

Automatic digit naming

This was the same as the automatic picture naming test, but with two lists of 50 digits.

Spoonerisms

Upon hearing a pair of words (like ‘basket–lemon’) via loudspeakers, the subject had to swap their initial phonemes and pronounce the resulting pair of non‐words (‘lasket–bemon’) in the correct order. The stimuli were 12 pairs of words from McCrory ( McCrory, 2001 ), which were recorded on hard disk and played one at a time from a computer. Both accuracy and time taken to produce each pair (from offset of stimulus) were recorded.

Non‐word repetition

Upon hearing a non‐word through headphones, the subject had to repeat it immediately. The stimuli were 40 non‐words from the Children’s Test of Nonword Repetition ( Gathercole and Baddeley, 1996 ), recorded on hard disk and played by computer.

Tests of auditory perception

All tests were performed in a quiet room using headphones which (except for audiological screening) were calibrated using a B&K 4157 ear simulator (Brüeland Kjaer, Naerum, Denmark). Masked thresholds and the syllable–formant discrimination task were run using special‐purpose psychoacoustic hardware and Sennheiser HD 475 headphones. The other tasks were run on a laptop with Sony MDR‐CD270 headphones.

Audiological screening

All participants were required to pass a pure‐tone screen using a standard clinical audiometer at or better than 25 dB HL (hearing level) at frequencies of 0.5, 1, 2, 4 and 8 kHz, in both ears.

Backward and simultaneous masking

The masking tasks were modelled closely on corresponding ones described by Wright and colleagues ( Wright et al ., 1997 ), with identical stimuli but a different adaptive procedure. Thresholds were measured monaurally in the right ear using a two‐interval, two‐alternative forced‐choice adaptive task tracking 79% correct using Levitt’s (1971 ) procedure with modifications by Baker and Rosen (2001 ) to increase efficiency. On each trial, two 300 ms bursts of a bandpass masking noise [0.6–1.4 kHz at a spectrum level of 40 dB sound pressure level (SPL)] were presented with a 340 ms interstimulus interval (ISI). The 20 ms 1 kHz sinusoidal probe tone occurred along with one of the noise bursts. The listener indicated which of the noise bursts was associated with the probe by pressing one of two buttons on a response box. Feedback was given by lighting the correct button. The probe tone could occur either simultaneously with the masking noise (200 ms after masker onset; simultaneous masking) or with its onset 20 ms prior to the start of the masker (backward masking). All stimuli were gated on and off with 10 ms cosine‐squared envelopes.

The probe tone was set to be clearly audible at the beginning of each test, its level decreasing by 8 dB after each correct response until the first reversal. Hereafter, the standard 3‐down/1‐up rule was implemented, with a decreased step size of 6 dB. Step size decreased by 2 dB after each successive reversal until it was 2 dB, at which point four further reversals were obtained. The final threshold value was estimated as the mean of the final four reversal points.

Absolute thresholds for perception of the probe tone were also acquired in a condition with no masking noise.

A minimum of two tests of threshold and simultaneous masking took place per subject, and four of backward masking (because there is greater within‐subject variability in this condition). All tests of a condition took place consecutively, with reversal of the order from one subject to the next. Absolute thresholds were always tested between backwards and simultaneous masking, and further tests were run if two thresholds for a subject were not within 6 dB. Once this criterion was met, medians of all the tests run in each condition were taken as the final index of performance.

Formant discrimination in syllables and non‐speech analogues

The ability of subjects to discriminate second‐formant transitions in speech and non‐speech sounds was assessed using the software package described by Carrell and colleagues ( Carrell et al ., 1999 ).

A ba – da continuum and the corresponding non‐speech analogues were generated using the Klatt (1980 ) synthesizer in cascade mode with a 1 ms update interval. The 41 stimuli in each continuum differed only in second‐formant (F2) onset frequency, which was varied in equal logarithmic steps.

The ba – da continuum was based on that specified by Mody and colleagues ( Mody et al ., 1997 ) but with only the lower two formants and with a monotone fundamental frequency at 125 Hz. The voicing source was turned off 235 ms into the signal and allowed to decay naturally so as to avoid transients. The total duration of each signal was 250 ms. Steady‐state formant frequencies were 750 and 1200 Hz with bandwidths of 90 Hz for both. The first‐formant (F1) transition was identical for all stimuli, beginning at 200 Hz and reaching 750 Hz after 35 ms. The second formant (F2) began at 825 Hz for ba and at 1500 Hz for da , reaching its steady‐state value of 1200 Hz after 50 ms. Non‐speech isolated‐F2 stimuli were obtained simply by outputting from the synthesizer the waveforms from the F2 resonator on their own (a straightforward option in the Klatt synthesizer). Note that no plosive release bursts were included. Thus the crucial acoustic distinction was carried only by the F2 transition and was similar for the speech and the non‐speech stimuli.

The discrimination task was based on a 4IAX (four‐interval, two‐alternative, forced‐choice same–different) procedure. On each trial, two pairs of stimuli are heard, with a longer interval (900 ms) between the pairs than within (300 ms). One pair of stimuli are identical, being two repetitions of the most extreme ba . In the other, the ba is paired with another stimulus on the continuum. The subject is required to indicate which pair of stimuli is different. At the beginning of the test, the ba is paired with an extreme da , but an adaptive procedure chooses the comparison stimulus so as to estimate the stimulus which is discriminable from the ba 69% of the time. Exactly the same procedure was applied to test discrimination of the non‐speech analogues.

Subjects were not acquainted with the sounds being presented until the trials began, aside from a verbal explanation. Two consecutive measurements of the just noticeable difference (jnd) were acquired for each condition ( ba and non‐speech analogues), with the order alternated between subjects.

Phonemic categorization

Categorization functions were obtained for three speech sound continua using special‐purpose software known as SPA (Speech Pattern Audiometry). Two of the continua varied place of articulation ( ba – da , date–gate ) and one varied voicing ( coat–goat ). The ba – da continuum was the highly schematic one described above.

Both date–gate and coat–goat were based, with minor modifications, on the ‘combined‐cue’ synthetic continua developed by Hazan and Barrett (2000 ) using the Klatt (1980 ) synthesizer. Unlike the ba – da continuum, these were modelled closely on a particular speaker’s tokens (an adult female speaker of standard southern British English). They are much more complex than typical formant‐synthesized speech, and sound much more natural.

The date–gate continuum varied both the spectrum of the initial release burst and the starting frequencies of the second and third formants to signal the change in place of articulation. The coat–goat continuum varied voice onset time in 1 ms steps (the first formant onset frequency covaried with voice onset time, as it does naturally). Both continua consisted of 51 stimuli. Further details of their properties can be found in Hazan and Barrett (2000 ).

On each trial of the test, subjects heard a single stimulus and indicated which they had heard by clicking on one of two pictures on the computer screen (except for ‘BA’ and ‘DA’, which were spelled out in upper case letters, as here). Two independent adaptive tracks, with Levitt’s (1971 ) rules as modified by Baker and Rosen (2001 ), were used to estimate the points on the continuum at which the stimuli were labelled as one word of the pair (e.g. ‘coat’) 29 and 71% of the time. The procedure terminated when there was a total of five reversals on each track, or a maximum of 50 trials. Tracks started at the endpoints of the continuum, and step size decreased from a large step to a smaller one over the first three reversals. In order to assist in the stability of the phoneme categories, continuum endpoints were randomly interspersed throughout the test on 20% of the trials. The categorization function was derived from all trials in a particular test, and summary statistics for slope and category boundary estimated by probit analysis. Shallower slopes indicate less sensitivity to variations in the particular acoustic feature used in the continuum.

Frequency modulation detection at 2 and 240 Hz

Stimuli were modelled closely on those used by Talcott and colleagues ( Talcott et al ., 2000 ). Each trial consisted of two 1 s tone bursts (20 ms rise/fall times) separated by an inter‐stimulus interval of 500 ms. In each pair, one of the tones was a sinusoid of 1 kHz, whereas the other was frequency‐modulated. Two modulation frequencies were used (2 and 240 Hz). For each modulation frequency, a continuum of 100 stimuli was constructed spanning a wide range of values of the modulation index (a maximum modulation index of 4.95 in steps of 0.1 for the 2 Hz modulation frequency and 0.02475 in steps of 0.0005 for 240 Hz). These correspond to maximum frequency deviations of 9.9 and 5.94 Hz, respectively, for the two continua. Stimuli were presented through headphones at ∼75 dB SPL.

The discrimination task was run in the guise of an identification experiment using the SPA software described above, but without continuum endpoints randomly interspersed. Subjects indicated which tone was modulated by clicking on an appropriate graphic. Feedback was provided in the form of appropriate pictures (a happy face for correct responses and a sad face for incorrect ones). Probit analysis was used to fit cumulative Gaussian Functions to the psychometric functions, so as to obtain an estimate of the modulation that was detectable 75% of the time.

Temporal order judgement of long and short sounds

The temporal order judgement task was based on two sounds, readily identifiable without prior training as a car horn (periodic with a fundamental frequency of ∼400 Hz) and an aperiodic dog bark. Starting from sounds accompanying a children’s computer game, various manipulations of amplitude envelope and duration were used to create stimuli with a total duration of 115 ms each, with rise and fall times of 5 ms (‘long’ sounds). The two stimuli were then normalized to have the same root mean square level. The continuum of sounds consisted of 204 stimuli in which the stimulus onset asynchrony varied from +405 ms (horn leading dog) to –405 ms (dog leading horn) in 4 ms steps. Stimuli were allowed to overlap to the degree necessary to create the specified stimulus onset asynchrony values. ‘Short’ sounds were the same stimuli cut to 30 ms duration, thus minimizing stimulus overlap at short stimulus onset asynchrony values at the expense of less distinctive sound qualities. For testing, the same adaptive procedure and data analysis were employed as for FM detection, but the subjects indicated simply which sound (dog or car horn) they heard first. Feedback as to the correctness of response was given after every trial.

Tests of visual perception

A more detailed description of these tests is available in supplementary material at http://www.lscp.net/persons/ramus/dyslexia02/supp.html .

Experimental procedures and stimulus generation were controlled by a Macintosh computer (Apple Computer). Experiments were run under the MatLab programming environment (Mathworks, Natick, MA, USA). Software for display calibration and stimulus display contained elements of the VideoToolbox ( Pelli, 1997 ) and PsychToolbox ( Brainard, 1997 ) software packages. Stimuli were displayed on a 19 inch Sony Trinitron CRT monitor operating at a screen resolution of 1024 × 768 pixels with a frame refresh rate of 85 Hz. Subjects viewed the screen binocularly at a viewing distance of 228 cm for the acuity experiment and 114 cm for all other conditions. Under these conditions one pixel subtends 0.5 and 1.0 min of arc respectively. Subjects always fixated the centre of the screen, aided by the presence of a continuously visible fixation marker. Subjects made all responses on a numeric keypad clearly marked with available choices.

Experimental procedure

An adaptive psychophysical staircase procedure (QUEST; Watson and Pelli, 1983 ) was used to estimate thresholds. QUEST works by sampling a range of cue levels and using subjects’ responses, in combination with a Bayesian estimator, to attempt to converge on the cue level yielding 83% correct performance on the task. Unless stated otherwise, runs consisted of blocks of 45 trials and at least three runs were undertaken for each data point. Feedback, in the form of an audible beep, was used to indicate errors. Each subject underwent at least three runs in each task and the median of all runs is reported.

Visual acuity

Subjects were presented with a Landolt C, centred on their point of fixation, at one of four orientations (0°, 90°, 180° or 270° rotation). The letters appeared white (100 cd/m 2 ) on a grey (50 cd/m 2 background. By convention, the thickness of the stroke forming the C is 1/5 of the letter diameter, as is the height of the gap. Subjects performed a single‐interval 4AFC (four‐alternative forced choice) task to report the orientation of the letter using the keypad. Stimuli were presented for a total of 500 ms and were smoothly ramped on and off with a Gaussian contrast envelop (σ = 200 ms) to minimize the contribution of transients at the stimulus onset and offset. Threshold sizes of the ‘C’ gap (expressed in arc min) were converted to produce a minimum angle of resolution (MAR). This was then converted to Snellen acuity (Snellen acuity in metres = minimum angle of resolution 6/6*).

Contrast sensitivity, magno‐ versus parvo‐cellular

Perhaps the most direct way to assess magno‐cellular (M) versus parvo‐cellular (P) function is to measure differences in sensitivity to low‐contrast stimuli designed to target each stream. A number of studies have interpreted such contrast sensitivity findings as supporting M deficits in dyslexics (e.g. Martin and Lovegrove, 1987 ; Slaghuis and Ryan, 1999 ) (for a critical review see Skottun, 2000 ; for other objections see Stuart et al ., 2001 ). However, many such studies have been methodologically flawed either in terms of the spatial/temporal frequencies of stimuli employed or because, while some show poor dyslexic performance on M‐specific stimuli, few establish normal performance with P‐specific stimuli ( Skottun, 2000 ). We sought to avoid these pitfalls and measured contrast sensitivity using a grating detection task.

Stimuli were Gabor patterns: cosinusoidal gratings spatially windowed by an isotropic Gaussian contrast envelope (σ = 1.0°) (Fig. 1 A, B). We tested two combinations of spatial and temporal frequency: magnocellular‐selective (M‐selective) stimuli had a peak spatial frequency of 0.5 cycles per degree (c/°) and counter‐phase flickered at the rate of 15 reversals/s, while parvocellular‐selective (P‐selective) stimuli had a peak spatial frequency of 8.0 c/° and did not counter‐phase flicker. Spatial frequency values were chosen to span the point at which psychophysical detection switches from transient to sustained mechanisms (∼1.5 c/°) ( Legge, 1978 ). To further target the magnocellular pathway we followed Demb and colleagues ( Demb et al ., 1998 ) in making M‐selective stimuli low‐luminance, since it is known that the M‐pathway response is dominant at mesopic/scotopic light levels ( Purpura et al ., 1988 ; Lee et al ., 1997 ). M‐selective stimuli therefore had a mean luminance of 5 cd/m 2 (range 0–10 cd/m 2 ) while P‐selective stimuli varied around a mean luminance of 40 cd/m 2 (range 0–80 cd/m 2 ). Stimulus duration was 500 ms. In order to minimize the impact of onset and offset transients in P‐selective conditions, the contrast of all stimuli was smoothly ramped on and off with a Gaussian contrast envelope (σ = 200 ms).

Subjects were presented with two intervals; one randomly selected interval contained a Gabor patch (with carrier in random phase), the other a blank field at background luminance. The subjects’ task was then to indicate which interval contained the grating; this is a 2AFC (two‐alternative forced‐choice) task). The onset of each interval was indicated by an auditory cue, and intervals were separated by a 500 ms ISI. Contrast detection thresholds are presented as percentage Michelson contrast [(L min – L max )/(L min + L max ), where L min and L max are the luminances of the darkest and brightest parts of the display, respectively (in cd/m 2 )].

Speed discrimination, magno‐ versus parvo‐cellular

There is evidence that while poorer contrast sensitivity for M‐selective stimuli may not reliably co‐occur with dyslexia, poor speed discrimination might ( Eden et al ., 1996; Demb et al ., 1998 ). We measured speed discrimination using versions of the stimuli similar to those we used to probe contrast detection (described in the preceding section) but with drifting carriers. The P‐ and M‐selective stimuli were tested with reference speeds of 1.0 and 16.0°/s and contrasts of 20 and 80%, respectively. Speeds were selected not only to target transient and sustained mechanisms, but also to produce equivalent temporal frequencies in terms of carrier cycles/s (i.e. an M : P speed ratio of 16 : 1 and an M : P spatial frequency ratio of 1 : 16). Stimulus contrast was again enveloped using a temporal Gaussian function. However, in order to prevent subjects counting the number of bars passing rather than judging speed, the standard deviation of the envelope was varied uniformly and randomly between 160 and 240 ms. Neither class of stimulus flickered, but in all other respects (e.g. luminance differences) they were identical to the detection stimuli described above.

Subjects were presented with two intervals, both containing a Gabor patch with a carrier drifting randomly to the left or right. In one randomly selected interval the carrier moved at reference speed; in the other it moved slightly faster. Subjects indicated the interval in which the grating moved faster (2AFC). QUEST was used to estimate the percentage increase in speed over baseline required to perform this discrimination with 83% accuracy. Intervals were again separated by an ISI of 500 ms and, although all stimuli were clearly visible, were also audibly pre‐cued.

Coherent motion detection

A number of studies have claimed that dyslexics are poorer at detecting coherent motion embedded in moving noise than normal controls ( Cornelissen et al ., 1995 ; Eden et al ., 1996 ; Raymond and Sorenson, 1998 ; Witton et al ., 1998; Everatt et al ., 1999 ; Slaghuis and Ryan, 1999 ; Talcott et al ., 2000 ), and it has further been claimed that poor coherent motion detection correlates with poor letter position encoding ( Cornelissen et al ., 1998 ). We sought to test these findings and broadly followed the methods of Witton and colleagues ( Witton et al ., 1998 ) for generating stimuli. Subjects were presented with an 8° × 8° field of 150 randomly positioned dots (each subtending 1 arc min), appearing white (100 cd/m 2 ) on a grey background (50 cd/m 2 ), and moving rapidly (11°/s) to the left or the right. Stimulus movies lasted for 900 ms and consisted of 19 distinct frames. Dots appeared for a maximum of four movie frames before being randomly replaced (limited lifetime elements) to minimize the possibility of subjects using tracking eye movements. Subjects performed a single‐interval 2AFC task: to report whether the dots were moving, on average, to the left or the right. The difficulty of the task was manipulated (using QUEST) by replacing a proportion of the elements with dots moving in a random direction (with the same lifetime, speed, etc.). The threshold estimate corresponds to the minimum proportion of coherently moving dots supporting 83% discrimination of direction.

Cerebellar tests

Each subject underwent a battery of tests measuring balance, motor coordination and timing, all involving the cerebellum to some degree. Obviously, poor performance in any of these tests could have causes other than cerebellar dysfunction, but it was hoped that, by bringing together a battery of varied tasks involving the cerebellum, difficulties across the whole battery would be a good indication of cerebellar dysfunction.

Balance/dual task

The subjects’ static balance was assessed in four different conditions of increasing difficulty: (i) eyes open, feet apart; (ii) eyes closed, feet together; (iii) eyes closed, feet together, arms extended; (iv) eyes closed, feet together, arms extended and counting backwards. This last condition was inspired directly by Nicolson and Fawcett (1990 ); the presence of the secondary task is meant to evaluate the automaticity of the subject’s balance. Because dyslexics might find it more difficult to count backwards (because of phonological problems), the difficulty of the task was calibrated as in Nicolson and Fawcett (1990 ): prior to the test session, the speed with which each subject was able to count backwards in 3 s was measured and used to determine the steps in which they should count during the balance dual task: in ones, in twos, in threes or in sevens. In each trial, subjects were instructed to stand as still as possible while measurements were made over a 40 s period. Each of the four conditions was repeated three times for each subject, and the order of the 12 resulting trials was counterbalanced across two groups of subjects.

In order to assess the subjects’ stability more objectively than in previous studies, we measured body movements and the changes in position of the ground reaction force (centre of foot pressure, CoP). Movements of the body were measured using an opto‐electronic motion analysis system (CODA mpx30; Charnwood Dynamics, Rothley, UK), which tracked in three dimensions infrared‐emitting diodes attached to anatomical landmarks. Movements at the level of the neck and wrists were obtained from infrared‐emitting diodes that were fixed to the skin over the C7 spinous process and over both ulnar styloid processes. Movements of the CoP between the feet and the ground were calculated from the distribution of forces measured from a force plate (Kistler type 9287; Kistler Instrumente, Winterthur, Switzerland). The force plate data were low‐pass filtered (50 Hz cut‐off frequency) before digitization. All data were sampled at 100 Hz. Body stability was assessed by calculating the total distance (path length) travelled by each infrared‐emitting diode in three dimensions and by the CoP in two dimensions during each 40 s trial. To reduce the influence of noise on path length measures, the data were averaged over every 10 data points, which reduced the effective sampling rate to 10 Hz. The distances between successive data points were then calculated and summed to give total path length.

Bead threading

Subjects had to thread 15 beads as fast as possible, holding the string in the dominant hand. The dependent measure was total time taken, and was assessed twice. This task and the test material were taken from the Dyslexia Screening Test ( Fawcett and Nicolson, 1996 ).

Finger‐to‐thumb

Subjects placed the index finger of one hand onto the thumb of the other hand and vice versa. Then, keeping the top thumb and finger together, they rotated one hand clockwise and the other anticlockwise until the finger and thumb touched again, and so on. The task was demonstrated and subjects were trained until they completed the movement fluently five times. They were then asked to perform 10 such movements as fast as possible. The measure was the time taken for 10 movements, and was assessed twice. This test was drawn from the Dow and Moruzzi (1958 ) battery and was administered as described by Fawcett and colleagues ( Fawcett et al ., 1996 ).

Repetitive finger‐tapping

Subjects were asked to press repeatedly and as fast as possible a button on a response box with the index finger of their dominant hand. One hundred presses were recorded and the dependent measure was the average interval between two presses. This task was adapted from Denckla and colleagues ( Denckla et al ., 1985 ).

Bimanual finger‐tapping

Bimanual finger‐tapping in synchrony with a metronome was recorded in three conditions: (i) left and right hand alternately at 2 Hz; (ii) left and right hand alternately at 5 Hz; (iii) asymmetrical rhythm (tap twice with the dominant hand then once with the other hand and so forth) at 4 Hz. In each condition, subjects first had to tap for 30 s in synchrony with the metronome, then the metronome stopped and they had to continue for 30 s at exactly the same pace. Subjects had to rest their hands on the table and move only the index fingers at the metacarpophalangeal joint. The metronome sound was produced by a computer, which also recorded the subjects’ responses through a response box. Dependent measures were the average inter‐response interval (IRI) and its standard deviation. Previous work suggested that adult dyslexics would show greater IRI variability in the fast (5 Hz) and asymmetrical conditions ( Wolff et al ., 1990 ; Wolff, 1993 ).

Time versus loudness estimation

Time estimation is the only cerebellar task not involving motor control, and is therefore crucial in distinguishing the cerebellar hypothesis from a solely motor one. We used exactly the same task as Nicolson and colleagues ( Nicolson et al ., 1995 ), which was inspired by Ivry and Keele (1989 ).

In each time estimation trial, two tones were presented successively, and the task was to say whether the second one was longer or shorter than the first. The standard stimulus, always presented first, was a 1200 ms pure tone of frequency 392 Hz. Twenty‐two comparison tones had respective durations of 400, 700, 800, 900, 950, 1000, 1050, 1100, 1140, 1160, 1180, 1220, 1240, 1260, 1300, 1350, 1400, 1450, 1500, 1600, 1700 and 2000 ms. The two tones were separated by a silence interval of 1000 ms. Each trial was repeated three times, giving a total of 66 test trials, which were presented in random order. The test block was preceded by a practice block of eight trials (using only the eight extreme comparison tones), during which feedback was provided. No feedback was provided during the test block. The stimuli were presented by a computer through headphones at ∼75 dB SPL. After each pair of sounds, subjects had to press [s] or [l] on the keyboard for ‘shorter’ or ‘longer’.

The classification function (percentage of shorter responses as a function of the duration of the comparison tone) of each subject was fitted with a logistic function. The parameters of the logistic function were then used to estimate the jn difference at which each subject was 75% correct.

Loudness estimation was a non‐cerebellar control task. This experiment followed exactly the same design as time estimation, except that all tones were 1000 Hz and 1000 ms and differed only in loudness. Comparison tones had respective amplitudes 4, 8, 12, 16, 20, 26, 32, 38, 46, 56 and 70% greater or smaller than the standard stimulus. The calibrated level was ∼67 dB SPL for the standard tone. Subjects had to respond whether the second tone was louder or softer than the first one, pressing [s] or [l]. The same fitting procedure as for time estimation was followed for the percentage of ‘softer’ responses.

Procedure to assess deviance

Since one of the goals of this study was to determine in which domains a given dyslexic individual did and did not show abnormal performance, it was necessary to adopt a criterion for deviance. A common procedure is to set a threshold at n standard deviations of the mean of the control group. However, there is of course some arbitrariness in the choice of the value of n , and no value has been used consistently in the literature.

In the present study we chose n = 1.65 SD. In a normal distribution, this corresponds to the fifth percentile, which seems a reasonable threshold for deviance. However, because a control subject may occasionally show abnormal performance in one task, there is a risk that the control mean and standard deviations might be skewed by such points of data, which might make the criterion more stringent than intended. For this reason, we applied the criterion in two steps: (i) compute the control mean and standard deviation and identify control subjects who qualify for abnormal performance according to the 1.65 SD criterion (typically, this applied to 0 or 1 control subject for each measure); (ii) recompute the control mean and standard deviation excluding these control subjects, and identify dyslexics who are outside ±1.65 SD.

The application of this procedure to the results described below seemed to confirm that it successfully identified those dyslexic subjects whose performance was outside the range of most of the controls.

Results are presented in Table 1 . The two groups were adequately matched for sex (eight males and eight females in each group), handedness (two controls and one dyslexic left‐handed) and full‐scale IQ. Dyslexics scored significantly higher on one performance subtest of the WAIS, picture completion [ F (1,30) = 6.1, P  < 0.05]. On the other hand, they scored significantly lower in verbal IQ [ F (1,30) = 6.5, P = 0.016], which is directly attributable to their significantly lower scores in two verbal subtests of the WAIS: digit span [ F (1,30) = 21, P  < 0.001] and letter–number sequencing [ F (1,30) = 14.9, P = 0.001]. Furthermore, they were marginally poorer at arithmetic [ F (1,30) = 3.6, P = 0.069]. The straightforward reason for these lower scores is that these three subtests load heavily on verbal short‐term memory, which is known to be affected in dyslexics as part of their phonological deficit ( Brady et al ., 1983 ). The three scores are subsumed by the Working Memory Index (WMI) of the WAIS, which was therefore also significantly different between the two groups [ F (1,30) = 28, P  < 0.001]. In the rest of the analyses, the WMI will be taken as an additional measure of phonological performance, since it is a sensitive measure of the ability to accurately receive, retain, manipulate and reproduce phonological representations.

Table 2 shows that dyslexics were significantly poorer than controls in all measures of literacy: WRAT reading [ F (1,30) = 30, P  < 0.001], WRAT spelling [ F (1,30) = 85.5, P  < 0.001], NART [ F (1,30) = 25.4, P  < 0.001], reading speed [ F (1,30) = 12.7, P = 0.001], non‐word reading accuracy [ F (1,30) = 22, P  < 0.001] and non‐word reading time [ F (1,30) = 24.7, P  < 0.001]. This last measure is the time taken to produce each non‐word, measured from the onset of display of the non‐word to the offset of the non‐word produced. Times to produce erroneous responses were not excluded, as there was no speed–accuracy trade‐off.

In order to summarize literacy performance for the purpose of deviance analysis, we converted the six relevant variables into Z ‐scores, and averaged these Z ‐scores to produce a single variable called LITERACY, also shown in Table 2 . The deviance analysis on LITERACY found that all but one dyslexic subject (J.G.) and just two control subjects (K.B. and C.C.) showed abnormal performance (one control subject excluded from control statistics). Subject J.G. still was 1.3 SD below the control mean. His file mentioned more severe literacy difficulties at the age of 12, suggesting that his good performance was due to adequate teaching and successful compensation strategies. He was therefore not excluded from the dyslexic group.

The two groups did not differ significantly on the two syntax tests. A deviance analysis on the average of the two tests did not single out any dyslexic subject (one control subject excluded). However, closer examination of each individual’s file revealed that one subject (F.H.) had had phonological difficulties as a child and consequently received speech therapy between ages 5 and 7 years. This suggests that he may have qualified for a diagnosis of SLI. This will be discussed further in the light of his other results.

The two groups did not differ significantly on the score obtained from the ADD questionnaire [ F (1,30) = 2.26, P = 0.14]. However, six dyslexics and one control were found to have a T ‐score that was both deviant according to our criterion (one control excluded) and above 65, the threshold for clinical significance for T ‐scores. Higher scores for dyslexics in this questionnaire are not entirely surprising since five questions out of 40 concerned reading or writing, and three concerned verbal short‐term memory. We recomputed the ADD scores after excluding these questions. Two dyslexics (J.C. and O.N.) and one control (M.M.) remained with deviant scores, and will therefore be considered as potentially presenting an additional attentional disorder.

Table 3 shows that dyslexics were significantly poorer than controls in all phonological tests: rapid picture naming [ F (1,30) = 10.7, P = 0.003], rapid digit naming [ F (1,30) = 20.5, P  < 0.001], spoonerisms in both accuracy [ F (1,30) = 7.5, P = 0.01] and production time [ F (1,30) = 13.4, P = 0.001], and non‐word repetition [ F (1,30) = 7.5, P = 0.01]. In order to assess whether the dyslexics’ poor performance in automatic naming might have been due to overall slowness, we computed a covariance analysis with group as independent variable, digit naming as dependent variable, and the Processing Speed Index of the WAIS as covariate (the Processing Speed Index summarizes performance on the symbol search and digit‐symbol coding subtests). The Processing Speed Index effect was found to be significant [ F (1,29) = 7.1, P = 0.01]. Nevertheless, the group effect was still highly significant [ F (1,29) = 21.5, P  < 0.001] even after differences in overall speed were taken into account. Similar results were obtained with picture naming [group effect, F (1,29) = 10.9, P = 0.003]. Poor performance in rapid automatic naming therefore reflects phonological difficulties beyond individual differences in overall speed.

In order to summarize phonological performance for individual analyses, we averaged the Z ‐scores of the first five variables in Table 3 plus the WMI. This new variable, PHONOLOGY, is also shown in Table 3 , and individual scores are plotted in Fig. 2 A. A deviance analysis on PHONOLOGY reveals that all dyslexics and one control (C.C.) have abnormal phonological performance (one control excluded). It can therefore be concluded that all the dyslexics in this sample suffer from a phonological deficit.

Auditory perception tests

Table 4 shows the results of the speech perception tests. For each subject we considered the average of the two thresholds measured per condition. Two values are given for syllable categorization results: the position of the boundary along the continuum and the jnd (the number of steps required for the categorization to shift from 50 to 75%). The jnd for the four‐interval forced‐choice ba – da discrimination is also given, together with that for the non‐speech control condition.

The two groups did not differ significantly in any of the speech categorization tasks. However, there was a trend towards a difference for the coat – goat threshold [ F (1,30) = 3.5, P = 0.07]; this was accounted for by five dyslexics who had inordinately high jnds, although they had phoneme boundaries within the control range.

For the ba – da /F2 discrimination task, there was no significant group difference, either in the speech or in the non‐speech condition (with a single formant F2). A paired‐samples t ‐test revealed that jnds were significantly lower in the non‐speech than in the speech condition [ t (31) = 2.2, P = 0.035], consistent with the reduced discriminability of speech stimuli within phoneme categories. A repeated measures analysis showed that this did not interact with the group factor [ F (1,30) < 1].

Results of the non‐speech tests are summarized in Table 5 . For each subject we considered the median of the two to four thresholds measured per condition. The results of two control subjects in the FM 240 Hz task were rejected because of dysfunctional headphones. None of the control conditions (simultaneous masking, absolute thresholds, FM detection at 240 Hz) showed any significant group effect. There was a trend for a group difference in backward masking [ F (1,30) = 2.63, P = 0.11], due to six dyslexics with thresholds >60 dB. There were significant group differences in FM detection at 2 Hz [ F (1,30) = 4.2, P = 0.048] and in temporal order judgement with long stimuli [ F (1,30) = 8.26, P = 0.007] and with short stimuli [ F (1,30) = 6.4, P = 0.017]. In all conditions in which group differences were observed, they were attributable to the high thresholds of five to seven dyslexics.

There are several ways to assess the overall auditory performance of subjects in relation to dyslexia. According to the magnocellular theory, dyslexics should be poor at rapid auditory processing, i.e. tasks involving short sounds or fast transitions ( Tallal et al ., 1993 ). According to another view, those dyslexics who are impaired in the auditory modality are impaired only in tasks involving speech stimuli (as opposed to non‐speech sounds) ( Mody et al ., 1997 ). In order to compare the two hypotheses, we computed several summary variables, which are presented in Table 6 .

RAPID summarizes performance on all tasks involving short sounds or fast transitions; it is the average Z ‐score of ba – da jnd, date – gate jnd, coat – goat jnd, ba – da discrimination, ba – da F2 discrimination, backward masking, simultaneous masking, and temporal order judgement for short and long conditions (even in the long condition, stimulus onset asynchronies became short). Absolute threshold was not considered a rapid processing task, because when it was presented in isolation the short duration of the tone did not make it particularly difficult to detect. Neither was FM detection at 240 Hz, since at this frequency the modulations are not resolved by the auditory system. SLOW is the average Z ‐score of all the other jnds: absolute threshold and FM detection at 2 and 240 Hz.

SPEECH is the average Z ‐score of the tasks involving speech: the three syllable categorization tasks and ba – da discrimination. NONSPEECH is the average Z ‐score of all the other jnds: ba – da F2 discrimination, backward masking, simultaneous masking, absolute threshold, temporal order judgement for short and long conditions, and FM detection at 2 and 240 Hz.

Since some results in the literature are consistent neither with the rapid auditory processing theory nor with the speech‐specific theory (e.g. poor performance on slowly varying non‐speech sounds, Witton et al ., 1998 ), we computed a more pragmatic variable, AUDITORY, summarizing all the tasks which (i) have shown poor performance in dyslexics in the literature, or (ii) should show poor performance in dyslexics according to at least one theory. This variable averaged the Z ‐scores of ba – da jnd, date – gate jnd, coat – goat jnd, ba – da discrimination, ba – da F2 discrimination, backward masking, temporal order judgement for short and long conditions, and FM detection at 2 Hz (i.e. the same as RAPID, without simultaneous masking and with FM 2 Hz).

All summary variables showed a significant group effect. A repeated measures analysis revealed no interaction between group and RAPID versus SLOW [ F (1,30) = 1.27, P = 0.27], showing that dyslexics were not worse at tasks involving rapid auditory processing than at other tasks. Furthermore, a deviance analysis found abnormal performance in seven dyslexics and one control in RAPID and six dyslexics and one control in SLOW (one control excluded in each task). Overall, our results do not support the hypothesis that dyslexics are specifically impaired at rapid auditory processing. Similarly, there was no interaction between group and SPEECH versus NONSPEECH [ F (1,30) < 1], showing that dyslexics were not worse at speech tasks than at non‐speech tasks. A deviance analysis found abnormal performance in seven dyslexics and two controls in SPEECH (one control excluded) and five dyslexics in NONSPEECH. Our results therefore do not support the speech‐specific hypothesis either.

The ‘pragmatic’ AUDITORY score showed the greatest difference between controls and dyslexics [ F (1,30) = 8.58, P = 0.006], with 10 dyslexics out of 16 and one control showing abnormal performance (one control excluded). Individual scores are plotted in Fig. 2 B. Unfortunately, no obvious construct seems to be able to capture what it is that all these auditory tasks have in common and that tasks such as simultaneous masking and FM detection at 240 Hz do not have. This remains true even if one considers only the most sensitive tasks, i.e. temporal order judgement for short and long conditions and FM 2 Hz. We therefore have to conclude, like Rosen and Manganari (2001 ), that an explanation for the auditory deficits observed in certain dyslexics has to be more sophisticated than just rapid auditory, or speech, processing.

This is further confirmed by looking at the individual scores for the summary auditory variables (Table 7 ). There seems to be no regularity whatsoever in the nature of the auditory deficits that dyslexics have. For instance, within the dyslexic group there are double dissociations between fast and slow auditory processing (A.W., M.L. and V.F. versus A.J. and N.D.C.), as well as between speech and non‐speech perception (S.M., K.H., V.F. and J.C. versus L.P. and J.G.). Some dyslexics seem to have absolutely no auditory deficit (M.W., D.M., O.N.) and some have relatively focal problems (A.J., N.D.C., S.M.), while others are impaired across the board (F.H., D.T.).

In summary, we find that a significant proportion of dyslexics are impaired in the auditory domain. However, there is great heterogeneity in the nature of the problem. Depending on how one construes the auditory deficit, between seven and 10 dyslexics out of 16 were affected, compared with just one or two controls. Certain dyslexics, on the other hand, seemed to have entirely intact auditory abilities. This is consistent with all previous studies in which individual data have been examined. This conclusion holds even when using a far wider array of auditory tasks than in previous studies.

Visual perception tests

One dyslexic subject had to be excluded from this part of the study because he was blind in one eye. All subjects had a Snellen acuity above 6/9.3. Mean thresholds for the two groups are presented in Table 8 . None of the variables showed a significant group effect (all P values >0.20).

In coherent motion detection, it appears that our subjects had much higher thresholds than in comparable published studies (e.g. Witton et al ., 1998 ). The reason seems to be our use of smaller dots and the fact that the experiment was run under low‐luminance conditions, both in the testing room and on the monitor. As the magnocellular system is particularly sensitive to low‐luminance conditions, this should have increased the probability of observing magnocellular deficits. However, this increased the overall difficulty of the task so much that two subjects (one control, one dyslexic) were unable to perform it even at 100% coherence. This floor effect therefore prevents us from knowing whether some dyslexics were particularly impaired in this task. For this reason, this variable is not included in the deviance analysis.

We computed a summary variable, VISION, as the average Z ‐score of ‘contrast sensitivity magno’ and ‘speed discrimination magno’. A deviance analysis on this variable found that just two dyslexics out of 15 and two controls had abnormal performance in the magnocellular conditions (one control excluded) (see individual data in Fig. 2 C). This is consistent with previous studies in which individual data were examined; for instance, Cornelissen and colleagues found between five and 10 dyslexics out of 29 who were outside the range of most controls ( Cornelissen et al ., 1995 ), and Witton and colleagues found around four out of 17 (both in coherent motion) ( Witton et al ., 1998 ).

The steps in which each subject counted backwards were determined so as to equate the difficulty of the tasks across the subjects. Among the controls, three counted in twos, nine in threes and four in sevens, while among the dyslexics, one counted in ones, 11 in twos and four in threes (χ 2 = 11.5, P  < 0.01). This factor did not correlate with any measure of balance/dual task.

In each condition and measure, the three repeated measures per subject were averaged. There was a total of 12 variables across the four conditions (path lengths of the CoP and the C7 diode for the two conditions with arms alongside; CoP and C7 plus the two hands for the two conditions with arms extended). Means and standard deviations of all measures are summarized in supplementary material ( http://www.lscp. net/persons/ramus/dyslexia02/supp.html ). In order to assess group differences, a multivariate covariance analysis was performed with height and weight as covariates, as these factors might have had an influence on a subject’s stability. In fact they did not have any significant effect on the measures. Furthermore, none of the measures was found to differ significantly between groups, even in the dual task condition [all F (1,28) < 1].

In order to summarize the balance results for further analyses, we averaged the Z ‐scores of these 12 variables into a single BALANCE score (Table 9 ). This new variable did not differ significantly between groups [ F (1,30) < 1]. A deviance analysis found two dyslexics (O.N. and D.T.) and two controls with abnormal performance in BALANCE (one control excluded).

For each task, the mean and standard deviation of IRIs during the first 30 s (with metronome) and during the next 30 s (without metronome) are reported in supplementary material ( http://www.lscp.net/persons/ramus/dyslexia02/supp.html ). None of these variables showed any significant group effect. Following Wolff and colleagues ( Wolff et al ., 1990 ), we used only IRI standard deviations for subsequent analyses.

We summarized performance in finger‐tapping by averaging the Z ‐scores of all IRI standard deviations to form the new variable BIMANUAL (Table 9 ). This variable did not differ significantly between the two groups [ F (1,30) = 1.1, P = 0.3]. A deviance analysis found four dyslexics (M.L., V.F., N.C. and D.T.) and two controls with abnormal BIMANUAL scores (one control excluded).

Time/loudness estimation

In both tasks, the fit of the logistic regression was significant for all subjects (all P values <0.001). The jnds for duration and loudness differences were analysed. Neither variable showed a significant group difference. Two dyslexics (M.L. and V.F.) and two controls had abnormally high thresholds in time estimation (one control excluded). Curiously, five dyslexics (M.W., N.D.C., M.L., F.H. and D.T.) and one control were deviant on loudness estimation (one control excluded). Considering that M.L., V.F., F.H. and D.T. were already deviant on AUDIO, it seems that both time and loudness estimations tap some aspect of auditory function. Thus, it may not be very appropriate to interpret poor performance in time estimation as an indicator of cerebellar dysfunction.

Repetitive finger‐tapping, finger‐to‐thumb and bead‐threading

For bead‐threading and finger‐to‐thumb, each task was performed twice, and only the best score was recorded. The bead‐threading data for three dyslexic subjects were missing. Mean scores for each group are reported in Table 9 . None of these cerebellar tests showed any significant difference between groups.

We computed a new variable, CEREB, averaging the Z ‐scores of all the cerebellar tests reported in Table 9 (including BALANCE and BIMANUAL but excluding loudness estimation, since this was only a control task). This variable did not differ significantly between the two groups [ F (1,30) < 1]. A deviance analysis on this variable suggests that four dyslexics (M.L., O.N., D.T. and J.G.) and two controls had abnormal overall performance in the cerebellar tests (one control excluded) (see individual data in Fig. 2 D). However, considering that just one of these four dyslexics (M.L.) was impaired in time estimation and that he was also impaired in loudness estimation and other auditory tasks, it is not quite clear whether the CEREB variable reflects cerebellar dysfunction at all, or whether it simply reflects some aspect of motor control. Similarly, only two dyslexics (O.N. and D.T.) had impaired balance, only one of them in the dual task (D.T.). This casts doubt on the idea of a general automaticity deficit. The present results are intermediate between reports of a high incidence (>50%) of motor/cerebellar disorders in dyslexics ( Fawcett et al ., 1996 ; Ramus et al ., 2003 ) (but their criterion was ±1 SD of the control mean) and reports of no such disorders ( Wimmer et al ., 1998 ; van Daal and van der Leij, 1999 ).

Further analyses

Relationship between auditory and phonological performance.

The great heterogeneity of auditory performance observed in the dyslexic group, when compared with the relative homogeneity of the phonological deficit, would suggest that there is no meaningful relationship between the two domains. Yet there was a significant correlation between AUDITORY and PHONOLOGY ( r = –0.54; P = 0.001) (Table 10 ). That is, AUDITORY accounted for 29.6% of the variance in PHONOLOGY. In order to be really meaningful, this correlation should hold within each group, since an overall correlation is predicted even without causation by virtue of the differences between the two groups along both dimensions. In fact, the correlation held within the control group ( r = –0.6, P = 0.01) but not within the dyslexic group ( r = –0.3, P = 0.26), a rather surprising finding since the dyslexic group showed greater variability. The scatterplot (Fig. 3 ) seems to indicate that auditory performance does not really predict phonological performance, but rather that it places an upper limit on it. In other words, poor audition entails poor phonology, but the reverse is not true: some subjects had very poor phonology but excellent audition (e.g. O.N. and M.W.).

In order to further explore the relationship between auditory and phonological skills, we looked at the correlations between the phonological tasks and the summary auditory variables (Table 11 11). Obviously, the numbers in the present multiple case study do not allow powerful correlation analyses; indeed, if a Bonferroni correction were applied here, the only significant correlation would be between picture‐naming and SLOW. Yet Table 11 11 provides interesting indications: that naming tasks seem to correlate with NONSPEECH and SLOW auditory processing, while spoonerism accuracy and non‐word repetition correlate with SPEECH and RAPID auditory processing. (Note that the variables summarized in SPEECH are also included in RAPID, and those summarized in SLOW are also included in NONSPEECH, so these associations are expected by design.) Verbal short‐term memory (WMI), on the other hand, does not seem to correlate reliably with any of the auditory variables, suggesting that some aspects of phonology might be less affected by auditory problems. If such a pattern of correlations were to be confirmed in future studies, it would suggest, interestingly, that different types of auditory deficits might affect different aspects of phonology.

It is worth noting that the correlation between SPEECH and spoonerisms and non‐word repetition may arise for two different reasons. The straightforward interpretation is that speech perception skills have a developmental impact on phonological skills, as measured by spoonerisms and non‐word repetition. But is also likely that, whether or not speech perception affects phonology, it affects performance in these particular tasks. Indeed, difficulties discriminating, say, between /b/ and /d/ must make it more difficult to correctly repeat a non‐word containing /b/ or /d/, and likewise for spoonerisms. So a correlation between SPEECH (and therefore RAPID) and phonological tasks involving speech perception is expected, even in the absence of developmental causation. However, this reasoning does not generalize easily to other correlations, e.g. between rapid automatic naming and SLOW.

In summary, the present results suggest that certain auditory deficits may act as aggravating factors for certain aspects of phonological performance, but do not seem strictly necessary for a phonological deficit to occur in the first place.

Role of vision and cerebellar function

CEREB was found to correlate weakly with AUDITORY ( r = 0.35, P = 0.05), but this would not survive Bonferroni correction. Examination of the scatterplot suggests that the correlation is due to just one outlier (D.T., the worst performer in both domains), whose removal does indeed annihilate the effect ( r = 0.04, P = 0.84). Therefore, CEREB does not seem to have any effect on the other variables. Neither does VISION (Table 10 ).

Overlap between the different disorders

Figure 4 summarizes the individual data across the different domains. As we have seen before, 16 dyslexics out of 16 had poor performance in phonology, 10 in audition, four in cerebellar function and two in magnocellular vision. There is some overlap between cerebellar and auditory disorders. In the present sample, as also reported by Witton and colleagues ( Witton et al ., 1998 ), visual disorders were confined to a subset of the auditorily affected dyslexics. Finally, five dyslexics seemed to be entirely unaffected by any sensory or motor/cerebellar disorder, i.e. they seemed to have a purely phonological dyslexia.

Predictors of literacy

The fact that five of the dyslexics seemed to have a phonological deficit without any sensory or motor disorder suggests that a pure phonological deficit is sufficient to cause a reading impairment. The question therefore arises whether the sensory or motor disorders observed in some individuals make an additional contribution to reading problems.

This question was investigated by running a stepwise multiple linear regression of LITERACY on PHONOLOGY, AUDITION, VISION and CEREB. The main predictor by far was PHONOLOGY, accounting for 76.1% of the variance [ F (1,30) = 95.4, P  < 0.001]. AUDITORY was found to account for an additional 4.2% of the variance [ F (1,29) = 6.2, P = 0.02] (when entered first, AUDITORY accounted for 41.8% of the variance). Finally, CEREB was found to account for an additional 4.8% of the variance [ F (1,28) = 9.1, P = 0.005]. However, the coefficient of CEREB does not have the predicted sign; indeed, the greater (poorer) the CEREB score, the greater (better) the LITERACY residuals. We see no explanation of this relationship other than chance. We therefore conclude that CEREB does not actually contribute to the variance in LITERACY. VISION was not a significant predictor in the regression, contrary to the hypothesis that visual problems might be an additional factor of reading impairment.

How might auditory performance affect literacy, in addition to its impact on phonological performance? One possible link is via spelling. Indeed, WRAT spelling was the only literacy task that involved speech perception. Therefore, speech perception problems may affect the spelling of unknown words (non‐words, for practical purposes). One would then expect that spelling is the literacy task that AUDITORY is most correlated with. This was indeed the case ( r = –0.609, P  < 0.001), although by very little (with WRAT reading: r = –0.607, P  < 0.001). Of course, reading and spelling are themselves highly correlated ( r = 0.82, P  < 0.001), so little difference could be expected. The only other direct link we can think of between audition and literacy is that all the reading tasks involve speaking aloud, which itself may require auditory feedback for efficient self‐correction. Presumably these two weak links are sufficient to explain the 4.2% of additional variance.

Possible role of additional developmental disorders

As we recalled in the Introduction, some researchers have proposed that auditory and motor/cerebellar deficits are found only in dyslexics who have an additional developmental disorder—SLI and ADHD respectively. In the present study, we specifically tried to avoid such comorbid cases. However, two dyslexics had abnormally high scores on the ADD questionnaire: J.C. and O.N. O.N. happens also to be an outlier on the CEREB variable, but J.C. seems to be a pure phonological case. We have also mentioned earlier that, according to his file, subject F.H. might be a case of mild SLI. He happens to have had the second worst AUDITORY score. No other indication of any additional developmental disorder was found in the present sample. Our results suggest that, although comorbid developmental disorders may increase the likelihood of observing sensory/motor disorders in dyslexic individuals, this is not the whole story. A good number of our subjects have sensory or motor problems without having any sign or history of SLI or ADHD (for a similar conclusion see also Ramus et al ., 2003 ).

As in most previous studies of dyslexia, we found that the most significant cognitive problem of dyslexic individuals lies in phonological skills. Our analysis of individual data even shows that all the dyslexics in our sample were so affected. Obviously, this does not preclude the existence of reading‐impaired people whose problem is not phonological. It remains perfectly possible that other, less frequent disorders can provoke reading impairments entirely independently of phonology; this might be the case in visual stress, for instance ( Wilkins, 1995 ).

We found that a significant number of dyslexics in our sample (10 out of 16) had auditory problems. This is a rather higher incidence than in previous studies, where it ranged from 0 to 50%, with typically one‐third of dyslexics affected. Previous studies are actually consistent with the results we obtained on any particular auditory task considered separately. The higher incidence found here results from the administration of a greater number of tasks than in any previous study (12 measures per individual) and from the compounding of all the relevant variables to make a more sensitive measure of auditory performance. However, it is not the case that these auditory problems can be characterized as a rapid auditory processing deficit, as predicted by the magnocellular theory, and neither is it the case that they can be reduced to a speech perception deficit; actually, no coherent construct seems to be able to characterize the patterns observed. Rather, it seems that, within each individual, the pattern of good and poor auditory performance is more or less random, and this pattern varies considerably across subjects. Nevertheless, auditory performance does have a significant impact on phonological skills, accounting for 30% of the variance. In other words, dyslexics who have an auditory impairment have, to a certain extent, an aggravated phonological deficit.

As a speculation, we mention an alternative, perhaps more parsimonious possibility: that the scattered auditory problems would be due to a failure in top‐down processes. Indeed, phonological processes might provide top‐down control through expectancies that enhance low‐level auditory perception. At least in the visual domain, the ubiquity of such top‐down enhancement in sensory hierarchies is increasingly demonstrated in single‐cell recordings and brain imaging studies ( Friston and Büchel, 2000 ; Lamme and Roelfsema, 2000 ; O’Connor et al ., 2002 ).

We also found that motor problems were present in certain dyslexics (four out of 16), even in the absence of measurable comorbid ADHD. However, the results obtained on time estimation and the balance/dual task do not militate in favour of a cerebellar origin or a general automaticity deficit (this is consistent with Stringer and Stanovich, 1998 ; Wimmer et al ., 1998 ; Ramus et al ., 2003 ). Finally, our data raise the question whether motor problems play any causal role in dyslexia. Contrary to the predictions of the cerebellar theory, we found no influence of motor/cerebellar performance either on phonology or on literacy. This might be due to the low prevalence of motor/cerebellar problems in the present sample (four out of 16), but this is consistent with another study in which the prevalence was higher ( Ramus et al ., 2003 ).

Only two of the dyslexics in our sample seemed to have visual problems of a magnocellular nature. This is in line with other studies in which individual data also showed a relatively low incidence of visual deficits. This low incidence, together with the fact that the two visually impaired dyslexics also have auditory and phonological problems, makes it impossible, using the present data, to assess whether visual performance may have an independent contribution to reading impairment.

The generalizability of the present study may be intrinsically limited by the particularities of the population studied, which is not representative in several respects: sex, achievement and age. Because we selected an equal number of males and females, whereas dyslexia is thought to be more frequent in males, one could argue that our sample was biased towards the female pattern, which may be a milder form of dyslexia. To test this hypothesis, we ran analyses of variance with sex and group as independent variables and LITERACY, PHONOLOGY, AUDITORY, VISION and CEREB as dependent variables. We found no main effect of sex on any of the variables (all P values >0.10), and a significant sex × group interaction only on CEREB [ F (1,27) = 5.5, P = 0.027], revealing that males were more impaired than females in the dyslexic group but not in the control group. Therefore, our sex ratio may have led us to slightly underestimate motor problems in the dyslexic group compared with the general dyslexic population.

Having selected high‐achieving adult dyslexics is another obvious source of bias, which may have decreased the incidence of each disorder and the overlap between disorders. This implies again that the incidence reported for each disorder in the present sample is not to be generalized to the whole dyslexic population. At this stage, it should be recalled that the main goal of this study was not to establish the respective incidence of the different deficits associated with dyslexia, but to assess the extent to which they were associated or could be dissociated. In this respect, we found that motor difficulties seem dissociable from auditory and visual deficits, and, most importantly, that a phonological deficit can arise in the absence of auditory, visual and motor impairments.

These conclusions might be moderated by the age bias: indeed, it is in principle conceivable that sensory and motor impairments are always present in dyslexic children, and that they somehow disappear through development in certain individuals. If this were the case, our cases of pure phonological dyslexia might just be an illusion due to sensory‐motor recovery. How likely is this possibility? Most studies supporting the magnocellular theory have been run on adults (because of the constraints of psychophysical tasks), with positive findings and no suggestion that they might be more positive in children. Conversely, many negative findings of auditory or visual deficits were from studies on children. Finally, a recent study of dyslexic children aimed at replicating the present study without the sex, age and achievement biases has found similar results, i.e. a limited incidence of sensory and motor disorders, with cases of pure phonological deficits (S. White, E. Milne, S. Rosen, P. C. Hansen, J. Swettenham, U. Frith, F. Rasmus, unpublished results). Thus, it appears that sensory‐motor deficits do not play a greater role in explaining dyslexia in children than they do in adults. Of course, it remains possible that auditory or motor deficits act much earlier in infancy, setting phonological acquisition off‐track, then recovering in most cases before school age (note that this is not a plausible scenario for visual deficits, since if they recovered before school age, little impact would be expected on reading). Such a hypothesis can only be tested in longitudinal studies starting at birth. Differences in auditory and speech perception between at‐risk and control infants have indeed been documented ( Leppanen et al ., 1999 ; Pihko et al ., 1999 ; Molfese, 2000 ; Guttorm et al ., 2001 ; Richardson et al ., 2003 ). However, methodological limitations have made it impossible to consider infants’ individual performance, and therefore these studies cannot address the possibility that some dyslexic infants have intact auditory processing. Never theless, the twin study of SLI children conducted by Bishop and colleagues ( Bishop et al ., 1999 ) suggests that phonological deficits (assessed by non‐word repetition) have a largely genetic origin, while auditory deficits (assessed by Tallal’s repetition test) have not, and instead may be due to environmental influences. If this is to be extrapolated to dyslexia and to other measures of phonological and auditory processing, it may well be the case that auditory disorders are not necessary for a phonological deficit to arise.

The results of the present study support the phonological deficit theory of developmental dyslexia. A phonological deficit may not be a necessary cause of dyslexia, given the possibility of other independent (but rare) causes of reading impairment, but the present comprehensive study suggests that it is a sufficient cause. The phonological deficit can arise independently of any sensory or motor impairment. Nevertheless, a significant proportion of dyslexics suffer from additional auditory, visual or motor disorders. Auditory deficits, at least, may aggravate the phonological deficit, with consequences for reading impairment. The nature of the auditory deficits observed is not particularly consistent with the hypothesis of a rapid processing deficit related to a magnocellular dysfunction. Neither is the nature of motor/timing impairments particularly consistent with the hypothesis of an automaticity deficit or a cerebellar dysfunction. The nature of the phonological deficit and its relationship to auditory processing difficulties remains to be established. Why sensory and motor disorders are frequently associated with phonological deficits (and other developmental disorders) is still to be understood.

We thank Kingsley Betts, Helen Cheng, Andrew Milne and Robin Wilson for help collecting the data; Eamon McCrory, Sarah Griffiths, Cordelia Fine and Elizabeth Hill for various contributions; John Morton, Rod Nicolson, Angela Fawcett and members of the ICN Dyslexia Club for discussions and advice; Heather van der Lely for providing the two syntax tests and Jon Driver for allowing us access to his soundproof room. We thank the UCL examination section for facilitating access to the dyslexic group, and all the volunteers for their assiduous participation. This work was supported by a Marie Curie fellowship of the European Community programme Quality of Life (QLG1‐CT‐1999‐51305) (F.R.), Medical Research Council grants G9617036 (U.F.) and G0100256 (B.L.D.), a fellowship from the Wellcome Trust (S.C.D.) and a fellowship from the Secretaría de Estado de Universidades, Investigación y Desarrollo, M.E.C., Spain (J.M.C.).

Fig. 1 Stimuli used for contrast sensitivity and speed discrimination. ( A ) Magnocellular‐specific stimulus; ( B ) Parvocellular‐specific stimulus.

Fig. 2 Individual scores on summary factors for each domain. The solid line indicates the control mean and the dashed line the chosen deviance threshold (1.65 SD above the control mean after excluding deviant controls). Deviant individuals are identified, except for phonology, where all dyslexics are deviant. ( A ) phonology; ( B ) audition; ( C ) vision; ( D ) cerebellar function.

Fig. 3 Auditory versus phonological performance.

Fig. 4 Distribution of phonological, auditory, visual and cerebellar disorders in the sample of 16 dyslexic adults. Initials refer to individual dyslexic subjects.

Psychometric tests (mean ± SD)

FSIQ = full‐scale IQ; VIQ = verbal IQ; PIQ = performance IQ; VCI = verbal comprehension index; POI = perceptual orientation index; WMI = Working Memory Index (WAIS); PSI = Processing Speed Index (WAIS). ADD = attention deficit disorder scale. * P  < 0.05; *** P  < 0.001.

Reading and language tests (mean ± SD)

CR = correct response; syl. = syllable; acc. = accuracy; GNRT = Graded Nonword Reading Test; RT = reaction time; ASTOP = Advanced Syntactic Test of Pronominal reference; TAPS = Test of Active and Passive Sentences; NART = National Adult Reading Test. ** P  < 0.01; *** P  < 0.001.

Phonological tests (mean ± SD)

acc. = accuracy; CR = correct response; RT = reaction time; CNREP = Children’s Test of Nonword Repetition. * P  < 0.05; ** P  < 0.01; *** P  < 0.001.

Speech perception tests (mean ± SD)

Unit is the number of steps on the continuum (out of 41 for ba – da and out of 51 for date – gate and coat – goat ).

Non‐speech perception tests (mean ± SD)

* P  < 0.05; ** P  < 0.01. FM = frequency modulation.

Summary auditory variables (mean ± SD)

* P  < 0.05; ** P  < 0.01.

Individual Z‐scores for summary auditory variables for the dyslexic group and deviant controls

Only deviant values (>1.65) are shown.

Visual perception tests (mean ± SD)

Cerebellar tests (mean ± SD)

amp. = amplitude.

Pearson correlations between summary variables across domains

* P  < 0.05; ** P  < 0.01; *** P  < 0.001, no correction applied.

Pearson correlations between phonological and summary auditory scores.

RT = reaction time; CNREP = Children’s Test of Nonword Repetition. * P  < 0.05; ** P  < 0.01, no correction applied.

Adlard A, Hazan V. Speech perception in children with specific reading difficulties (dyslexia). Q J Exp Psychol 1998 ; 51A : 153 –77.

Ahissar M, Protopapas A, Reid M, Merzenich MM. Auditory processing parallels reading abilities in adults. Proc Natl Acad Sci USA 2000 ; 97 : 6832 –7.

Amitay S, Ben‐Yehudah G, Banai K, Ahissar M. Disabled readers suffer from visual and auditory impairments but not from a specific magnocellular deficit. Brain 2002 ; 125 : 2272 –85.

Baker RJ, Rosen S. Evaluation of maximum‐likelihood threshold estimation with tone‐in‐noise masking. Br J Audiol 2001 ; 35 : 43 –52.

Bishop DV, Bishop SJ, Bright P, James C, Delaney T, Tallal P. Different origin of auditory and phonological processing problems in children with language impairment: evidence from a twin study. J Speech Lang Hear Res 1999 ; 42 : 155 –68.

Bradley L, Bryant PE. Difficulties in auditory organisation as a possible cause of reading backwardness. Nature 1978 ; 271 : 746 –7.

Brady SA, Shankweiler DP. Phonological processes in literacy. Hillsdale, NJ: Lawrence Erlbaum; 1991 .

Brady S, Shankweiler D, Mann V. Speech perception and memory coding in relation to reading ability. J Exp Child Psychol 1983 ; 35 : 345 –67.

Brainard DH. The Psychophysics Toolbox. Spat Vis 1997 ; 10 : 433 –6.

Brown TE. Brown Attention‐Deficit Disorder Scales. San Antonio: Psychological Corporation, 1996 .

Brown WE, Eliez S, Menon V, Rumsey JM, White CD, Reiss AL. Preliminary evidence of widespread morphological variations of the brain in dyslexia. Neurology 2001 ; 56 : 781 –3.

Brunswick N, McCrory E, Price CJ, Frith CD, Frith U. Explicit and implicit processing of words and pseudowords by adult developmental dyslexics: a search for Wernicke’s Wortschatz? Brain 1999 ; 122 : 1901 –17.

Carrell TD, Bradlow AR, Nicol TG, Koch DB, Kraus N. Interactive software for evaluating auditory discrimination. Ear Hear 1999 ; 20 : 175 –6.

Cestnick L. Cross‐modality temporal processing deficits in developmental phonological dyslexics. Brain Cogn 2001 ; 46 : 319 –25.

Cornelissen P, Munro N, Fowler S, Stein J. The stability of binocular fixation during reading in adults and children. Dev Med Child Neurol 1993 ; 35 : 777 –87.

Cornelissen P, Richardson A, Mason A, Fowler S, Stein J. Contrast sensitivity and coherent motion detection measured at photopic luminance levels in dyslexics and controls. Vision Res 1995 ; 35 : 1483 –94.

Cornelissen PL, Hansen PC, Gilchrist I, Cormack F, Essex J, Frankish C. Coherent motion detection and letter position encoding. Vision Res 1998 ; 38 : 2181 –91.

Demb JB, Boynton GM, Best M, Heeger DJ. Psychophysical evidence for a magnocellular pathway deficit in dyslexia. Vision Res 1998 ; 38 : 1555 –9.

Denckla MB, Rudel RG, Chapman C, Krieger J. Motor proficiency in dyslexic children with and without attentional disorders. Arch Neurol 1985 ; 42 : 228 –31.

Dow RS, Moruzzi G. The physiology and pathology of the cerebellum. Minneapolis: University of Minnesota Press; 1958 .

Eden GF, Stein JF, Wood HM, Wood FB. Differences in eye movements and reading problems in dyslexic and normal children. Vision Res 1994 ; 34 : 1345 –58.

Eden GF, VanMeter JW, Rumsey JM, Maisog JM, Woods RP, Zeffiro TA. Abnormal processing of visual motion in dyslexia revealed by functional brain imaging. Nature 1996 ; 382 : 66 –9.

Everatt J, Bradshaw MF, Hibbard PB. Visual processing and dyslexia. Perception 1999 ; 28 : 243 –54.

Farmer ME, Klein RM. The evidence for a temporal processing deficit linked to dyslexia: A review. Psychonom Bull Rev 1995 ; 2 : 460 –93.

Farrag AF, Khedr EM, Abel‐Naser W. Impaired parvocellular pathway in dyslexic children. Eur J Neurol 2002 ; 9 : 359 –63.

Fawcett AJ, Nicolson RI. The Dyslexia Screening Test. London: Psychological Corporation; 1996 .

Fawcett AJ, Nicolson RI. Dyslexia: the role of the cerebellum. In: Fawcett AJ, editor. Dyslexia: theory and good practice. London: Whurr; 2001 . p. 89 –105.

Fawcett AJ, Nicolson RI, Dean P. Impaired performance of children with dyslexia on a range of cerebellar tasks. Ann Dyslex 1996 ; 46 : 259 –83.

Frederickson N, Frith U, Reason R. Phonological Assessment Battery. Windsor (UK): NFER‐Nelson; 1997 .

Friston KJ, Büchel C. Attentional modulation of effective connectivity from V2 to V5/MT in humans. Proc Natl Acad Sci USA 2000 ; 97 : 7591 –6.

Galaburda AM, Sherman GF, Rosen GD, Aboitiz F, Geschwind N. Developmental dyslexia: four consecutive patients with cortical anomalies. Ann Neurol 1985 ; 18 : 222 –33.

Galaburda AM, Menard MT, Rosen GD. Evidence for aberrant auditory anatomy in developmental dyslexia. Proc Natl Acad Sci USA 1994 ; 91 : 8010 –3.

Gathercole SE, Baddeley AD. The Children’s Test of Nonword Repetition. London: Psychological Corporation; 1996 .

Geschwind N, Galaburda AM. Cerebral lateralization. Biological mechanisms, associations, and pathology: I. A hypothesis and a program for research. Arch Neurol 1985 ; 42 : 428 –59.

Grant AC, Zangaladze A, Thiagarajah M, Sathian K. Tactile perception in developmental dyslexia. Neuropsychologia 1999 ; 37 : 1201 –11.

Guttorm TK, Leppänen PHT, Richardson U, Lyytinen H. Event‐related potentials and consonant differentiation in newborns with familial risk for dyslexia. J Learn Disabil 2001 ; 34 : 534 –44.

Hari R, Renvall H. Impaired processing of rapid stimulus sequences in dyslexia. Trends Cogn Sci 2001 ; 5 : 525 –32.

Hari R, Renvall H, Tanskanen T. Left minineglect in dyslexic adults. Brain 2001 ; 124 : 1373 –80.

Hazan V, Barrett S. The development of phonemic categorisation in children aged 6 to 12. J Phonet 2000 ; 28 : 377 –96.

Heath SM, Hogben JH, Clark CD. Auditory temporal processing in disabled readers with and without oral language delay. J Child Psychol Psychiatry 1999 ; 40 : 637 –47.

Heim S, Freeman RB Jr, Eulitz C, Elbert T. Auditory temporal processing deficit in dyslexia is associated with enhanced sensitivity in the visual modality. Neuroreport 2001 ; 12 : 507 –10.

Hill NI, Bailey PJ, Griffiths YM, Snowling MJ. Frequency acuity and binaural masking release in dyslexic listeners. J Acoust Soc Am 1999 ; 106 : L53 –8.

Ivry RB, Keele SW. Timing functions of the cerebellum. J Cogn Neurosci 1989 ; 1 : 136 –52.

Johannes S, Kussmaul CL, Munte TF, Mangun GR. Developmental dyslexia: passive visual stimulation provides no evidence for a magnocellular processing defect. Neuropsychologia 1996 ; 34 : 1123 –7.

Klatt DH. Software for a cascade/parallel formant synthesizer. J Acoust Soc Am 1980 ; 67 : 971 –95.

Kronbichler M, Hutzler F, Wimmer H. Dyslexia: verbal impairments in the absence of magnocellular impairments. Neuroreport 2002 ; 13 : 617 –20.

Kujala T, Myllyviita K, Tervaniemi M, Alho K, Kallio J, Näätänen R. Basic auditory dysfunction in dyslexia as demonstrated by brain activity measurements. Psychophysiology 2000 ; 37 : 262 –6.

Lamme VA, Roelfsema PR. The distinct modes of vision offered by feedforward and recurrent processing. [Review]. Trends Neurosci 2000 ; 23 : 571 –9.

Lee BB, Smith VC, Pokorny,J, Kremers J. Rod inputs to macque ganglion cells. Vision Res 1997 ; 37 : 2813 –28.

Legge GE. Sustained and transient mechanisms in human vision: temporal and spatial properties. Vision Res 1978 ; 18 : 69 –81.

Leonard CM, Eckert MA, Lombardino LJ, Oakland T, Kranzler J, Mohr CM, et al. Anatomical risk factors for phonological dyslexia. Cereb Cortex 2001 ; 11 : 148 –57.

Leppanen PH, Pihko E, Eklund KM, Lyytinen H. Cortical responses of infants with and without a genetic risk for dyslexia: II. Group effects. Neuroreport 1999 ; 10 : 969 –73.

Levitt H. Transformed up‐down methods in psychoacoustics. J Acoust Soc Am 1971 ; 49 Suppl 2: 467 –76.

Liberman AM, Mattingly IG. The motor theory of speech perception revised. Cognition 1985 ; 21 : 1 –36.

Livingstone MS, Rosen GD, Drislane FW, Galaburda AM. Physiological and anatomical evidence for a magnocellular defect in developmental dyslexia. Proc Natl Acad Sci USA 1991 ; 88 : 7943 –7.

Lorenzi C, Dumont A, Füllgrabe C. Use of temporal envelope cues by children with developmental dyslexia. J Speech Lang Hear Res 2000 ; 43 : 1367 –79.

Lovegrove WJ, Bowling A, Badcock B, Blackwood M. Specific reading disability: differences in contrast sensitivity as a function of spatial frequency. Science 1980 ; 210 : 439 –40.

Manis FR, Mcbride‐Chang C, Seidenberg MS, Keating P, Doi LM, Munson B, et al. Are speech perception deficits associated with developmental dyslexia? J Exp Child Psychol 1997 ; 66 : 211 –35.

Marshall CM, Snowling MJ, Bailey PJ. Rapid auditory processing and phonological ability in normal readers and readers with dyslexia. J Speech Lang Hear Res 2001 ; 44 : 925 –40.

Martin F, Lovegrove W. Flicker contrast sensitivity in normal and specifically disabled readers. Perception 1987 ; 16 : 215 –21.

McAnally KI, Stein JF. Auditory temporal coding in dyslexia. Proc Natl Acad Science USA 1996 ; 263 : 961 –5.

McArthur GM, Bishop DVM. Auditory perceptual processing in people with reading and oral language impairments: Current issues and recommendations. [Review]. Dyslexia 2001 ; 7 : 150 –70.

McArthur GM, Hogben JH. Auditory backward recognition masking in children with a specific language impairment and children with a specific reading disability. J Acoust Soc Am 2001 ; 109 : 1092 –100.

McCrory E. A neurocognitive investigation of phonological processing in dyslexia. London PhD: University College London; 2001 .

McCrory E, Frith U, Brunswick N, Price C. Abnormal functional activation during a simple word repetition task: a PET study of adult dyslexics. J Cogn Neurosci 2000 ; 12 : 753 –62.

Mody M, Studdert‐Kennedy M, Brady S. Speech perception deficits in poor readers: auditory processing or phonological coding? J Exp Child Psychol 1997 ; 64 : 199 –231.

Molfese DL. Predicting dyslexia at 8 years of age using neonatal brain responses. Brain Lang 2000 ; 72 : 238 –45.

Nagarajan S, Mahncke H, Salz T, Tallal P, Roberts T, Merzenich MM. Cortical auditory signal processing in poor readers. Proc Natl Acad Sci USA 1999 ; 96 : 6483 –8.

Neale MD. Neale Analysis of Reading Ability Revised. 2nd ed. Windsor (UK): NFER‐Nelson; 1997 .

Nelson HE. National Adult Reading Test. 2nd ed. Windsor (UK): NFER‐Nelson; 1991 .

Nicolson RI, Fawcett AJ. Automaticity: a new framework for dyslexia research? Cognition 1990 ; 35 : 159 –82.

Nicolson RI, Fawcett AJ, Dean P. Time estimation deficits in developmental dyslexia: evidence of cerebellar involvement. Proc R Soc Lond B Biol Sci 1995 ; 259 : 43 –7.

Nicolson RI, Fawcett AJ, Berry EL, Jenkins IH, Dean P, Brooks DJ. Association of abnormal cerebellar activation with motor learning difficulties in dyslexic adults. Lancet 1999 ; 353 : 1662 –7.

Nicolson R, Fawcett AJ, Dean P. Dyslexia, development and the cerebellum. Trends Neurosci 2001 ; 24 : 515 –6.

Nittrouer S. Do temporal processing deficits cause phonological processing problems? J Speech Lang Hear Res 1999 ; 42 : 925 –42.

O’Connor DH, Fukui MM, Pinsk MA, Kastner S. Attention modulates responses in the human lateral geniculate nucleus. Nat Neurosci 2002 ; 5 : 1203 –9.

Paulesu E, Frith U, Snowling M, Gallagher A, Morton J, Frackowiak RSJ, et al. Is developmental dyslexia a disconnection syndrome? Evidence from PET scanning. Brain 1996 ; 119 : 143 –57.

Paulesu E, Démonet J‐F, Fazio F, McCrory E, Chanoine V, Brunswick N, et al. Dyslexia: cultural diversity and biological unity. Science 2001 ; 291 : 2165 –7.

Pelli DG. The VideoToolbox software for visual psychophysics: transforming number into movies. Spat Vis 1997 ; 10 : 437 –42.

Pihko E, Leppanen PH, Eklund KM, Cheour M, Guttorm TK, Lyytinen H. Cortical responses of infants with and without a genetic risk for dyslexia: I. Age effects. Neuroreport 1999 ; 10 : 901 –5.

Pugh KR, Mencl WE, Shaywitz BA, Shaywitz SE, Fulbright RK, Constable RT, et al. The angular gyrus in developmental dyslexia: task‐specific differences in functional connectivity within posterior cortex. Psychol Sci 2000 ; 11 : 51 –6.

Purpura K, Kaplan E, Shapley RM. Background light and the contrast gain of primate P and M retinal ganglion cells. Proc Natl Acad Sci USA 1988 ; 85 : 4534 –7.

Rae C, Lee MA, Dixon RM, Blamire AM, Thompson CH, Styles P, et al. Metabolic abnormalities in developmental dyslexia detected by 1H magnetic resonance spectroscopy. Lancet 1998 ; 351 : 1849 –52.

Ramus F. Dyslexia. Talk of two theories. Nature 2001 ; 412 : 393 –5.

Ramus F, Pidgeon E, Frith U. The relationship between motor control and phonology in dyslexic children. J Child Psychol Psychiatry . In press 2003 .

Raymond J, Sorenson R. Visual motion perception in children with dyslexia: normal detection but abnormal integration. Visual Cogn 1998 ; 5 : 389 –404.

Reed MA. Speech perception and the discrimination of brief auditory cues in reading disabled children. J Exp Child Psychol 1989 ; 48 : 270 –92.

Richardson U, Leppänen PHT, Leiwo M, Lyytinen H. Speech perception differs in infants at familial risk for dyslexia as early as six months of age. Dev Neuropsychol . In press 2003 .

Rosen S, Manganari E. Is there a relationship between speech and nonspeech auditory processing in children with dyslexia? J Speech Lang Hear Res 2001 ; 44 : 720 –36.

Ruff S, Cardebat D, Marie N, Demonet JF. Enhanced response of the left frontal cortex to slowed down speech in dyslexia: an fMRI study. Neuroreport 2002 ; 13 : 1285 –9.

Schulte‐Körne G, Deimel W, Bartling J, Remschmidt H. Auditory processing and dyslexia: evidence for a specific speech processing deficit. Neuroreport 1998 a; 9 : 337 –40.

Schulte‐Körne G, Deimel W, Bartling J, Remschmidt H. Role of auditory temporal processing for reading and spelling disability. Percept Mot Skills 1998 b; 86 : 1043 –7.

Serniclaes W, Sprenger‐Charolles L, Carré R, Démonet J‐F. Perceptual discrimination of speech sounds in developmental dyslexia. J Speech Lang Hear Res 2001 ; 44 : 384 –99.

Share DL, Jorm AF, MacLean R, Matthews R. Temporal processing and reading disability. Read Writ Interdisciplin J 2002 ; 15 : 151 –78.

Shaywitz SE, Shaywitz BA, Pugh KR, Fulbright RK, Constable RT, Mencl WE, et al. Functional disruption in the organization of the brain for reading in dyslexia. Proc Natl Acad Sci USA 1998 ; 95 : 2636 –41.

Shaywitz BA, Shaywitz SE, Pugh KR, Mencl WE, Fulbright RK, Skudlarski P, et al. Disruption of posterior brain systems for reading in children with developmental dyslexia. Biol Psychiatry 2002 ; 52 : 101 –10.

Skottun BC. The magnocellular deficit theory of dyslexia: the evidence from contrast sensitivity. [Review]. Vision Res 2000 ; 40 : 111 –27.

Slaghuis WL, Ryan JF. Spatio‐temporal contrast sensitivity, coherent motion, and visible persistence in developmental dyslexia. Vision Res 1999 ; 39 : 651 –68.

Snowling MJ. Phonemic deficits in developmental dyslexia. Psychol Res 1981 ; 43 : 219 –34.

Snowling MJ. Dyslexia. 2nd ed. Oxford: Blackwell; 2000 .

Snowling MJ, Stothard SE, McLean JM. Graded non‐word reading test. Bury St. Edmunds (UK): Thames Valley Test Company; 1996 .

Spinelli D, De Luca M, Judica A, Zoccolotti P. Crowding effects on word identification in developmental dyslexia. Cortex 2002 ; 38 : 179 –200.

Stein JF, Fowler MS. Unstable binocular control in children with specific reading retardation. J Res Read 1993 ; 16 : 30 –45.

Stein J, Walsh V. To see but not to read; the magnocellular theory of dyslexia. Trends Neurosci 1997 ; 20 : 147 –52.

Stein J, Talcott J, Witton C. The sensorimotor basis of developmental dyslexia. In: Fawcett AJ, editor. Dyslexia: theory and good practice. London: Whurr; 2001 . p. 65 –88.

Stoodley CJ, Talcott JB, Carter EL, Witton C, Stein JF. Selective deficits of vibrotactile sensitivity in dyslexic readers. Neurosci Lett 2000 ; 295 : 13 –6.

Stringer R, Stanovich KE. On the possibility of cerebellar involvement in reading disability. In: 4th conference of the Society for Scientific Studies of Reading. San Diego; 1998 .

Stuart GW, McAnally KI, Castles A. Can contrast sensitivity functions in dyslexia be explained by inattention rather than a magnocellular deficit? [Review]. Vision Res 2001 ; 41 : 3205 –11.

Talcott JB, Witton C, McLean MF, Hansen PC, Rees A, Green GG, et al. Dynamic sensory sensitivity and children’s word decoding skills. Proc Natl Acad Sci USA 2000 ; 97 : 2952 –7.

Tallal P. Auditory temporal perception, phonics, and reading disabilities in children. Brain Lang 1980 ; 9 : 182 –98.

Tallal P, Miller S, Fitch RH. Neurobiological basis of speech: a case for the preeminence of temporal processing. [Review]. Ann NY Acad Sci 1993 ; 682 : 27 –47.

Temple E, Poldrack RA, Protopapas A, Nagarajan S, Salz T, Tallal P, et al. Disruption of the neural response to rapid acoustic stimuli in dyslexia: evidence from functional MRI. Proc Natl Acad Sci USA 2000 ; 97 : 13907 –12.

Temple E, Poldrack RA, Salidis J, Deutsch GK, Tallal P, Merzenich MM, et al. Disrupted neural responses to phonological and orthographic processing in dyslexic children: an fMRI study. Neuroreport 2001 ; 12 : 299 –307.

van Daal V, van der Leij A. Developmental dyslexia: Related to specific or general deficits? Ann Dyslex 1999 ; 49 : 71 –104.

van der Lely HK. Specifically language impaired and normally developing children: Verbal passive vs. adjectival passive sentence interpretation. Lingua 1996 a; 98 : 243 –72.

van der Lely HKJ. The Test of Active and Passive Sentences (TAPS). Available from the author at the Centre for Developmental Language Disorders and Cognitive Neuroscience. London: University College London; 1996 b and http://www.ucl.ac.uk/DLDCN/tests.html.

van der Lely HKJ. Advanced‐Syntactic test of Pronominal reference (A‐STOP). Available from the author at the Centre for Developmental Language Disorders and Cognitive Neuroscience. London: University College London; UK, 1997 and http://www.ucl.ac.uk/DLDCN/tests.html.

van der Lely HKJ, Stollwerck L. Binding theory and grammatical specific language impairment in children. Cognition 1997 ; 62 : 245 –90.

van Ingelghem M, van Wieringen A, Wouters J, Vandenbussche E, Onghena P, Ghesquière P. Psychophysical evidence for a general temporal processing deficit in children with dyslexia. Neuroreport 2001 ; 12 : 3603 –7.

Vellutino FR. Dyslexia: research and theory. Cambridge (MA): MIT Press; 1979 .

Victor JD, Conte MM, Burton L, Nass RD. Visual evoked potentials in dyslexics and normals: failure to find a difference in transient or steady‐state responses. Vis Neurosci 1993 ; 10 : 939 –46.

Watson AB, Pelli DG. QUEST: a Bayesian adaptive psychometric method. Percept Psychophys 1983 ; 33 : 113 –20.

Wechsler D. The Wechsler Adult Intelligence Scale. 3rd ed. London: Psychological Corporation; 1998 .

Wilkins AJ. Visual stress. Oxford: Oxford University Press; 1995 .

Wilkinson GS. Wide Range Achievement Test 3. Wilmington (DE): Wide Range; 1993 .

Wimmer H, Mayringer H, Landerl K. Poor reading: a deficit in skill‐automatization or a phonological deficit? Sci Stud Read 1998 ; 2 : 321 –40.

Wimmer H, Mayringer H, Raberger T. Reading and dual‐task balancing: evidence against the automatization deficit explanation of developmental dyslexia. J Learn Disabil 1999 ; 32 : 473 –8.

Witton C, Talcott JB, Hansen PC, Richardson AJ, Griffiths TD, Rees A, et al. Sensitivity to dynamic auditory and visual stimuli predicts nonword reading ability in both dyslexic and normal readers. Curr Biol 1998 ; 8 : 791 –7.

Wolff PH. Impaired temporal resolution in developmental dyslexia. [Review]. Ann NY Acad Sci 1993 ; 682 : 87 –103.

Wolff PH, Michel GF, Ovrut M, Drake C. Rate and timing precision of motor coordination in developmental dyslexia. Dev Psychol 1990 ; 26 : 349 –59.

Wright BA, Lombardino LJ, King WM, Puranik CS, Leonard CM, Merzenich MM. Deficits in auditory temporal and spectral resolution in language‐impaired children. Nature 1997 ; 387 : 176 –8.

Yap RL, van der Leij A. Testing the automatization deficit hypothesis of dyslexia via a dual‐task paradigm. J Learn Disabil 1994 ; 27 : 660 –5.

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• Diseases & Conditions

Dyslexia is a learning disorder that involves difficulty reading due to problems identifying speech sounds and learning how they relate to letters and words (decoding). Also called a reading disability, dyslexia is a result of individual differences in areas of the brain that process language.

Dyslexia is not due to problems with intelligence, hearing or vision. Most children with dyslexia can succeed in school with tutoring or a specialized education program. Emotional support also plays an important role.

Though there's no cure for dyslexia, early assessment and intervention result in the best outcome. Sometimes dyslexia goes undiagnosed for years and isn't recognized until adulthood, but it's never too late to seek help.

Signs of dyslexia can be difficult to recognize before your child enters school, but some early clues may indicate a problem. Once your child reaches school age, your child's teacher may be the first to notice a problem. Severity varies, but the condition often becomes apparent as a child starts learning to read.

Before school

Signs that a young child may be at risk of dyslexia include:

• Late talking
• Learning new words slowly
• Problems forming words correctly, such as reversing sounds in words or confusing words that sound alike
• Problems remembering or naming letters, numbers and colors
• Difficulty learning nursery rhymes or playing rhyming games

Once your child is in school, dyslexia symptoms may become more apparent, including:

• Reading well below the expected level for age
• Problems processing and understanding what is heard
• Difficulty finding the right word or forming answers to questions
• Problems remembering the sequence of things
• Difficulty seeing (and occasionally hearing) similarities and differences in letters and words
• Inability to sound out the pronunciation of an unfamiliar word
• Difficulty spelling
• Spending an unusually long time completing tasks that involve reading or writing
• Avoiding activities that involve reading

Teens and adults

Dyslexia signs in teens and adults are a lot like those in children. Some common dyslexia symptoms in teens and adults include:

• Difficulty reading, including reading aloud
• Slow and labor-intensive reading and writing
• Problems spelling
• Mispronouncing names or words, or problems retrieving words
• Difficulty summarizing a story
• Trouble learning a foreign language
• Difficulty doing math word problems

When to see a doctor

Though most children are ready to learn reading by kindergarten or first grade, children with dyslexia often have trouble learning to read by that time. Talk with your health care provider if your child's reading level is below what's expected for your child's age or if you notice other signs of dyslexia.

When dyslexia goes undiagnosed and untreated, childhood reading difficulties continue into adulthood.

There is a problem with information submitted for this request. Review/update the information highlighted below and resubmit the form.

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Dyslexia results from individual differences in the parts of the brain that enable reading. It tends to run in families. Dyslexia appears to be linked to certain genes that affect how the brain processes reading and language.

Risk factors

A family history of dyslexia or other reading or learning disabilities increases the risk of having dyslexia.

Complications

Dyslexia can lead to several problems, including:

• Trouble learning. Because reading is a skill basic to most other school subjects, a child with dyslexia is at a disadvantage in most classes and may have trouble keeping up with peers.
• Social problems. Left untreated, dyslexia may lead to low self-esteem, behavior problems, anxiety, aggression, and withdrawal from friends, parents and teachers.
• Problems as adults. The inability to read and comprehend can prevent children from reaching their potential as they grow up. This can have negative long-term educational, social and economic impacts.

Children who have dyslexia are at increased risk of having attention-deficit/hyperactivity disorder (ADHD), and vice versa. ADHD can cause difficulty keeping attention. It can also cause hyperactivity and impulsive behavior, which can make dyslexia harder to treat.

• Dyslexia. Merck Manual Professional Version. https://www.merckmanuals.com/professional/pediatrics/learning-and-developmental-disorders/dyslexia. Accessed April 6, 2022.
• Sutton Hamilton S. Reading difficulty in children: Clinical features and evaluation. https://www.uptodate.com/contents/search. Accessed April 6, 2022.
• Sutton Hamilton S. Reading difficulty in children: Interventions. https://www.uptodate.com/contents/search. Accessed April 6, 2022.
• Sanfilippo J, et al. Reintroducing dyslexia: Early identification and implications for pediatric practice. Pediatrics. 2020; doi:10.1542/peds.2019-3046.
• Hall C, et al. Current research informing the conceptualization, identification, and treatment of dyslexia across orthographies: An introduction to the special series. Learning Disability Quarterly. 2021; doi:10.1177/073194872092901.
• Specific learning disorder. In: Diagnostic and Statistical Manual of Mental Disorders DSM-5-TR. 5th ed. American Psychiatric Association; 2022. http://dsm.psychiatryonline.org. Accessed April 6, 2022.
• Dyslexia information page. National Institute of Neurological Disorders and Stroke. https://www.ninds.nih.gov/Disorders/All-Disorders/Dyslexia-Information-Page#disorders-r1. Accessed April 6, 2022.
• Information and resources for adolescents and adults with dyslexia ⸺ It's never too late. International Dyslexia Association. https://dyslexiaida.org/adolescents-and-adults-with-dyslexia/. Accessed April 6, 2022.
• Support: New to learning disabilities. Learning Disabilities Association of America. https://ldaamerica.org/support/new-to-ld/. Accessed April 6, 2022.
• Heubner AR (expert opinion). Mayo Clinic. June 6, 2022.
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Dyslexia sample case study

Case Study � �Katie� by Ashley Rutledge, NAU student The words just zoomed right by. They were taken in, processed, and filed away. Now on to the next sentence. And the next and the next and the next, never really pausing. Reading was something that came naturally, something that I�d been doing almost automatically since age 6. But for some people it wasn�t and isn�t so easy. Dyslexia and other severe reading disabilities are something very real, something 17 percent to 20 percent of children experience (National Center for Learning Disabilities, 1999).

Enter �Katie,� a 19-year old young woman who has been living with the effects of dyslexia since age 5, even though she was not formally diagnosed until age 17. Katie is from Tempe, Arizona where she has lived with her mother, father, and younger brother for her entire life. Both of her parents are teachers; her father teaches music at various grade levels and her mother is a librarian at a local high school. Katie�s younger brother is 14 and in the 8th grade. Katie graduated high school in the top of her class and is now an honors student at the college she attends.

Obviously then, Katie�s family places a large emphasis on education. However, she describes her family as being torn on the issue of grades and learning. �My dad was the one who cared about learning. He also has symptoms of dyslexia, even though he has never formally been diagnosed. So I think that�s where his emphasis on learning came from; he could understand. But my mom cared about the grades. I could have been held back a couple of times because I really wasn�t learning anything, I was just memorizing answers to get by, but she didn�t want me to because of the stigma of being held back.� Katie also described the extreme intelligence of her younger brother as being somewhat of an obstacle. She remembers being embarrassed about having to ask him how to spell words, even though he is 6 years younger. Fortunately though, her parents never compared the two children in terms of their academics. �I would have lost,� Katie says.

Looking back on her early education, Katie cringes. She has memories as early as preschool of not being able to understand the alphabet. �I just didn�t understand the concept of letters,� she says. �The order, the sounds, recognizing them on paper, the whole thing just confused me.� As a result, she cried frequently. And the older she got, the more frustrated she became. She says that she was �pretty much okay with the progress of things until [she] realized that [she] was way far behind everyone else. They all understood.� And she didn�t, and her self-worth plunged. Katie recollects that she just felt so stupid.

When asked about specific memories from her educational experience, Katie is quick to recall. �Second grade is a time that particularly stands out. My teacher, Mrs. Cates, had divided us into reading groups according to our current reading level. There was the smart group, the mediocre group, and the dumb group. I was obviously in the dumb group but by the end of the first week Mrs. Cates had kicked me out. She didn�t even bother to ask me why I was struggling or offer me any extra help. She just made me sit outside while everyone else read. It was like she didn�t even care. She had no patience, and looking back, she almost made it a point to ignore me and be negative towards me. She had the opportunity to diagnose me because of my obvious struggles but she didn�t. And I missed out.�

Regardless of such constant negative experiences, Katie looks back at her education with a smile. She has become a stronger person because of what she has faced, and ultimately, Katie feels that is so much more important than the people she had to deal with are. Indirectly, they taught her not to feel sorry for her self and to persevere. �But the good teachers were the ones who cared about me as a person first, and then worried about my disorder. They made me think, not just memorize facts so that I could move on at the end of the year.�

According to the International Dyslexia Association, individuals diagnosed with dyslexia are in need of a structured language program. They �require multi-sensory delivery of language content. Instruction that is multi-sensory employs all pathways of learning � at the same time, seeing, hearing, touching writing, and speaking.� In Katie�s case, seeing and hearing were the only two methods applied, which was not sufficient for her.

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• Published: 27 May 2024

Exploring brain plasticity in developmental dyslexia through implicit sequence learning

• Gaia Olivo   ORCID: orcid.org/0000-0002-7514-4493 1 , 2 ,
• Jonas Persson 2 , 3 &
• Martina Hedenius 4 , 5  

npj Science of Learning volume  9 , Article number:  37 ( 2024 ) Cite this article

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Developmental dyslexia (DD) is defined as difficulties in learning to read even with normal intelligence and adequate educational guidance. Deficits in implicit sequence learning (ISL) abilities have been reported in children with DD. We investigated brain plasticity in a group of 17 children with DD, compared with 18 typically developing (TD) children, after two sessions of training on a serial reaction time (SRT) task with a 24-h interval. Our outcome measures for the task were: a sequence-specific implicit learning measure (ISL), entailing implicit recognition and learning of sequential associations; and a general visuomotor skill learning measure (GSL). Gray matter volume (GMV) increased, and white matter volume (WMV) decreased from day 1 to day 2 in cerebellar areas regardless of group. A moderating effect of group was found on the correlation between WMV underlying the left precentral gyrus at day 2 and the change in ISL performance, suggesting the use of different underlying learning mechanisms in DD and TD children during the ISL task. Moreover, DD had larger WMV in the posterior thalamic radiation compared with TD, supporting previous reports of atypical development of this structure in DD. Further studies with larger sample sizes are warranted to validate these results.

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Introduction.

Developmental dyslexia (DD) is defined as experiencing difficulties in decoding written words, even in the absence of intellectual disability and with adequate educational guidance. DD has an estimated prevalence of 5–17%, with high variability stemming from different assessment methods, as well as linguistic and socio-cultural factors 1 . DD is neurobiological in origin, yet still little is known regarding the brain structural correlates of DD. Largely conflicting reports exist concerning neuroimaging findings of structural brain differences in dyslexic individuals compared with typically developing (TD) controls 2 , with the exception of consistently smaller total intracranial volumes in dyslexic compared with controls 2 . A recent meta-analysis suggested that the focus on different writing systems across studies may have partly played a role in these inconsistencies 3 . For example, DD showed smaller gray matter volume (GMV) compared with TD controls in temporoparietal, occipitotemporal, and cerebellar cortices in alphabetic languages 3 , 4 , while the GMV of the left inferior frontal gyrus was more affected in morpho-syllabic languages 3 . Alterations in the temporo-parietal junction, in particular, may be a potential marker of the risk for developing dyslexia in pre-readers 5 .

Alterations of the cerebellum and temporo-parietal structures can also be observed in the underlying white matter 6 , 7 , 8 . White matter abnormalities in DD, however, extend well beyond thalamocortical projections and the reading network 9 , 10 , encompassing also the limbic system and the motor system, especially the cerebellar fibers and corona radiata 9 . The arcuate fasciculus seems to be particularly relevant to the development of dyslexia 8 , 11 , 12 . This tract connects the temporal cortex and inferior parietal cortex to the frontal lobe, and is responsible for connecting Broca’s and Wernicke’s areas, as well as connecting the visual word form area to language related areas, such as the planum temporale 11 , 12 .

While most previous studies have focused on the processes subserving reading and writing abilities, other deficits have been observed in individuals with DD. For example, studies using the Serial Reaction Time (SRT) task 13 have reported implicit sequence learning deficits in children 14 , 15 , 16 , 17 and young adults with DD 18 , 19 , 20 , in contrast with well-preserved explicit learning abilities 15 , 21 , 22 .

The SRT task is a complex task encompassing several different cognitive functions, involving procedural learning and the acquisition and use of complex, sequence-based motor, perceptual and cognitive skills 23 . The task resembles a four-choice reaction time task. A visual cue appears at any of four positions on a computer screen. When a cue appears, the participant has to select the corresponding response button. Sequential trials, in which the cues follow repeating sequence of positions, are alternated with random trials, in which the cues don’t follow any repeating patterns of positions 24 . In addition to the implicit recognition and prediction of sequential associations (i.e., sequence-specific learning, henceforth ISL), the task also requires the integration of visual and motor responses, and fine movement control, that is, a more general visuomotor skill learning (henceforth GSL) 25 . ISL and GSL are subserved by partly different brain structures. Basal ganglia seem to sustain ISL, while additional structures are recruited for GSL, including the premotor cortex and cerebellum 25 .

The impairment in ISL observed in DD has been suggested to be task-dependent, and more evident when performing higher-order sequence learning tasks 14 , 18 , 19 , 21 , 26 . Moreover, it seems to be more evident after extended practice over repeated training sessions, during the consolidation phase and subsequent stages of learning 15 , 27 . Whether this may reflect potential hindrances in short-term plasticity of the involved brain structures is, however, yet to be explored, as no studies have been conducted so far to explore the neurobiological underpinnings and brain plasticity effects of repeated ISL practice in DD.

Recent studies employing repeated structural imaging of the adult human brain, in fact, have demonstrated that structural brain changes, measured as changes in estimated local GMV and WMV, can be induced by motor skill learning 28 , 29 . Such morphological brain changes can be detected already after a few days 30 , 31 or even minutes of practice 32 , 33 , and seem to be particularly prominent in the motor cortex 33 , 34 , 35 and cerebellar structures 33 , 36 , 37 , 38 .

The aim of the present study was therefore to examine potential atypicalities in short-term learning-related brain plasticity in DD by examining GMV and WMV changes related to ISL and GSL on the SRT task in a group of children with DD and TD control children. Recently, we examined the neural correlates of sequence-specific learning on the SRT task in children with DD compared to TD controls 39 . The study was performed over two days with a 24-h inter-session interval. We expected that SRT training would produce less brain plasticity changes in children with DD compared with TD children, mainly reflected by less pronounced post-training increases in GMV in relevant brain areas (suggestive of neurogenesis, synaptogenesis and production of non-neuronal supportive cells). We also expected the extent of WMV modifications, reflective of myelin changes, to be smaller in DD children compared with TD.

Behavioral measures

Behavioral results have been reported and described in detail previously 39 . Briefly, there was a trend for the DD group to have longer average response time on the task across sessions ( p  = 0.092). GSL was observed across both groups in the form of a significant reduction in reaction times from day 1 to day 2 ( p  < 0.001), but there were no group differences in GSL ( p  = 0.12). The DD group showed significantly less ISL at both days; however, the groups did not differ in terms of ISL change from day 1 to day 2 ( p  = 0.390; Table 1 , Fig. 1 ). In summary, performance differences between groups were specific to ISL, while no differences in GSL were observed.

The figure represents the mean implicit sequence learning (ISL; left panel) and general skill learning (GSL; right panel) scores at day 1 and day 2 in children with developmental dyslexia (DD) and typically developing children (TD). Overall, both groups improved on their ISL score with the same pattern; however, TD children consistently performed better than DD at both time-points. No statistically significant group differences were observed on the GSL score between DD and TD at either time-points. Error bars represents standard error of the mean (SEM).

Time and group difference on GMV and WMV

A statistically significant effect of time on GMV was found in the right cerebellar lobule 8, extending to the lobule 7b and adjacent crus, exhibiting increased GMV on day 2 compared with day 1 (p FWE-corr < 0.001; t = 4.68; Cohen’s d = 0.605; Table 2 , Fig. 2 ). No statistically significant effects of group or group × day interaction were detected, indicating that GMV changes over time occurred in both groups with a similar pattern.

The figure shows the clusters where statistically significant time and group effects were found on GMV and WMV. GMV (red cluster) was increased at day 2 compared with day 1 in the right cerebellar lobule VIII, while WMV was decreased at day 2 in the same region (purple cluster). Moreover, an effect of group was found on WMV underlying the left posterior thalamic radiation, with larger WMV in DD compared with TD (green cluster). DD developmental dyslexia, GMV gray matter volume, TD typically developing, WMV white matter volume.

WMV was, on the other hand, reduced on day 2 compared with day 1 (p FWE-corr = 0.001, t = 4.98, Cohen’s d = 0.617) in the right cerebellum, particularly in the right lobule 8. A main effect of group was also detected, with DD having greater WMV in the left posterior thalamic radiation and inferior longitudinal fasciculus (ILF) compared with TD, particularly in the cuneus (p FWE-corr = 0.004; t = 4.97; Cohen’s d = 1.404). No effect of the group × day interaction on WMV was found.

Correlations between ISL, GSL, and imaging measures

No correlations between the change in GMV and WMV from day 1 to day 2, and ISL or GSL change, were detected. However, an effect of the group × ISL change interaction, indicating a moderating effect of group on the association between ISL change and WMV, was found on the WMV at day 2 in a cluster of 744 voxels underlying the left precentral gyrus (p FWE-corr = 0.006; F = 32.13) (Fig. 3A ). In particular, while a negative correlation was present in the TD group ( p  = 0.034; r = −0.501), no correlation was observed in the DD group ( p  = 0.364, r = 0.235) (Fig. 3B ). No correlations between GMV at day 2 and ISL change were detected, nor any effect of the group × ISL change interaction. No correlations between either GMV or WMV at day 2 and GSL were detected, nor any effect of the group × GSL change interaction.

A Shows the clusters where statistically significant correlations were found between WMV at day 2 and ISL change (yellow cluster). The scatterplot in B shows the moderating effect of group on the correlation between WMV and ISL change. While a negative correlation was present with WMV of the left precentral gyrus at day 2 in TD, no statistically significant correlation was detected in DD. DD developmental dyslexia, ISL implicit sequence(-specific) learning, TD typically developing children, WMV white matter volume.

We investigated brain plasticity in a group of children with DD, compared with TD children, after two sessions of training on a SRT task with a 24-h interval. GMV was increased at day 2 in the right cerebellar lobules 8 and 7b in the whole sample, indicating that GMV changes over time occurred in both groups with a similar pattern. WMV in the same areas was, on the other hand, reduced on day 2 compared with day 1. Moreover, DD had larger WMV in the left posterior thalamic radiation and ILF compared with TD, at both days. Furthermore, WMV underlying the left precentral gyrus at day 2 showed a positive correlation with ISL change in DD, but a negative correlation in TD.

The cerebellum is part of a larger fronto-striatal-cerebellar circuitry recruited during ISL tasks 18 . Within this network, the cerebellum has been suggested to subserve general visuomotor abilities 25 . The increase in GMV in the cerebellum from day 1 to day 2 observed in our sample is consistent with current models of skill learning, postulating the occurrence of plasticity phenomena involving neurogenesis and gliogenesis 40 . Learning-related GMV increases have been, in fact, ascribed to the interplay of different mechanisms, primarily changes in synapse density and dendritic spine morphology, and proliferation of neurons and non-neuronal cells 40 . The latter include proliferation of astrocytes, involved in supporting synaptic function, ion homeostasis, neuronal energy expense and regulating blood flow in response to neuronal activity; and microglia, which plays a role in supporting structural and functional plasticity of synapses and dendrites during both development and learning 40 . The reduction in WMV observed in our sample following skill learning is, on the other hand, of more difficult interpretation 40 , 41 . Plasticity changes in white matter may stem from variation in myelination, axon diameter, fiber density, or fiber geometry 40 . Most of the studies focusing on white matter plasticity during skill learning focused on microstructural diffusivity properties rather than volume 41 , leading to somewhat conflicting findings. In adults, several studies have reported increased fractional anisotropy or decreased mean diffusivity (suggestive of increased myelin volume or increased packing density of the myelin fibers) over time after motor skill learning; however, other equally valid studies have reported opposite results (for a review, see ref. 41 ). For example, training on a whole-body balancing task can induce decreases in fractional anisotropy 42 , and possibly a reduction in WMV 41 . Current evidence suggests that white matter plasticity processes following skill learning can result in both increases and decreases in WMV, such as the WMV decrease observed in our sample, depending on the duration of the intervention and the type of skill learning protocol employed 41 . The lack of group differences in GMV and WMV of this area is, on the other hand, consistent with the observation that GSL occurred to similar extent in both groups in our sample. It is worth noting, nonetheless, that alterations in cerebellar functions have been postulated to be driving the automaticity deficits observed in children with DD 43 , 44 , 45 . The cerebellum is, in fact, activated in healthy individuals both when executing a previously learned (automatic) sequence of finger presses, as well as when learning a new sequence of presses by trial and error 46 . People with DD, however, show significantly less cerebellar activation in both conditions 43 , along with impaired implicit motor learning compared with controls 47 . Moreover, developmental differences in cerebellar asymmetry and gray matter volume have been reported in children with DD 47 , 48 . However, whether the changes in cerebellar GMV and WMV in our sample were primarily driven by the improvement on general, sequence-independent visuomotor learning rather than by implicit skill learning, is difficult to interpret. Further studies including a control group performing a visuomotor task with no sequence learning involved are warranted to disentangle the effects of sequence-independent and sequence-dependent learning in the cerebellum.

The volume of the white matter underlying the left precentral gyrus at day 2 showed a trend for a positive correlation with ISL change in DD, but a negative correlation in TD. A pattern of correlations similar to ours has been previously reported in a study investigating cerebral blood flow during a SRT task in adults with schizophrenia 49 ; patients showed a positive correlation between cerebral blood flow in the premotor areas (including the precentral gyrus) and learning, while a negative correlation was found in controls 49 . Functional dynamic connectivity between the precentral gyrus and the dorsal caudate has also been previously reported to be negatively correlated with performance improvement on an implicit probabilistic sequence learning (IPSL) task in adults 50 , such that participants with greater connectivity between these regions showed less improvement 50 . The different patterns of correlation suggest that different strategies are employed by the two groups to achieve performance improvement on the ISL task 49 . A lower baseline score on the ISL task may reflect a reduced ability, in the DD group, to detect a sequence (signal) that is embedded in a stream of random noise 49 . A reduced ability to infer abstract relationships between events, and to predict the occurrence of specific events from context may lead these children to rely more on explicit, stimulus-driven strategies to perform the task 49 , 51 . Interestingly, the right precentral gyrus has also been speculated to be involved in the pathogenesis of dyslexia independently of the disordered reading experience, as reduced GMV in this area has been reported in dyslexic children compared with age-matched controls, but also with younger controls matched on reading abilities 52 .

In our sample, however, an effect of group was only found on WMV in the left posterior thalamic radiation (extending to the ILF), which was larger in DD compared with TD, at both assessment days. The posterior part of the thalamic radiation connects the caudal parts of the thalamus with the parietal and occipital lobes via the posterior thalamic peduncle and posterior limb of the internal capsule (PLIC). A previous study performed in young adults has reported alterations in the posterior part of the thalamic radiation in a sample of young adults with DD, though pointing toward a reduction of the structural connectivity of this tract in young adults with DD compared with TD controls 53 , in contrast with our findings. This discrepancy may be driven by the different age range of the participants recruited in our study. In TD children, in fact, the PLIC, encompassing the posterior thalamic radiation, shows age-related increases in myelination and/or axonal density, reflected by increases in fractional anisotropy (FA) and decreased in apparent diffusion coefficient (ADC) 7 , 54 . This pattern is accentuated in children with DD, who show even higher FA values and lower ADC values than TD children up until 11 years of age 7 , followed by a decrease to control levels. The apparent diffusivity coefficient of the PLIC has also been reported to correlate with writing abilities in children with DD 55 . However, conflicting findings exist, as other studies have reported reduced FA in this tract in children with spelling difficulties compared with controls 56 , and higher FA in this tract has been related to better reading abilities 57 , 58 . Reading abilities have also been related to structural integrity of the ILF 59 , 60 , 61 , though opposite findings of a correlation between reduced FA in the ILF and better reading abilities in TD children who are just beginning to read have also been reported 62 . Future studies with larger sample size, allowing an age-based stratification of the children, might shed light on the involvement of the PLIC in dyslexia, and on the specific deviations from the normal white matter development occurring in this period of complex brain development.

The present study has limitations that have to be acknowledged. The sample size was relatively small, calling for further studies with larger sample sizes to validate our findings. Moreover, a total of 26/35 children (DD, n  = 13/17; TD, n  = 13/18) included in this study had participated in a previous sequence learning study using the alternating serial reaction time paradigm (Hedenius et al. 27 ), and were therefore not naïve to the study task. This may have potentially limited the power to detect differences in structural brain plasticity patterns between DD and TD. It must also be mentioned that the test-retest reliability of the SRT task has been argued to be sub-optimal 23 ; however, this issue impacts primarily longitudinal measures of individual differences across sessions, potentially leading to the detection of false positive improvements on the task 23 . This may have limited the power for finding correlations between changes in imaging measures and change on the task performance. On the other hand, the reliability seems to be appropriate from within-session group comparison 23 . Therefore, our finding of consistently lower within-session ISL in the DD group, compared to TD, at both days, is unlikely to be driven by the low test-retest reliability of the task. In our study, the test-retest reliability of the SRT measures was 0.6 (moderate reliability). Nonetheless, the observed improvement in performance observed from day 1 to day 2 across groups, needs to be interpreted with caution. Similar considerations must also be made concerning the lack of a non-experimental control group of children (i.e., children scanned 24 h apart without SRT training), which may raise concerns relative to the impact of measurement error on MRI findings. While intra-scanner test-retest reliability of brain volumetric measurements has been reported to be excellent in clinical and non-clinical populations 63 , 64 , 65 , future studies including a non-experimental group should be performed to account for repeated MRI measurement error.

In sum, we investigated brain plasticity in a group of children with DD, compared with TD children, after two sessions of training on a SRT task with a 24-h interval. GMV was increased, while WMV was reduced in the right cerebellar lobules 8 and 7b at day 2 compared with day 1 in the whole sample, suggesting the occurrence of plasticity phenomena induced by ISL practice. However, longitudinal studies are needed to evaluate the time-course and maintenance of these plasticity changes. WMV underlying the left precentral gyrus at day 2 showed a trend for a positive correlation with ISL change in DD, but a negative correlation in TD, pointing toward the use of different underlying learning mechanisms in DD and TD children for solving the ISL task, with the DD group potentially relying more on explicit, stimulus-driven strategies to compensate for a reduced ability for implicit learning. Moreover, DD had larger WMV in the left posterior thalamic radiation and ILF compared with TD, supporting previous reports of atypical development of this structure in children with DD. However, further studies with larger sample sizes, and with subjects naïve to the task to be performed, are warranted to validate and generalize these results.

Participants

The participating children (DD n  = 17; TD n  = 18) were all part of a larger behavioral study focusing on learning and memory in children with reading difficulties (The REMEMBR project), and the sample and behavioral paradigm have been described in detail in previous reports from this project 27 , 39 . The study was conducted in accordance with the Declaration of Helsinki, and was approved by the ethical review board of Uppsala, Sweden. All parents or legal guardians gave written informed consent, and all children provided written assent to participate in the study.

The groups did not differ with respect to age (9–13 years), sex, performance IQ (PIQ), or a language composite score based on vocabulary and syntactic comprehension (Table 3 ). Significant group differences were observed in word reading, reading fluency, spelling, and phoneme awareness (see ref. 39 ). All children were mono-lingual Swedish speaking, with equivalent exposure to English as a second language in school.

Children with DD were recruited from speech and language therapy clinics in the Stockholm-Uppsala area in Sweden. Inclusion criteria were: a clinical diagnosis of DD from a certified speech and language therapist, and a word reading score <15th percentile on a standardized Swedish word reading test 66 . Exclusion criteria for the DD group were PIQ scores <80 67 , any other known comorbid neuropsychiatric condition (as reported by parents) and a language composite stanine score <3. As previously described in Hedenius and Persson 39 , the language composite score was derived from the vocabulary subtest from the DLS 68 , and the Swedish version of the Test for Reception of Grammar – 2 (TROG – 2) 69 . These DD inclusion/exclusion criteria are consistent with the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) 70 as well as with previously published studies on DD (e.g., ref. 71 ).

TD children were recruited from schools in the same area. Inclusion criteria for the TD group were normal language, reading and writing development as reported by parents. Exclusion criteria were any known neurodevelopmental condition (as reported by parents), PIQ scores <80 67 , word reading, non-word reading, or spelling scores 66 <the 20th percentile, or a language composite stanine score <3.

Possible unrecognized ADHD was ruled out using the executive functions subdomain in the Five-to-Fifteen (FTF) parent questionnaire 72 . The FTF targets ADHD symptoms, and its common co-morbidities, in children and adolescents between 5 and 15 years of age. It has been shown to be a reliable and valid screening instrument that correlates significantly with other ADHD questionnaires, as well as performance-based measures 73 , 74 . No child in the sample had significant ADHD symptoms.

Implicit sequence learning

Four squares were presented horizontally in the center of a computer screen. Each square position corresponded to one of four buttons, in order from left to right. Participants were instructed to press the corresponding button using the index and middle finger of each hand as quickly and accurately as possible when a white square turned gray (Fig. 4A ). Behavioral data on the ISL were collected during fMRI scanning. Response accuracy and reaction times (RT) were recorded with two MRI-compatible response boxes, one for each hand. Button presses were recorded using E-prime 2.0 75 . The task was administered in two sessions on two separate days (day 1 and day 2), with a 24-h inter-session-interval (Fig. 1C ). Each session included 24 blocks. Each block consisted of 36 trials, and each trial lasted 700 milliseconds (ms) with a 300 ms inter-stimulus interval. In half of the blocks, and unknown to the participants, the trials followed a fixed second-order 12-item sequence with positions from left (1) to right (4) of 1–2–1–4–2–3–4–1–3–2-4–3 76 . In the remaining blocks, trials were presented in a pseudo-random order with the constraint that two consecutive trials were not the same. Sequence and random blocks were alternated, and each block was separated by a 17-second fixation period (Fig. 1B ). Error trials or omissions were excluded from analysis and median response times were used to minimize the influence of outlier responses.

The figure provides a schematic overview of ( A ) the serial reaction time task used in the study; B the study design; C the collection timeline across the two days. The practice session consisted of 10 min of performance on the ISL. Behavioral data were included from the two in-scanner sessions at day 1 and 2.

MRI acquisition

Structural images were acquired 24 h apart on Discovery TM MR750 3.0 Tesla scanner, with a 32-channel phased array receiving head coil (General Electric). T1-weighted 3D spoiled gradient recalled (SPGR) images were acquired, with 0.94 × 0.94 × 1 mm 3 voxel size (TR: 7.908 ms, TE: 3.06 ms, field of view: 24 cm, 176 axial slices, flip angle of 12).

Preprocessing of MR images

Preprocessing of structural images was performed with CAT12 ( https://neuro-jena.github.io/cat/ ), with the longitudinal processing pipeline optimized for the detection of subtler changes in response to short-term plasticity effects ( https://neuro-jena.github.io/cat12-help/ ). Prior to applying the CAT12 longitudinal pipeline, customized tissue probability maps were created for our pediatric sample. To this purpose, an initial segmentation of the images was performed using Statistical Parametric Mapping 12 (SPM12) ( https://www.fil.ion.ucl.ac.uk/spm/software/download/ ), running on Matlab 2022b. The segmentation maps were then fed to the Template-O-Matic (TOM8) toolbox ( https://neuro-jena.github.io/software.html#tom ), for the creation of the customized template. An average template was fitted from the segmented tissue maps, including age (modeled with a third order, cubic regression) and gender as regressors. The customized tissue probability maps generated with TOM8 were used for the longitudinal segmentation pipeline implemented in CAT 12.

In brief, the CAT 12 longitudinal pipeline consists of a preliminary inverse-consistent rigid-body registrations to realign all images for each participant, followed by the application of intra-subject bias-field corrections. The images are then segmented individually into gray matter (GM), white matter and cerebrospinal fluid. The customized tissue probability maps obtained with TOM 8 were used for segmentation. For each participant, a mean spatial transformation for all time-points is then calculated for spatial registration to the standard Montreal Neurological Institute (MNI) brain template. These mean deformations are then applied to individual images, to obtain normalization to the MNI space. Finally, smoothing with a 6 mm FWHM Gaussian kernel was applied.

Data quality was checked by estimating sample homogeneity measures. Data that deviate from the sample increase variance and can negatively affect statistical power. The mean Z-score measures the homogeneity of the final data, reflecting the quality of the images after preprocessing; a low Z-score reflects poor data quality. The image quality rating (IQR), on the other hand, combines measurements of noise and spatial resolution of the images before pre-processing. The product between IQR * mean Z-scores was used to evaluate data quality, as recommended by CAT 12 manual. Three participants were flagged based on the IQR*mean Z-score product; two participants had low Z-score (deviation from the sample after pre-processing), while one participant had high IQR (deviation from the sample before pre-processing). Visually inspecting the data for the presence of artifacts or low image quality is recommended in such cases, to confirm whether the subject is an outlier. If no artifacts are detected and the image quality is appropriate, the data can be retained ( https://neuro-jena.github.io/cat12-help/#module4 ). Visual inspection of the un-preprocessed and pre-processed data did not show any artifacts or issues with data quality; therefore, the data were retained in the analysis. Nonetheless, all analyses were also performed without these potential outliers, leading to the same results.

Statistical analysis

Statistical analysis of behavioral data was performed using Statistical Package for Social Sciences (SPSS). For each participant, we calculated the median RT for the random and sequence blocks of each session, separately. General skill learning (GSL) was defined as the sequence-independent RT decrease from day 1 to day 2, and was calculated from the average RT across sequence and random trials. Sequence-specific learning (ISL) was operationalized as the median RT difference, for each session, between random and sequence blocks. Because longer average response times will lead to numerically larger differences (and thus erroneously to more “learning” in the slower group) for both the GSL and ISL measures, a RT normalizing procedure was used. The GSL measure was normalized by dividing the RT difference between day 1 and day 2 with the average RT across both days. For the ISL measure, we followed the procedure outlined in Hedenius et al. 77 to calculate a normalized sequence learning measure. This measure was obtained by dividing the difference between the median RTs for the random and sequence blocks, in each session, by the average median RT across both random and sequence blocks, for that same session (i.e. (median RT for random blocks in session X - median RT for sequence blocks in session X)/((median RT for random blocks in session X + median RT for sequence blocks in session X)/2). For both measures, larger numbers reflect more learning.

The following variables were used as behavioral outcome measures for the SRT task: (1) the difference in ISL between day 1 and day 2 (henceforth ISL), reflecting the amount of sequence-specific learning from day 1 to day 2; (2) the change in the average RT across sequence and random trials between day 1 and day 2 (henceforth GSL), reflecting the amount of general skill learning from day 1 to day 2. Behavioral measures were tested for normality of distribution with the Shapiro Wilk’s test for normality. No outliers were detected in either measurement. Separate ANOVA tests were used to test for between-groups differences in baseline ISL and GSL, ISL and GSL changes from day 1 to day 2. The threshold for significance was set at p  < 0.05.

Statistical analysis of the imaging data was performed with CAT12. A flexible factorial design for longitudinal data was used. The flexible factorial model allows for mixed-model specification, with group set as between-subject factor (two levels: DD, TD), and time set as within-subject factor (two levels: day 1, day 2). Main effects of time and group, and the effect of the group × day interaction, were tested. A preliminary uncorrected threshold of p  < 0.001 was applied. Voxels surviving such threshold were further corrected for family-wise error (FWE) rate at cluster level with a threshold of p  < 0.05. The same analyses were performed on gray matter volume (GMV) and white matter volume (WMV).

GMV and WMV change from day 1 to day 2, and GMV and WMV at day 2 were tested for correlations with the ISL change from day 1 to day 2 (reflective of sequence-specific learning), and with GSL (reflective of visuomotor skill learning over training sessions). Voxel-wise, whole-brain correlation analyses were carried out in CAT12. Group was included as a potential moderator for the correlation between imaging and behavioral measures in all analyses, by testing for an interaction effect of group × ISL change, and group × GSL. A preliminary uncorrected threshold of p  < 0.001 was applied. Voxels surviving such threshold were further corrected for family-wise error (FWE) rate at cluster level with a threshold of p  < 0.05.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are available on request. The data are not publicly available due to GDPR restrictions. Data can be requested to J.P. A Data Use Agreement will be requested.

Habib, M. & Giraud, K. Dyslexia. Handb. Clin. Neurol. 111 , 229–235 (2013).

Article   PubMed   Google Scholar  

Ramus, F., Altarelli, I., Jednorog, K., Zhao, J. & Scotto di Covella, L. Neuroanatomy of developmental dyslexia: Pitfalls and promise. Neurosci. Biobehav. Rev. 84 , 434–452 (2018).

Yan, X. et al. Convergent and divergent brain structural and functional abnormalities associated with developmental dyslexia. Elife 10 , https://doi.org/10.7554/eLife.69523 (2021).

Linkersdorfer, J., Lonnemann, J., Lindberg, S., Hasselhorn, M. & Fiebach, C. J. Grey matter alterations co-localize with functional abnormalities in developmental dyslexia: an ALE meta-analysis. PLoS One 7 , e43122 (2012).

Article   PubMed   PubMed Central   Google Scholar  

Vandermosten, M., Hoeft, F. & Norton, E. S. Integrating MRI brain imaging studies of pre-reading children with current theories of developmental dyslexia: A review and quantitative meta-analysis. Curr. Opin. Behav. Sci. 10 , 155–161 (2016).

Steinbrink, C. et al. The contribution of white and gray matter differences to developmental dyslexia: insights from DTI and VBM at 3.0 T. Neuropsychologia 46 , 3170–3178 (2008).

Article   CAS   PubMed   Google Scholar  

Rollins, N. K. et al. Simple developmental dyslexia in children: alterations in diffusion-tensor metrics of white matter tracts at 3 T. Radiology 251 , 882–891 (2009).

Hoeft, F. et al. Neural systems predicting long-term outcome in dyslexia. Proc. Natl Acad. Sci. USA 108 , 361–366 (2011).

Cui, Z., Xia, Z., Su, M., Shu, H. & Gong, G. Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach. Hum. Brain Mapp. 37 , 1443–1458 (2016).

Lou, C. et al. White matter network connectivity deficits in developmental dyslexia. Hum. Brain Mapp. 40 , 505–516 (2019).

Vanderauwera, J., Wouters, J., Vandermosten, M. & Ghesquiere, P. Early dynamics of white matter deficits in children developing dyslexia. Dev. Cogn. Neurosci. 27 , 69–77 (2017).

Silani, G. et al. Brain abnormalities underlying altered activation in dyslexia: a voxel based morphometry study. Brain 128 , 2453–2461 (2005).

Nissen, M. J. & Bullemer, P. Attentional requirements of learning: Evidence from performance measures. Cogn. Psychol. 19 , 1–32 (1987).

Article   Google Scholar  

Deroost, N. et al. Intact first- and second-order implicit sequence learning in secondary-school-aged children with developmental dyslexia. J. Clin. Exp. Neuropsychol. 32 , 561–572 (2010).

Hedenius, M. et al. Impaired implicit sequence learning in children with developmental dyslexia. Res. Dev. Disabil. 34 , 3924–3935 (2013).

Inacio, F. et al. Implicit sequence learning is preserved in dyslexic children. Ann. Dyslexia 68 , 1–14 (2018).

Menghini, D. et al. Structural correlates of implicit learning deficits in subjects with developmental dyslexia. Ann. N. Y. Acad. Sci. 1145 , 212–221 (2008).

Howard, J. H. Jr., Howard, D. V., Japikse, K. C. & Eden, G. F. Dyslexics are impaired on implicit higher-order sequence learning, but not on implicit spatial context learning. Neuropsychologia 44 , 1131–1144 (2006).

Du, W. & Kelly, S. W. Implicit sequence learning in dyslexia: a within-sequence comparison of first- and higher-order information. Ann. Dyslexia 63 , 154–170 (2013).

Lum, J. A., Ullman, M. T. & Conti-Ramsden, G. Procedural learning is impaired in dyslexia: evidence from a meta-analysis of serial reaction time studies. Res. Dev. Disabil. 34 , 3460–3476 (2013).

Jimenez-Fernandez, G., Vaquero, J. M., Jimenez, L. & Defior, S. Dyslexic children show deficits in implicit sequence learning, but not in explicit sequence learning or contextual cueing. Ann. Dyslexia 61 , 85–110 (2011).

Hedenius, M., Ullman, M. T., Alm, P., Jennische, M. & Persson, J. Enhanced recognition memory after incidental encoding in children with developmental dyslexia. PLoS One 8 , e63998 (2013).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Oliveira, C. M., Hayiou-Thomas, M. E. & Henderson, L. M. The reliability of the serial reaction time task: meta-analysis of test-retest correlations. R. Soc. Open Sci. 10 , 221542 (2023).

Robertson, E. M. The serial reaction time task: implicit motor skill learning? J. Neurosci. 27 , 10073–10075 (2007).

Janacsek, K. et al. Sequence learning in the human brain: A functional neuroanatomical meta-analysis of serial reaction time studies. Neuroimage 207 , 116387 (2020).

Folia, V. et al. Implicit learning and dyslexia. Ann. N. Y. Acad. Sci. 1145 , 132–150 (2008).

Hedenius, M., Lum, J. A. G. & Bolte, S. Alterations of procedural memory consolidation in children with developmental dyslexia. Neuropsychology 35 , 185–196 (2021).

Draganski, B. et al. Neuroplasticity: changes in grey matter induced by training. Nature 427 , 311–312 (2004).

Wenger, E. et al. Repeated Structural Imaging Reveals Nonlinear Progression of Experience-Dependent Volume Changes in Human Motor Cortex. Cereb. Cortex 27 , 2911–2925 (2017).

PubMed   Google Scholar  

Kwok, V. et al. Learning new color names produces rapid increase in gray matter in the intact adult human cortex. Proc. Natl Acad. Sci. USA 108 , 6686–6688 (2011).

Irmen, F. et al. Functional and Structural Plasticity Co-express in a Left Premotor Region During Early Bimanual Skill Learning. Front. Hum. Neurosci. 14 , 310 (2020).

Månsson, K. N. T. et al. Viewing Pictures Triggers Rapid Morphological Enlargement in the Human Visual Cortex. Cereb. Cortex 30 , 851–857 (2020).

Olivo, G. et al. Estimated gray matter volume rapidly changes after a short motor task. Cereb. Cortex 32 , 4356–4369 (2022).

Taubert, M., Mehnert, J., Pleger, B. & Villringer, A. Rapid and specific gray matter changes in M1 induced by balance training. Neuroimage 133 , 399–407 (2016).

Sanes, J. N. & Donoghue, J. P. Plasticity and primary motor cortex. Annu. Rev. Neurosci. 23 , 393–415 (2000).

Nguyen-Vu, T. D. et al. Cerebellar Purkinje cell activity drives motor learning. Nat. Neurosci. 16 , 1734–1736 (2013).

Lee, K. H. et al. Circuit mechanisms underlying motor memory formation in the cerebellum. Neuron 86 , 529–540 (2015).

De Zeeuw, C. I. & Ten Brinke, M. M. Motor Learning and the Cerebellum. Cold Spring Harb. Perspect. Biol. 7 , a021683 (2015).

Hedenius, M. & Persson, J. Neural correlates of sequence learning in children with developmental dyslexia. Hum. Brain Mapp. 43 , 3559–3576 (2022).

Zatorre, R. J., Fields, R. D. & Johansen-Berg, H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat. Neurosci. 15 , 528–536 (2012).

Sampaio-Baptista, C. & Johansen-Berg, H. White Matter Plasticity in the Adult Brain. Neuron 96 , 1239–1251 (2017).

Taubert, M. et al. Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections. J. Neurosci. 30 , 11670–11677 (2010).

Nicolson, R. I., Fawcett, A. J. & Dean, P. Developmental dyslexia: the cerebellar deficit hypothesis. Trends Neurosci. 24 , 508–511 (2001).

Nicolson, R. I. & Fawcett, A. J. Developmental dyslexia, learning and the cerebellum. J. Neural Transm. Suppl . 69 , 19–36 (2005).

Stoodley, C. J. The Cerebellum and Neurodevelopmental Disorders. Cerebellum 15 , 34–37 (2016).

Jenkins, I. H., Brooks, D. J., Nixon, P. D., Frackowiak, R. S. & Passingham, R. E. Motor sequence learning: a study with positron emission tomography. J. Neurosci. 14 , 3775–3790 (1994).

Stoodley, C. J. & Stein, J. F. Cerebellar function in developmental dyslexia. Cerebellum 12 , 267–276 (2013).

Stoodley, C. J. Distinct regions of the cerebellum show gray matter decreases in autism, ADHD, and developmental dyslexia. Front. Syst. Neurosci. 8 , 92 (2014).

Marvel, C. L. et al. The neural correlates of implicit sequence learning in schizophrenia. Neuropsychology 21 , 761–777 (2007).

Stillman, C. M. et al. Caudate resting connectivity predicts implicit probabilistic sequence learning. Brain Connect. 3 , 601–610 (2013).

Ullman, M. T. & Pullman, M. Y. A compensatory role for declarative memory in neurodevelopmental disorders. Neurosci. Biobehav. Rev. 51 , 205–222 (2015).

Krafnick, A. J., Flowers, D. L., Luetje, M. M., Napoliello, E. M. & Eden, G. F. An investigation into the origin of anatomical differences in dyslexia. J. Neurosci. 34 , 901–908 (2014).

Muller-Axt, C., Anwander, A. & von Kriegstein, K. Altered Structural Connectivity of the Left Visual Thalamus in Developmental Dyslexia. Curr. Biol. 27 , 3692–3698 e3694 (2017).

Wilde, E. A. et al. Diffusion tensor imaging of hemispheric asymmetries in the developing brain. J. Clin. Exp. Neuropsychol. 31 , 205–218 (2009).

El-Sady, S. et al. Correlation between diffusion tensor imaging measures and the reading and cognitive performance of Arabic readers: dyslexic children perspective. Neuroradiology 62 , 525–531 (2020).

Gebauer, D. et al. Differences in integrity of white matter and changes with training in spelling impaired children: a diffusion tensor imaging study. Brain Struct. Funct. 217 , 747–760 (2012).

Beaulieu, C. et al. Imaging brain connectivity in children with diverse reading ability. Neuroimage 25 , 1266–1271 (2005).

Niogi, S. N. & McCandliss, B. D. Left lateralized white matter microstructure accounts for individual differences in reading ability and disability. Neuropsychologia 44 , 2178–2188 (2006).

D’Mello, A. M. & Gabrieli, J. D. E. Cognitive Neuroscience of Dyslexia. Lang. Speech Hear Serv. Sch. 49 , 798–809 (2018).

Elnakib, A. et al. Magnetic resonance imaging findings for dyslexia: a review. J. Biomed. Nanotechnol. 10 , 2778–2805 (2014).

Sun, Y. F., Lee, J. S. & Kirby, R. Brain imaging findings in dyslexia. Pediatr. Neonatol. 51 , 89–96 (2010).

Zaric, G. et al. Atypical White Matter Connectivity in Dyslexic Readers of a Fairly Transparent Orthography. Front. Psychol. 9 , 1147 (2018).

Hedges, E. et al. M154. Intra- and inter-scanner reliability of gray matter volume and cortical thickness estimates: implications for multicentre imaging studies in psychosis. Schizophrenia Bull. 46 , https://doi.org/10.1093/schbul/sbaa030.466 (2020).

Wittens, M. M. J. et al. Inter- and Intra-Scanner Variability of Automated Brain Volumetry on Three Magnetic Resonance Imaging Systems in Alzheimer’s Disease and Controls. Front. Aging Neurosci. 13 , 746982 (2021).

Jovicich, J. et al. MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 46 , 177–192 (2009).

Elwér, Å., Fridolfsson, I., Samuelsson, S. & Wiklund, C. LäSt: Test i läsförståelse, läsning och stavning (Hogrefe förlag, 2011).

Wechsler, D. Wechsler intelligence scale for children , 4th ed. (WISC-IV) (Pearson Assessments, 2004).

Järpsten, B. & Taube, K. DLS : för skolår 4-6. Handledning. 131 (Hogrefe Psykologiförlaget, 2010).

Bishop, D. Test for reception of grammar, version 2 (TROG-2). Swedish version., (Pearson Assessment, 2009).

American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5th ed.). (2013).

Knoop-van Campen, C. A. N., Segers, E. & Verhoeven, L. The modality and redundancy effects in multimedia learning in children with dyslexia. Dyslexia 24 , 140–155 (2018).

Kadesjo, B. et al. The FTF (Five to Fifteen): the development of a parent questionnaire for the assessment of ADHD and comorbid conditions. Eur. Child Adolesc. Psychiatry 13 , 3–13 (2004).

Bohlin, G. & Janols, L. O. Behavioural problems and psychiatric symptoms in 5-13 year-old Swedish children-a comparison of parent ratings on the FTF (Five to Fifteen) with the ratings on CBCL (Child Behavior Checklist). Eur. Child Adolesc. Psychiatry 13 , 14–22 (2004).

Lind, A. et al. Relations between brain volumes, neuropsychological assessment and parental questionnaire in prematurely born children. Eur. Child Adolesc. Psychiatry 19 , 407–417 (2010).

Schneider, W., Eschman, A., & Zuccolotto, A. E-Prime (Version 2.0) (Psychology Software Tools Inc., Pittsburgh, PA, 2002).

Schendan, H. E., Searl, M. M., Melrose, R. J. & Stern, C. E. An FMRI study of the role of the medial temporal lobe in implicit and explicit sequence learning. Neuron 37 , 1013–1025 (2003).

Hedenius, M. et al. Grammar predicts procedural learning and consolidation deficits in children with Specific Language Impairment. Res. Dev. Disabil. 32 , 2362–2375 (2011).

Joel, A & Berggren, M. Paulin test av fonologisk medvetenhet: en normering samt utvärdering avtestet och faktorer som kan påverka deltagarnas resultat (2013).

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Acknowledgements

This study was funded by the Foundation Sunnerdahl Disability Fund, the Promobilia Foundation, the Royal Swedish Academy of Sciences (Lennart “Aktiestinsen” Israelssons stipendiefond), and the Sven Jerring Foundation.

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G.O. contributed to imaging data analysis, results interpretation, and writing the original draft; J.P. and M.H. contributed to the conceptualization and design of the study, to the analysis of behavioral data, and to results interpretation. All authors contributed to critically revise the manuscript for important intellectual content and approved the final version of this manuscript.

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Olivo, G., Persson, J. & Hedenius, M. Exploring brain plasticity in developmental dyslexia through implicit sequence learning. npj Sci. Learn. 9 , 37 (2024). https://doi.org/10.1038/s41539-024-00250-w

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Dyslexia Demystified: Understanding and Addressing Challenges in Reading

Dyslexia is a common learning difficulty that affects the way the brain processes written and spoken language. Despite its prevalence, there remains a significant need for greater dyslexia awareness. At Neuhaus Education Center, we are dedicated to providing effective dyslexia support and educational strategies to help every child overcome barriers to reading.

Dyslexia is characterized by difficulties with accurate and/or fluent word recognition, poor spelling, and decoding abilities. These challenges are often unexpected in relation to other cognitive abilities and the provision of effective classroom instruction. It is important to understand that dyslexia is not due to a lack of intelligence or desire to learn; with the right support, individuals with dyslexia can achieve great success.

Children with dyslexia often experience frustration and diminished confidence as they encounter reading difficulties. These challenges can affect a child’s ability to perform in school and impact their everyday activities and social interactions. Recognizing the signs of dyslexia early can lead to timely intervention, which is crucial for helping children manage this condition effectively.

Promoting Dyslexia Awareness

Increasing dyslexia awareness is essential. Awareness leads to better support for those affected and encourages understanding from peers and educators. Neuhaus Education Center is committed to promoting awareness through workshops, resources, and advocacy. By educating teachers, parents, and the community about dyslexia, we can create a more inclusive and supportive educational environment.

Promoting dyslexia awareness also involves dispelling common myths and misconceptions that often surround the condition. Many still wrongly associate dyslexia with a lack of effort or intelligence, which can lead to stigma and discourage those affected from seeking help. At Neuhaus Education Center, we emphasize the fact that dyslexia is a neurological issue, not a reflection of a person’s capability or effort. Through targeted information campaigns and public speaking events, we aim to change perceptions and educate the community on the true nature of dyslexia.

Increased dyslexia awareness facilitates better resource allocation and policy development in educational systems. Understanding the specific needs and challenges of students with dyslexia allows for the creation of more effective educational policies and practices. This can include training programs for teachers, improved assessment methods that do not unfairly disadvantage students with dyslexia, and the implementation of specialized resources and technologies to aid learning. By investing in awareness, we enhance individual educational outcomes while enriching our educational institutions and communities.

Dyslexia Support at Neuhaus Education Center

At Neuhaus Education Center , our approach to dyslexia support involves a combination of proven strategies tailored to each student’s unique needs. We utilize multisensory learning techniques that integrate visual, auditory, and kinesthetic components to enhance reading and spelling skills. Our programs are designed to build phonemic awareness, phonics skills , and reading comprehension, providing students with the tools they need to succeed.

Our educators are specially trained to recognize and address reading difficulties, employing specific interventions that make learning more accessible to students with dyslexia. These interventions include structured literacy programs that are systematic, explicit, and based on scientific research.

In addition to our structured literacy programs, Neuhaus Education Center also provides ongoing support and monitoring to ensure that each student’s progress is continually assessed and optimized. This personalized approach adapts over time as students develop and their needs change. We also emphasize the importance of collaboration between educators, parents, and other professionals involved in a child’s education. By creating a supportive network around each student, we enhance the effectiveness of our interventions and ensure a cohesive strategy that supports the student’s overall growth and development in reading and beyond. This holistic and flexible approach to dyslexia support is fundamental to fostering resilience and a positive educational experience for all our learners.

Join the Effort to Support Dyslexics

Understanding and addressing the challenges of dyslexia is about empowering individuals to reach their full potential. With the right support and strategies, children with dyslexia can excel in reading and beyond. By joining efforts to support those with dyslexia, we contribute to building a more equitable and understanding society, where the unique learning styles and needs of every individual are recognized and valued, paving the way for all learners to achieve success and fulfillment.

If you or someone you know is struggling with reading, don’t wait. Explore the resources and support available at Neuhaus Education Center. Join us in our mission to provide essential dyslexia support and foster a community of awareness and understanding. Together, we can make a difference in the lives of those with dyslexia.

What Michigan parents need to know about the ‘science of reading’

W hen Michele Maleszyk’s daughter came home from kindergarten last year, Maleszyk noticed she brought home reading material with letter patterns she hadn’t been taught yet.

“I thought it was odd she was expected to read books with patterns she didn’t know,” Maleszyk said. “I thought, ‘How can a kid sound out what they don’t know?’ The only way would be by looking at the pictures.”

The mother and former elementary school teacher said she found out her daughter’s Troy School District class was using the Lucy Calkins approach to literacy, which includes short lessons and aims to have students practice reading skills on their own by getting them excited about literature. The once widely popular learning model has been criticized by many parents and educators in recent years as ineffective.

Since then, Maleszyk has learned about and become an advocate for the science of reading , a term generally used to describe early literacy learning instruction that emphasizes phonics along with helping students build vocabulary and background knowledge. The approach applies findings from a body of neuroscience research and the study of cognitive psychology.

With more states switching to these curricula — in the last five years, at least 30 states have passed laws requiring reading instruction to be based on the science of reading — here’s an overview of the reading curricula in use in Michigan and what parents can do to advocate for their child’s literacy learning.

How is literacy instruction evolving?

Early literacy skills are important for students’ future success.

“If we think about reading, writing, speaking, and listening, we do those in all subject matters of school,” said Tanya Wright, an associate professor of Language and Literacy in the Department of Teacher Education at Michigan State University. “It is really critical to develop those skills in the early childhood years.”

Science on the best ways to teach kids to read is constantly evolving. Current research suggests effective reading instruction should include five core pillars: phonemic awareness, phonics and word recognition, fluency, oral vocabulary, and text comprehension.

Literacy interventions that emphasize phonics have won out over other approaches in the so-called “ Reading Wars ” over the years.

The whole language approach, which typically doesn’t include much phonics instruction and was based on the belief that learning to read is an innate process, came first. It included the three-cueing method, which means students are given three cues to decode text: semantic cues that give meaning from context, syntactic cues that give meaning through letters, and grapho-phonic cues that give meaning through spelling patterns.

Then came balanced literacy , which combined the whole language approach with some phonics instruction.

Curricula that are well-regarded by science of reading advocates include Core Knowledge Language Arts (sometimes called CKLA), EL Education , Wit and Wisdom , and Superkids Reading Program .

Curricula that have been evaluated by some education experts as not meeting expectations include Fountas & Pinnell Classroom and Units of Study for Teaching Reading , also known as Lucy Calkins, named for the literacy expert who created the curriculum.

But even for widely respected and popular programs that claim to use methods derived from the science of reading research, there is not much available peer-reviewed research on how effective specific curriculum materials are. And available efficacy studies have yielded mixed results .

Tara Kilbride, the interim associate director of the Education Policy Innovation Collaborative at Michigan State University, said it’s important to remember the science of reading is still changing.

“It will continue to evolve as more research happens and we learn more,” she said.

Which literacy curricula does Michigan use?

Michigan, which ranks 43rd in the country for reading, is one of the 26 states that lay out clear standards for reading instruction in teacher preparation programs that include the five core pillars, according to a report released by the National Council on Teacher Quality last month. The state also has standards for how educators should learn to support English learners.

But Michigan is among the states that do not verify these policies are being met. The state does not maintain full authority to review teacher preparation programs and does not audit them to ensure they align with the science of reading, the report says.

Though the state does provide guidance on using reading programs that align with research-based best practices, there is no one set reading curriculum for Michigan students. The state’s schools operate under local control, and districts decide their own curricula, making it impossible to discern how many districts use outdated or poorly rated core curricula.

Reading instruction materials can vary widely within districts and sometimes even within the same elementary schools, according to a 2022 policy brief by EPIC.

“What that really tells us is that across Michigan classrooms, kids are getting inconsistent instruction,” said Wright.

In a survey of more than 9,000 Michigan K-5 teachers and 192 superintendents, educators reported using more than 450 different English language arts curricula. Many teachers said they used multiple curricula and supplemental materials in their lessons.

The researchers found all participating districts provided guidance on curriculum selection. Despite guidance, teachers within the same district did not all use the same curriculum, and many were using curricula that were poorly rated or unrated.

For example, 31% of teachers in the survey said they used Fountas & Pinell, which did not meet expectations according to EdReports, a website that reviews instructional material.

Kristine Griffor, assistant superintendent for elementary instruction in the Troy School District, said Lucy Calkins has been used by all of the school system’s elementary school teachers for around 15 years, with an updated curriculum adopted nine years ago. A phonics component was to the reading and writing units of study five years ago, she said. A literacy leadership team that included teachers selected the curriculum, said Griffor.

Parents can check whether their school’s curriculum is considered high quality on the EdReports website.

While curricula is a key component that influences instruction, Wright said it’s not the only component. Teachers can use additional materials and their own knowledge to guide lessons.

What about students with dyslexia?

As has been the case nationally , Michigan dyslexia advocates have helped lead the push to adopt science of reading strategies. Though more research is needed , there is evidence the interventions used to identify and help struggling readers in curricula that claim to use the science of reading may hold promise for students with dyslexia, according to a 2021 study .

Some say aspects of a set of Michigan dyslexia bills proposed in October would benefit the overall student population.

One bill would tighten state standards for literacy screeners schools use to identify kids having trouble reading. Another would require school districts to have at least one teacher trained in the Orton-Gillingham method, a highly structured multisensory approach to reading instruction.

On Tuesday, the Senate Education Committee is set to discuss the dyslexia bill that would tighten screeners and another that would set standards for teacher preparation programs to ensure future educators have the tools they need to support students with dyslexia.

What can parents do to set their kids up for success in learning to read?

Wright suggests parents who want to better understand the best practices for teaching kids to read review the Literacy Essentials resource guide she helped compile with other researchers. The guide includes essential practices for kids in all grade levels, professional learning resources for educators, coaching modules, and more.

“We want kids to learn to look at the symbols and be able to figure out how they translate into words and sentences,” said Wright. “We also want kids, at the same time, to be building knowledge and vocabulary and comprehension skills, so that once they are independent decoders, they have the knowledge they need to comprehend the text.”

For example, Wright said that if her child was not receiving science or social studies instruction in the early grades, she would be concerned.

Parents may also want to get an understanding of how their child is learning literacy by asking teachers about how they approach carving out time for reading and writing during the school day.

They may also ask how teachers screen students for reading difficulties and what interventions are used, said Maleszyk, the parent in Troy.

“Ask them, ‘If my child is falling behind, what steps are you taking to support them?’” she said.

Parents might also ask teachers if they’ve received training in the Language Essentials for Teachers of Reading and Spelling (or LETRS ), which has been recommended by the state .

Experts and educators suggest taking a collaborative approach to talking with teachers and administrators about concerns with your child’s reading instruction. Everyone’s goal should be achieving student literacy, regardless of the approach.

Maleszyk said if a teacher is not able to answer your questions or address your concerns, you may want to talk with the school’s principal and then the district’s director of curriculum. She has also spoken about her concerns with her daughter’s curriculum at school board meetings.

“We are always learning different ways and practices and we feel the curriculum we selected centers on children,” said Griffor, the Troy School District administrator.

Inequities in Michigan’s literacy proficiency

Maleszyk said she knows her daughter will learn to read – she’s able to pay up to $80 an hour for tutoring. But she worries about students whose families can’t afford the extra support.

Michigan students have long struggled with literacy competency, and experts say inequitable school funding is among the many reasons students from low-income families and students of color have suffered the most from inadequate reading instruction.

A 2016 lawsuit alleged that the state denied students in the Detroit Public Schools Community District a basic education by failing to teach them to read. It was settled for $94.4 million.

In 2022, Michigan ranked 43rd compared to the rest of the nation for 4th grade reading, according to a report by Education Trust-Midwest that used data from the National Assessment of Educational Progress . The scores from that assessment were seven points lower than they were 20 years prior.

While the rest of the country’s reading scores dropped during the pandemic, Michigan’s plummeted at a faster rate than the national average due to a longtime underinvestment in public education, according to the 2023 State of Michigan Education report.

In an effort to improve early literacy, Michigan’s Republican-led Legislature and then-Gov. Rick Snyder approved the 2016 third-grade reading law, which included a retention rule.

The retention rule took effect in 2021 and other aspects of the law went into effect much earlier. Before the retention rule was repealed in March , Black students and kids from low-income families were more than twice as likely to have to repeat the third grade compared to their white peers and students from wealthier families.

Most districts pushed back against retaining more students, especially during the early stages of the pandemic, when learning loss was widespread and when the rule took effect.

The other aspects of the reading law remain, including the requirement that schools identify struggling readers and provide extra help.

Hannah Dellinger covers K-12 education and state education policy for Chalkbeat Detroit. You can reach her at [email protected] .

Autism, dyslexia, ADHD. How the University of San Diego is helping ‘neurodivergent’ students succeed

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Niki Elliott skipped the fifth grade. She was so smart that she could have skipped another, she said, but her mother didn’t want her in class with older boys.

She was bored in school. She had a “near photographic” memory and didn’t need to study — so she never learned how to, Elliot said. She remembers finishing her assignments in five minutes and spending the next 30 waiting for her classmates to catch up.

But when she got to college, classes were difficult. “I really had a big crash and burn,” she said.

Experts call students like Elliott “ twice exceptional ,” a term used to refer to children who are gifted in some areas, but also experience a learning or developmental challenge. In Elliott’s case, that challenge was attention deficit/hyperactivity disorder, which made it difficult for her to manage her time and attention.

She remembers being in college and thinking, “People told me I was so smart, but why am I struggling so hard?”

Elliot went on to become a special education teacher, and said she never stops thinking about how to create a world in which a young Black student like herself could be taught to work with — instead of against — her learning differences. Now a professor in the School of Leadership and Education Sciences at the University of San Diego, she’s helping to open the school’s Center for Embodied Equity and Neurodiversity in August.

At its simplest, neurodiversity is the idea that everybody’s brains work differently, and that these differences are normal. Neurodivergent, which is not a medical diagnosis, is an umbrella term that refers to people who have autism spectrum disorder, ADHD, dyslexia, or other atypical ways of thinking, learning and interacting with others.

“Embodied equity,” the other term in the new center’s name, refers to an anti-discrimination approach that considers all aspects of people’s identities — including race, gender, ability, socioeconomic status — when addressing social problems.

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Elliott said the center’s work will fall into four main categories: training K-12 teachers and support staffers, training community college educators, working on policy issues that affect neurodivergent students and offering programs to set up such students for success in college and the workplace.

The training is being funded through contracts with schools, colleges and other organizations; additional costs will be covered by grants from philanthropic foundations, Elliott said.

“We really have to work to change the mindset of faculty to understand the ways in which these adaptations to their delivery and development of content could make all the difference for so many more highly bright and capable students to thrive in higher ed,” Elliott said.

If teachers and education support staff are equipped with strategies to help students whose brains work differently, Elliott hopes that more of these students will have the option to go to college.

As the public understanding of brain differences expands, college leaders are trying to help make campuses become more hospitable to neurodivergent students.

A task force at UC Berkeley, for example, is focused on medical care and access to screenings or assessments; potential and curriculum changes and disability accommodations for students and for employees, who are often graduation students.

The needs of neurodivergent students force academics to confront a bias in which needless inflexibility is equated with academic rigor, said Lisa García Bedolla, vice provost for graduate studies, who is leading the effort.

San Diego State University offers a class focused on cognitive and social differences. It’s designed for neurodivergent students or those who want to work in fields such as social work, special education or psychology. According to the course description, topics include executive functioning and time management; social cognition, context awareness and how to take on the perspective of another person; and communication and relationship skills and self-advocacy.

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Inna Fishman, the founding director of SDSU’s Center for Autism and Developmental Disorders, said that although there’s been a “huge paradigm shift,” meaningful change for neurodivergent college students will take time.

This work is also complicated because definitions of neurodivergence vary and it’s difficult to know how many students stand to benefit.

Many experts believe the number of students with brain differences that fit under the neurodivergent umbrella is growing, whether because of an increase in people with such conditions or because of reduced stigma, greater awareness and better identification of such conditions.

The number of colleges where at least 5% of students report having a disability has risen from 510 in 2008 to 1,276 in 2022, according to data from the Integrated Postsecondary Education Data System. But this measure is imperfect: It includes students who have physical disabilities. Also, roughly two-thirds of college students with disabilities choose not to disclose it to their college.

“A lot of students when they leave K-12, they want to wash their label off of them and start fresh,” Elliott said. “They want to believe that they can do well in college without it, or that they would be mistreated or stigmatized if they let people know.”

Experts say that students whose brains work differently often face challenges during their K-12 education; when they get to college, the challenges don’t stop, they just change.

Laudan B. Jahromi, a professor of psychology and education at Teachers College at Columbia University, said these students often struggle with what she called “cognitive flexibility,” which can affect time management, planning, prioritizing and other such organizational skills, and make college classes more difficult to manage.

Fishman, at SDSU, said students with brain differences might need help taking notes, more time to take exams or to have instructions repeated to them multiple times. They might miss certain nonverbal communication or cues from their professors or peers.

Colleges offer accommodations that can help with some of these challenges, but often students can only unlock this help with a qualifying diagnosis, which can be difficult to get, depending on a student’s health insurance and access to the appropriate assessments.

Many neurodivergent students use medications that must be taken on a certain schedule to help manage their conditions, Elliott said. Problems arise when students’ classes are only offered at a time that doesn’t work with their medication schedule. If the students need a course to progress in their major, then they’re stuck trying to pass in conditions that don’t make sense for them. Elliott said this can lead attrition or underperformance.

And physically being in the classroom can cause stress for students who are sensitive to factors such as flickering fluorescent lights or certain types of sounds, or who have difficulty being around large groups.

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Some neurodivergent people struggle with understanding social dynamics and cues, or with social anxiety. Sometimes requiring social interactions involving graded group projects, puts them at a disadvantage. Socialization can pose significant challenges for these students outside the classroom, too, as they navigate community living, friendships and dating.

The students must also be their own advocates, often without fully understanding their own needs.

Elliott said the new center will offer a program that will support Black students with and without brain differences starting in sixth grade. The idea is to help students understand their learning styles, what they need to be successful in school and how to advocate for themselves as they move toward college. If the students finish high school and qualify for admission to the University of San Diego, they will have a they will have a full scholarship to attend .

Next year, Elliott said, the center will begin offering a summer bridge program specifically for neurodivergent students, with a similar curriculum.

“It’s teaching each person where their gifts are, how they contribute to a whole and how to use that to navigate a successful higher ed experience,” Elliott said.

This story was produced by The Hechinger Report , a nonprofit, independent news organization focused on inequality and innovation in education.

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The Role of Choline in Neurodevelopmental Disorders—A Narrative Review Focusing on ASC, ADHD and Dyslexia

Emma derbyshire.

1 Nutritional Insight, Surrey KT17 2AA, UK

Michael Maes

2 Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok 4002, Thailand

3 Research Institute, Medical University of Plovdiv, 10330 Plovdiv, Bulgaria

Associated Data

Not applicable.

Neurodevelopmental disorders appear to be rising in prevalence, according to the recent Global Burden of Disease Study. This rise is likely to be multi-factorial, but the role of certain nutrients known to facilitate neurodevelopment should be considered. One possible contributing factor could be attributed to deficits in choline intake, particularly during key stages of neurodevelopment, which includes the first 1000 days of life and childhood. Choline, a key micronutrient, is crucial for optimal neurodevelopment and brain functioning of offspring. The present narrative review discusses the main research, describing the effect of choline in neurodevelopmental disorders, to better understand its role in the etiology and management of these disorders. In terms of findings, low choline intakes and reduced or altered choline status have been reported in relevant population subgroups: pregnancy (in utero), children with autism spectrum disorders, people with attention deficit hyperactivity disorder and those with dyslexia. In conclusion, an optimal choline provision may offer some neuronal protection in early life and help to mitigate some cognitive effects in later life attributed to neurodevelopmental conditions. Research indicates that choline may act as a modifiable risk factor for certain neurodevelopmental conditions. Ongoing research is needed to unravel the mechanisms and explanations.

1. Introduction

Neurodevelopmental disorders (NDDs) are a class of disorders impacting brain development and function [ 1 ]. In the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) NDDs are defined as a group of conditions with onset in the developmental period, inducing deficits that produce impairments of functioning [ 2 ]. Within this definition, NDDs consist of: autism spectrum disorder (ASD; a communication disorder); attention-deficit/hyperactivity disorder (ADHD); intellectual disabilities; neurodevelopmental motor disorders (including tic disorders) and specific learning disorders (including dyslexia) [ 2 , 3 ]. A high level of comorbidity also exists between conditions. For example, ASD and ADHD are known to have shared genetic heritability, with both being associated with social and executive functioning impairments [ 4 ]. Most individuals with ASD exhibit ADHD symptoms, and around 15–25% of ADHD individuals have ASD symptoms [ 5 ]. Similarly, research from twin studies found that there was more than an eightfold increase in the prevalence of NDDs (termed ‘neurodevelopmental disorders and problems’ in this study) in individuals with dyslexia, compared with typical readers [ 6 ].

The global burden of NDDs appears to be rising, as demonstrated in Figure 1 . Both ASD and ADHD are reported to have risen in prevalence over the past 10 years [ 5 ]. An analysis using data from 204 countries and territories forming part of the Global Burden of Disease Study 2019 showed that for ASD, age-standardized rates had risen by around 0.06% annually over the last three decades [ 7 ]. The total global prevalence of ASD in 1990 was 20.3 million, increasing to 28.3 million in 2019 [ 8 ]. For males, ASD prevalence was 15.6 million in 1990 and 21.6 million in 2019 [ 8 ]. For females, the prevalence of ASD in 1990 was 4.7 million, rising by an additional 2 million to 6.7 million by 2019 [ 8 ]. For ADHD, in 1990 the reported prevalence was 72.4 million, rising to 84.7 million in 2019 [ 8 ]. In males, the global prevalence of ADHD was higher—52.6 million in 1990, increasing to 61.5 million in 2019 [ 8 ]. Amongst females, the 1990 global prevalence of ADHD was 19.8 million, rising to 23.2 million in 2019 [ 8 ]. Subsequently, the burdens of ADHD and ASD appear to have been greater in males than females [ 8 ]. ADHD and ASD remain under-recognized and underdiagnosed in many countries, especially amongst girls and women [ 9 , 10 ]; thus, prevalence rates could be even higher. Prevalence is also reported to be higher in certain population groups, such as looked-after children [ 11 , 12 ]. ADHD prevalence transitions into adulthood in around 30–50% of cases [ 13 ].

Global prevalence (in millions) of NDDs. Source: Data extracted from the Global Burden of Disease Study [ 8 ].

Dyslexia is highly prevalent, affecting around 20% (1 in 5) of the global population, and males/females equally [ 14 ]. It occurs across a range of cognitive and language abilities, a range which includes both higher-than- and lower-than-average levels of functioning [ 15 ]. There are different definitions of dyslexia, but Reid (2016) defines it succinctly as a “processing difference, often characterized by difficulties in literacy acquisition affecting reading, writing, and spelling. It can also have an impact on cognitive processes such as memory, speed of processing, time management, coordination, and automaticity. There may be visual and/or phonological discrepancies and there are usually some discrepancies in educational performances” [ 16 ].

NDDs are now the most frequently diagnosed conditions in child neurology/pediatric clinical practices [ 17 , 18 ]. There are many potential explanations as to why the prevalence of NDDs could be rising. Improved diagnostic screening might be one explanation [ 18 ]. Other factors such as maternal metabolic conditions [ 19 ] and misalignment with dietary and lifestyle recommendations have also been proposed [ 20 ]. Increasingly, the role of nutrition during gestation (pregnancy) and neurodevelopment is increasingly being recognized, with inadequate intakes of certain nutrients being linked to ADHD, ASD, altered cognition and visual and motor deficits [ 21 ]. Past research has focused heavily on nutrients such as the omega-3 fatty acid docosahexaenoic acid (DHA) [ 22 , 23 , 24 ], but now research has accrued for other nutrients, including choline, considering their roles in neurodevelopment and promoting optimal cognition [ 21 , 25 , 26 , 27 , 28 ].

In this narrative review, we focus on and examine the role(s) of choline as a potential modifiable risk factor for certain NDDs. We will focus on ADHD, ASD (now also referred to as ASC, autistic spectrum condition), and dyslexia, as this is where most research appears to sit.

2. Neurodevelopment

The brain is a central organ that orchestrates the whole body [ 29 ]. The human brain begins to develop as early as the third week into gestation when neural progenitor cells differentiate; this process extends into later adolescence and potentially across the lifespan [ 30 ]. Processes underpinning brain development include gene expression and environmental inputs, which are both crucial for normal brain development, with disruption of either significantly impacting upon neural outcomes [ 30 ]. The development of the brain’s circuitry begins as early as 2–3 weeks into gestation and requires the coordination of complex neurodevelopmental processes [ 31 ]. Stiles et al. (2010) explains that “brain development is aptly characterized as a complex series of dynamic and adaptive processes that operate throughout the course of development to promote the emergence and differentiation of new neural structures and functions” [ 30 ].

From approximately 6 weeks post-conception to mid-gestation, a number of cellular events occur, including neurogenesis followed by apoptosis, differentiation, migration and synapse formation [ 31 ]. Extended periods of cortical development occur across the lifespan. This ‘developmental’ phase occurs across childhood and adolescence when cortical development (outer layers of the cerebrum) transitions from lower-order, unimodal cortices with motor and sensory functions to higher-order, trans-modal cortices underpinning executive, socioemotional and mental brain functions [ 32 ].

The role of in utero programming, as described in the theory developed by Professor David Barker, and thus coined the ‘Barker Hypothesis’, is well-recognized [ 33 ]. If fetuses have a limited nutrient supply in utero they need to adapt; this is a process that can permanently modify their structure/metabolism, inducing ‘programme changes’ that can be the origins of other conditions later in life (the ‘fetal origins of adult disease’) [ 33 ]. It is now well recognized that changes in brain function can lead to a spectrum of NDDs [ 29 ].

Intrauterine exposures (including nutrients) have been linked to NDDs, although elucidating the timing and exact mechanisms can be challenging [ 34 , 35 ]. Such exposures during these sensitive windows of life are another factor considered to influence brain development [ 36 ]. Increasingly, it is well appreciated that normal neurodevelopment is central for brain functions across the lifespan, with any modulations potentially contributing to brain dysfunction [ 29 ]. Heland et al. (2022) recently proposed that nutritional deficits, to some extent, could potentially prevent neurodiversity, as certain nutrients have the ability to improve neurodevelopmental outcomes by mitigating pathological processes such as inflammation, hypoxia and oxidative stress [ 37 ]. This brings us on to the potential role of choline.

3. In Utero Origins

Zeisel et al. (2006) described how choline deficiency during sensitive periods of brain development could induce permanent changes in brain function and result in persistent cognitive and memory deficits [ 38 ]. As choline is important for brain development, Bernhard et al. (2013) raised concerns about choline levels in very low birth-weight infants, with nutritional intakes of preterm infants frequently being less than the estimated adequate intake, and shortages being apparent until day 10 postnatally [ 39 ].

In the Seychelles Child Development Nutrition Study, choline was listed as one of the key nutrients expected to have direct effects on neurodevelopment, both prenatally and postnatally, and was believed to have some correlation with fish consumption [ 40 ]. Another study looking at maternal egg consumption found that choline deficiency predicted fetal autonomic and brain maturation indices at 32- and 36-weeks’ gestation, respectively [ 41 ]. Poor availability of choline in utero has been further linked to impaired differentiation of retinal neuronal cells, indicating a role in the development of the visual system [ 42 ].

Derbyshire and Obeid (2020) [ 27 ] provided an updated systematic review using data from 38 animal and 16 human studies. In particular, it was concluded that choline over the first 1000 days of life could potentially: (1) support normal brain development; (2) protect against neural and metabolic insults, including alcohol; and (3) improve neural and cognitive functioning [ 27 ]. A further systematic review and meta-analysis collating evidence from 30 publications found that higher maternal choline intake was likely to be associated with improved child neurocognition/neurodevelopment [ 26 ].

Choline deficiency is common in pregnancy (in utero exposure) [ 43 , 44 ]. Average choline intakes amongst women of childbearing age have been explored in a review of 23 studies, and were reported to range from 233 mg/day to 383 mg/day, even with the inclusion of choline from supplements, and thus are consistently lower than the estimated adequate intake (AI) of 480 mg/day for pregnant women [ 45 , 46 ]. In a recent study conducted in Germany, only 7% of pregnant women achieved adequate choline intakes [ 47 ]. Similarly, amongst an Australian sample of pregnant women, median choline intake was 362 mg/day in early pregnancy, with eggs providing around 17% of the choline [ 48 ]. The authors concluded that few pregnant women met the AI for choline, and that this may need to be improved [ 48 ].

4. Mechanistic Studies

Choline is an essential micronutrient, as recognized by the United States (U.S.) Institute of Medicine in 1998 [ 49 ]. Mechanistically, it is a precursor of the brain neurotransmitter acetylcholine and membrane phospholipids, including phosphatidylcholine [ 50 , 51 ]. It is also a methyl donor known to play a central role in brain growth and development, maintaining the functional and structural integrity of the cell membrane [ 36 ]. Through the actions of its metabolites, it partakes in pathways involved in the methylation of genes related to memory and cognitive functions [ 36 ].

As shown in Table 1 , several mechanistic studies have investigated the effects and mechanisms of choline on brain development. The fetus and newborn are known to have high choline demands, with the micronutrient’s role in DHA and histone methylation believed to be as a modifier of genes involved in aspects of learning and memory [ 52 ]. Bekdash et al. (2016) describe how choline is an important epigenetic modifier of the genome (altering gene methylation, expression and cell function), with abnormal levels during fetal development and/or early postnatal life being linked to altered memory functions later in adult life [ 53 ]. Choline is also needed for normal memory development, possibly due to changes in the development of the memory center (hippocampus) in the brain [ 54 ]. Choline is thought to influence stem cell proliferation and apoptosis, therefore potentially modifying brain structure and function [ 55 ].

Murine models show that postnatal choline treatment can modulate neuronal plasticity, preventing deficits in motor coordination whilst enhancing density of dendritic spines and neuronal morphology [ 56 ]. Choline has further been found to partially restore dendritic structural complexity in murine hippocampal neurons that are iron deficient [ 57 ]. Other work shows that reduced choline supplies during gestation can impede and diminish the number of cortical neural progenitor brain cells (NPCs), with two types of NPCs—the radial glial and intermediate progenitor cells—being affected, indicating that choline supplies regulate cerebral cortex development [ 58 ]. A murine model focusing on autism showed that choline supplementation administered to offspring of methylenetetrahydrofolate reductase (MTHFR)-deficient mothers had the potential to attenuate the autistic-like phenotype [ 59 ]. Further research found choline supplementation to improve impairments in social interaction in a murine model of autism, helping to reduce deficits in social behavior and reduce anxiety [ 60 ].

Role(s) of choline in neurodevelopment and brain function.

Key: BHMT, betaine homocysteine methyltransferase; Cho, choline; DNA, deoxyribonucleic acid; MTHFR, Methylenetetrahydrofolate reductase; NPCs, neural progenitor cells; PC, phosphatidylcholine.

5. Brain Imaging (Animal Models)

Several imaging studies have investigated choline’s role in brain structure and function using animal models. Mudd et al. (2016) fed sows either a choline-deficient (CD) or choline-sufficient (CS) diet for the last half of gestation, finding that CD sows had smaller total brain volumes when measured using magnetic resonance imaging 30 days postnatally [ 65 ]. Concentrations of glycerophosphocholine-phosphocholine were also lower, indicating that choline deficiency appeared to delay neurodevelopment and induce structural and metabolic changes [ 65 ]. Other work by Mudd et al. (2018), using a similar approach, further showed that CD pigs had significantly reduced left- and right-cortex gray matter compared with prenatally CS pigs [ 66 ].

6. Human Studies

Nutritional status (including low choline) is regarded as playing a key role in the severity and incidence of core ASD symptoms [ 67 ]. Research from human studies has focused on the potential role(s) of choline in relation to ASD ( Table 2 ).

Firstly, work using spectroscopic imaging on children with ASD (aged 3–4 years) found that gray matter and white matter levels of choline were reduced, compared with typically developed children [ 68 , 69 ]. Focusing on the white matter composition, other work also found that ASD and its severity was associated with lower levels of choline in brain white matter, along with the perisylvian cortex [ 70 ]. Similarly, Margari et al. (2018) found brain metabolite levels (choline/Cr ratios) to be significantly altered in the frontal lobe white matter in ASD subjects versus controls [ 71 ]. Hardan et al. (2008) observed lower levels of choline in the left side of the thalamus in children with autism [ 72 ]. Some research has found lower choline levels in the thalamus to be correlated with behavioral scores in ASD children (7–18 years), i.e., increased severity of stereotyped behaviors and communication impairments [ 73 ].

Regarding metabolic characteristics, Wang et al. (2022) studied 29 ASD and 30 typically developing boys (mean age ≈ 3 years) [ 74 ]. Boys with ASD had lower levels of plasma choline, which was adversely correlated with ABC-language scores–findings that aligned with Gabis et al. (2019) [ 74 , 75 ]. Indeed, the work by Wang et al. (2022) also found that choline metabolism intermediates such as phosphatidylcholine and lysophosphatidylcholine (involved in glycerophospholipid metabolism) were reduced, implying that this could impact on processes of choline metabolism and subsequent impairments in language ability in ASD children [ 74 ]. Additional work has investigated the effects of supplementation. Gabis et al. (2019) undertook a double-blind randomized trial examining the combined effects of donepezil and choline (350 mg/day for 8 weeks in the open label study phase) compared to a placebo [ 75 ]. The treatment group had a sustained effect on receptive language skills in ASD children for 6 months post-treatment, with more significant effects observed in those under 10 years of age [ 75 ].

Other research has described habitual choline intake and status. Scientists using data from the U.S. Autism Intervention Research Network for Physical Health (AIR-P) study found that 60–93% of ASD children were consuming less than the recommended adequate intake for choline [ 76 ]. Children with autism also had lower plasma choline levels than did healthy controls [ 76 ]. The authors concluded that choline intakes were inadequate in a significant number of ASD children, which could contribute to abnormalities in folate-dependent one-carbon metabolism observed in many children with autism [ 76 ]. Very similar findings were reported by Hyman et al. (2012) [ 77 ]. An analysis of 3-day food records from children with ASD aged 2–11 years showed that few ASD or matched controls met recommended chorine intake levels [ 77 ].

Key studies investigating ASD and choline.

Key: ASD, autism spectrum disorder; Cho, choline; DB, double-blind; DD, delayed development; int., intervention; M, male; PC, placebo-controlled; TD, typical development; y, years.

Some human trials have included an analysis of choline status or circulating levels of brain metabolites and studied these factors in relation to ADHD ( Table 3 ).

Focusing on imaging studies, Alger et al. (2021) used whole-brain diffusion tensor imaging [ 78 ]. The results showed that ADHD children (aged 8–13 years) exposed to ‘prenatal alcohol exposure’ (PAE) had more severe white-matter pathology compared with those without PAE [ 78 ]. Amongst those with ADHD + PAE, Tower Test Achievement scores (higher for better performance) correlated negatively with choline, with its role somewhat unclear and warranting continued investigation [ 78 ]. Additional neuroimaging work measuring prefrontal white matter discovered that anterior corona radiata levels of choline were 27% lower in children and adolescents with ADHD + PAE, compared with those with idiopathic ADHD [ 79 ]. Other magnetic resonance research showed that 20–25% of neurons may have died or been severely dysfunctional in pediatric ADHD patients, and there seemed to be mild hyperactivity of the cholinergic system (the system that encompasses the synthesis and secretion of acetylcholine) [ 80 ].

Additional research has looked at supplementation. One randomized controlled trial provided 2–5-year-olds with fetal alcohol spectrum disorder (FASD) with choline (1.25 g. choline bitartrate powder delivering 513 mg/day choline) or a placebo over 9-months, with follow up 4 years later (mean age 8.6 years) [ 81 ]. At follow-up the children who had received choline had fewer behavioral symptoms of ADHD than did the placebo group [ 81 ]. The children administered choline also had better working memory ability, verbal memory, visual-spatial skills, and non-verbal intelligence than the placebo group [ 81 ]. Borlase et al. (2020) studied the effects of micronutrient treatment on brain neurometabolites, since these appeared to be altered in children with ADHD, particularly in the prefrontal cortex and striatum [ 82 ]. Children with ADHD (mean age 10.8 years, non-medicated, n = 27) received daily micronutrients or a placebo over 10 weeks [ 82 ]. It was not specified which micronutrients or dosages were involved, but choline was identified as a metabolite of interest. In the treatment group, there was a trend for decreased (improved) choline in the left and right striatum, though changes were not regarded to be of significance [ 82 ].

Some research has studied choline levels in relation to methylphenidate administration. Earlier work proposed that choline does not appear to be sensitive to methylphenidate treatment in children [ 83 ]. However, an analysis of regional brain spectra in 2008 found a significantly reduced signal of choline-containing compounds following methylphenidate treatment [ 84 ]. This is believed to fit with a recent energetics hypothesis of ADHD—that insufficient lactate supply to oligodendrocytes leads to impairments in fatty acid synthesis and myelin sheath formation, which may account for the reduced choline levels [ 84 , 85 ]. According to Wiguna et al. (2012), stimulant (long-acting methylphenidate) treatment over 12 weeks appears to induce neurochemical changes (thought to reflect improved neuroplasticity) in the prefrontal cortices of children [ 86 ]. In particular, the choline/creatine ratio decreased significantly in children (mean age 8.5 years) by 12.4% in the right prefrontal cortex and 16% in the left prefrontal cortex [ 86 ]. This was a pilot study, so repeated investigation is needed.

Key studies investigating ADHD and choline.

Key: B, boys; Cho, choline; DB, double-blind; FASD, fetal alcohol spectrum disorder; MPH, methylphenidate; NR, not reported; NS, Not clearly specified; PC, placebo-controlled; TD, typical development; y, years.

6.3. Dyslexia

An emerging body of evidence has looked at choline levels/metabolites in relation to markers of dyslexia ( Table 4 ). In one study, reading ability and executive function were measured in 24 children (8 to 12 years) with dyslexia and 30 typical readers [ 87 ]. For females with dyslexia there was a strong, statistically significant inverse correlation between processing speed and choline [ 87 ]. It is well appreciated that individuals with dyslexia have prolonged response times, and metabolite changes appear to be present which could hold promise as possible markers for dyslexia [ 87 ]. This is one of the first studies to identify metabolite changes in regions not regarded as being part of the ‘classic’ reading circuitry.

Earlier work has shown that choline levels were negatively correlated with reading and related linguistic measures in phonology and vocabulary (i.e., higher concentrations were associated with reduced performance) in the occipital lobe [ 88 , 89 ], left temporoparietal region [ 90 ] and the cerebellum [ 91 ]. It seems tenable that higher choline levels in reading-related regions for those with reading difficulties could be indicative of high membrane turnover, white matter, and cellular density [ 87 ]. This aligns with evidence that myelination is impaired in this population, particularly in the white-matter tracts that pass the temporoparietal regions and the occipital lobe [ 92 , 93 ].

Other research focusing on the visual and temporo-parietal cortex showed that children with dyslexia, compared to controls, had choline levels that were 7.6% lower in the left temporo-parietal region and 5.5% lower in the visual cortex [ 94 ]. Amongst children, the higher the choline concentration, the faster the Rapid Automatized Naming (RAN), though this did not reach a level of significance [ 94 ].

Key studies investigating markers of dyslexia.

Key: C, Control; Cho, choline; D, Individuals with dyslexia; M, male; MRI, Magnetic resonance imaging; NAA, N -acetyl aspartate; NR, not reported; RD, reading difficulties (termed disability in cited publication); TR, typical readers; y, years.

6.4. Processing Speed and Attention

It is well recognized that processing speed and working memory can be reduced in both individuals with ADHD and those with dyslexia, with individuals having both conditions concurrently being most affected [ 95 ]. Caudill et al. (2018) examined the effects of maternal choline supplementation during pregnancy [ 96 ]. Women moving into their third trimester were randomly assigned to groups consuming 480 mg choline/d ( n = 13) or 930 mg choline/d ( n = 13) until delivery [ 96 ]. Infant information processing speed and visuospatial memory were studied at 4, 7, 10 and 13 months of age [ 96 ]. Intriguingly, mean reaction time (averaged across the four ages) was significantly quicker for infants born to mothers who took the higher levels of choline—930 (vs. 480) mg choline/d [ 96 ]. These findings suggest that maternal consumption of around twice the recommended amount of choline during the last trimester of pregnancy improves infant information processing speed.

When these children were followed-up at 7 years of age, children who had been exposed to choline at a level of intake of 930 mg/d from the third trimester of pregnancy had improved levels of sustained attention (i.e., a significantly better ability to maintain correct signal detections) [ 97 ]. This may be attributed to them being able to sustain cholinergic activity in the prefrontal cortex of the brain, a region which regulates attentional control [ 97 ]. These findings are interesting, and have wider potential implications, including potential studies focusing on attention/inattention and choline status in individuals with ADHD and/or dyslexia.

7. Discussion

In the past, there has been a tendency to focus on nutrients such as long-chain omega-3 fatty acids in relation to their effects on neurodevelopment and NDDs [ 22 , 23 , 98 , 99 , 100 ]. However, in recent years there has been an accumulation of evidence highlighting the potential role(s) of choline [ 27 , 38 , 54 , 63 ].

Choline is recognized as having three distinct physiological roles: (1) the synthesis of neurotransmitter acetylcholine, (2) acting as a major methyl donor and (3) preserving the structural integrity and lipid-mediated signaling for cell membranes [ 44 , 101 ]. Choline has an important role in neurodevelopment, with normal concentrations enlarging cholinergic neurons in size and number in the medial septum [ 28 ]. There is now an accruing body of science demonstrating that choline is important for neurological development and brain function [ 21 , 27 ]. Regarding mechanisms, it is possible that the role(s) of choline are different depending on the life stage and form of the NDD. Mechanisms are complex and have potential to be multi-faceted. Modulations in white-matter pathology, impaired myelination, altered levels of brain metabolites and potential compensatory mechanisms have all been described in relation to choline markers [ 66 , 68 , 70 , 78 , 85 ]. Further research is now warranted to build on present insights and disentangle these.

For ASD, we have seen how lower plasma choline levels have been correlated with diminished language scores, indicating that there could be plausible links between products of choline metabolism and language ability [ 74 , 75 ]. Choline is a major brain metabolite and essential component of different membrane phospholipids [ 102 ]. Interestingly, other work conducted with typically developed children has discovered links between speeded (rapid) naming (a measure of long-term memory) and choline levels which could potentially influence language ability [ 103 ]. We have seen in several studies included in this narrative review how choline intakes and/or status appear to be reduced in children with NDDs, particularly ASD [ 74 , 76 , 77 ]. These are intriguing findings worthy of future development and research.

Moving on to ADHD, the effect of stimulant medication (methylphenidate) appears to be inconclusive, with some authors reporting that choline is not sensitive to this [ 83 ] and others documenting that levels of choline-containing compounds are reduced [ 84 , 86 ]. Some studies have looked at the effects of medication on brain choline metabolite levels [ 84 , 104 ]. One spectroscopic study observed reduced choline levels in the anterior cingulum following chronic methylphenidate treatment [ 84 ]. Additional research found that children with ADHD on stimulant medication had significantly higher choline ratios in the left prefrontal region, indicating that the medication could normalize brain metabolite levels [ 104 ]. Further clinical trials are needed to investigate this further. Given the advances of new medications, these should also be studied in relation to any effects on choline levels/metabolites. Another important point to consider is that sensory issues and food selectivity in these population groups could further impact dietary intakes [ 105 , 106 ], including that of choline.

Turning to dyslexia, more research is needed to unravel the science. It appears that higher choline levels in those with reading difficulties for reading-related regions could be reflective of higher membrane turnover, white matter, and cellular density, indicating compromised myelination in this population [ 87 , 92 , 93 ]. For dyslexia, the visual magnocellular theory posits that omega-3 long chain polyunsaturated fatty acids (particularly docosahexaenoic acid; DHA) provide membranes with properties to enable rapid electrical activity of M-cells and the rapid opening and closure of these cells [ 107 ]. Thus, a lack of DHA can affect the integrity of these neurons and result in impaired visual magnocellular function [ 107 ]. Interestingly, in the in utero environment, there is now evidence that choline is needed for the appropriate development of the visual system, especially the regulation of temporal progression of retinogenesis [ 42 ]. This is a field that would be worthy of extended study in relation to dyslexia and visual processing.

Some organizations are now beginning to specifically mention choline’s role(s) in neurodevelopment. For example, both the American Academy of Pediatrics (AAP) and the American Medical Association (AMA) have communicated the importance of maternal choline intake during pregnancy and lactation and identified that failure to provide choline during the first 1000 days post-conception could result in lifelong shortfalls in brain function, despite subsequent nutrient repletion [ 108 , 109 , 110 , 111 ]. The AAP calls for pediatricians to move beyond simply recommending a ‘good diet’ and ensure that pregnant women and young children have access to food that provides adequate amounts of “brain-building” nutrients, with choline being listed as one of these [ 111 ]. The main food groups reported as contributing to choline intake are milk, egg, and their derived products, as well as meat, grains and fish [ 112 ]. The AMA explains that during pregnancy, cognitive, neural tube and hippocampus development are dependent on adequate choline intake, and have called for prenatal vitamin supplements to contain ‘evidenced-based’ amounts of choline [ 110 ].

A recent analysis of over 180 commercial prenatal supplements identified that these varied in content, frequently only providing a subset of essential vitamins, and containing amounts that tended to be below recommendations [ 113 ]. The authors concluded that choline was only included in 40% of prenatal supplements, at a median level of 25 mg [ 113 ]. They also reported the incidence of certain physical and mental health conditions in the U.S.—9.4% for ADHD, and 2% for autism—and recognized that choline is needed for fetal brain development, potentially lowering the risk of neural tube defects and autism [ 113 ]. It was concluded that women who do not eat several eggs per week may benefit from prenatal supplements containing choline. Dosages provisionally advised in this publication—at least 350 mg of choline during the first two trimesters, and approximately 600 mg in the third trimester—were rather high [ 113 ]. EFSA advises an AI of 480 mg/day choline for pregnant and 520 mg/day choline for lactating women [ 46 ]. Furthermore, it is important to consider that tolerable upper intake levels have not been formally established for choline. Some side-effects, such as gastrointestinal disturbances and fishy body odor, have been reported in earlier studies administering higher amounts of choline (8–20 g/day) [ 114 , 115 , 116 ], though these studies are dated and not representative of the population of interest.

Overall, there is an emergent evidence base accruing in this important field. It is evident that more clinical trials are needed before firm conclusions can be drawn. This paper aimed to provide a first insight into the field. One prudent point to consider is the nature of the terminologies used in scientific papers published within this field. We are now gradually moving away from phrases such as ‘reading disorders’ or ‘reading disabilities’ and ‘autism spectrum disorder’ to revised terms such as ‘reading difficulties’ and ASC (autism spectrum condition). Some older terms may have been used in this paper, but only when referring to older studies that used such terms. In the future, greater consistency is needed in aligning with revised, modernized terminologies.

On a final note, now is the time to pay greater attention to choline from the perspective of neurodevelopment and NDD. Many countries, including the United Kingdom, do not yet have formal choline intake recommendations [ 117 ]. Clearly this is a central starting point. A generic lack of awareness about the nutrient choline and its potential role(s) in neurodevelopment and NDDs is evident [ 108 , 109 , 117 ]. Firming up choline recommendations and guidance to women of childbearing age potentially has tremendous implications for supporting the neurodevelopment of the next generation.

8. Conclusions

All taken together, choline appears to play a central role in brain development, growth, and function. An accruing body of evidence indicates that choline could have underpinning roles in the etiology of ASD, ADHD and possibly other NDDs. The origins of these conditions are multi-faceted, but can be genetic and attributed to environmental factors, including dietary exposures such as choline (in utero and beyond). Mechanisms of choline in relation to brain function and neurochemistry may be different at different life stages, e.g., in utero versus later in life, and for the variations of NDDs that exist and co-exist. Future research is needed in this important field. Choline certainly appears to be a nutrient worthy of consideration when studying neurodevelopment and NDDs.

Acknowledgments

Thank you to J. Cooper, Medical Statistician, for statistical expertise in plotting data from the Global Burden of Disease Study [ 8 ].

Funding Statement

Procter & Gamble funded E.D.’s writing of the paper. M.M. did not receive any funding for reviewing and contributing to the publication.

Author Contributions

E.D. conceptualized, researched, wrote, and edited the article. M.M. reviewed and edited the article. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

Procter & Gamble commissioned E.D. to write the manuscript.

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