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• Published: 22 August 2022

Long-lasting, dissociable improvements in working memory and long-term memory in older adults with repetitive neuromodulation

• Shrey Grover   ORCID: orcid.org/0000-0003-3168-5543 1 ,
• Wen Wen 1 ,
• Vighnesh Viswanathan 1 ,
• Christopher T. Gill 1 &
• Robert M. G. Reinhart   ORCID: orcid.org/0000-0003-2156-4633 1 , 2 , 3 , 4 , 5  

Nature Neuroscience volume  25 ,  pages 1237–1246 ( 2022 ) Cite this article

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• Biological techniques
• Cognitive ageing
• Long-term memory
• Neurophysiology
• Short-term memory

The development of technologies to protect or enhance memory in older people is an enduring goal of translational medicine. Here we describe repetitive (4-day) transcranial alternating current stimulation (tACS) protocols for the selective, sustainable enhancement of auditory–verbal working memory and long-term memory in 65–88-year-old people. Modulation of synchronous low-frequency, but not high-frequency, activity in parietal cortex preferentially improved working memory on day 3 and day 4 and 1 month after intervention, whereas modulation of synchronous high-frequency, but not low-frequency, activity in prefrontal cortex preferentially improved long-term memory on days 2–4 and 1 month after intervention. The rate of memory improvements over 4 days predicted the size of memory benefits 1 month later. Individuals with lower baseline cognitive function experienced larger, more enduring memory improvements. Our findings demonstrate that the plasticity of the aging brain can be selectively and sustainably exploited using repetitive and highly focalized neuromodulation grounded in spatiospectral parameters of memory-specific cortical circuitry.

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The world is facing many challenges due to a rapidly aging global population. The shift in age demographics is associated with considerable personal, social, healthcare and economic costs 1 . A critical factor contributing to aging-induced costs is the impairment in basic memory systems essential for activities of daily living, such as making financial decisions or comprehending language 2 . Emerging reports suggest an increased likelihood of such impairments due to the ongoing Coronavirus Disease 2019 (COVID-19) pandemic 3 . Moreover, there exists considerable variability in memory decline across individuals during aging 4 , with accelerated decline potentially predicting subsequent Alzheimer’s disease and other dementias 5 . Substantial progress in neuroscience has identified the brain circuits and networks that underpin memory capacities, and studies have suggested that the rhythmic activity of cognitive circuitry may be important for the coordination of information processing 6 . What is needed now are technologies to non-invasively isolate and augment the rhythmic activity of neural circuits, inspired by models of healthy aging, to determine whether it is possible to protect or even enhance memory function for older adults in a rapid and sustainable fashion 6 , 7 .

A challenge in improving memory function in older adults is that memory function may not be instantiated by a single cognitive mechanism. Previous research has characterized a capacity-limited working memory (WM) store for brief maintenance of information and an unlimited long-term memory (LTM) store for sustained maintenance of information 8 . Within this dual-store framework, previous research has identified both concurrent deficits 9 and selective deficits 10 in WM and LTM function with aging, using the classic immediate free recall paradigm, associating these stores with the canonical recency and primacy effects, respectively 11 . Neuropsychological research has long alluded to distinct anatomical and functional substrates of primacy and recency effects and the corresponding WM and LTM stores 11 , 12 , 13 . Differential contributions of the dorsolateral prefrontal cortex (DLPFC) and the inferior parietal lobule (IPL) have been suggested 14 . However, it is not known whether distinct rhythmic mechanisms in these regions subserve distinct memory processes during free recall. If unique rhythmic mechanisms in spatially distinct brain regions can be identified, then these brain rhythms can be independently and non-invasively manipulated using techniques such as high-definition transcranial alternating current stimulation (HD-tACS) for selectively improving memory function in older adults.

Rhythmic activity in the theta and gamma frequency ranges are thought to contribute to both WM 15 and LTM 16 function, particularly during free recall 17 . However, previous attempts at modulating these rhythms to improve memory have yielded inconsistent findings. Although there are some suggestions of improvements in WM with modulation of parietal theta rhythms 18 , changing theta rhythms in the frontal regions 7 , 19 and gamma rhythms in the parietal 20 and frontal 21 regions have yielded contradictory results. Similarly, although frontal gamma tACS has previously suggested improvements in LTM 22 , 23 , other spatiospectral combinations, such as frontal theta 24 , 25 and parietal theta 26 modulation, have shown variable effects. In addition, although modulation of gamma rhythms in the medial parietal cortex has shown some benefits to LTM 27 , causal evidence for involvement of these rhythms in lateral parietal cortices is scarce. Moreover, much of this evidence comes from studies in young adults, using paradigms targeting visuospatial memory and using conventional tACS, which has poorer spatial resolution and target engagement than techniques such as HD-tACS guided by current flow models 28 . Thus, which specific combinations of location and frequency of neuromodulation are effective for selectively improving WM and LTM function, particularly in older adults, are unknown.

Based on the balance of evidence, we tested the hypotheses that modulation of theta rhythms in the IPL would improve auditory–verbal WM function (recency effect), whereas modulation of gamma rhythms in the DLPFC would improve auditory–verbal LTM function (primacy effect) in older adults (Experiment 1). To modulate these rhythms, we applied tACS with optimal source-sink configurations of nine 12-mm ring electrodes (8 × 1 tACS) guided by current flow models to improve the focality of current flow 28 . Moreover, we sought to induce long-lasting effects by performing repetitive neuromodulation over multiple days and tested memory performance up to 1 month after intervention. Furthermore, we examined the effect of interindividual differences 4 and tested whether older individuals with lower general cognitive performance would benefit more from neuromodulation. To confirm the location specificity and frequency specificity of our hypotheses and address the conflicting findings in the field, we performed a second experiment (Experiment 2) in which we switched the entrainment frequencies in the two regions to examine the effect of gamma entrainment in the IPL and theta entrainment in the DLPFC on memory function. To explicitly test the replicability of the principal findings, we performed a third experiment (Experiment 3), similar to Experiment 1, examining the effect of gamma modulation in the DLPFC and theta modulation in the IPL in an independent sample of participants. Across these three experiments, we sought evidence for a double dissociation in the two memory stores according to the distinct spatiospectral characteristics of their underlying anatomical and functional substrates and, consequently, for selective and long-lasting improvements in memory function in older adults.

We conducted a randomized, double-blind study consisting of two sham-controlled experiments to target memory function in older adults and an additional experiment to test the replicability of the principal findings. In Experiment 1, 60 participants (Table 1 ) were randomized into three groups (sham, DLPFC gamma and IPL theta; Fig. 1 ). We used a repetitive neuromodulation protocol in which each participant received 8 × 1 tACS according to their assigned group for 20 minutes each day on four consecutive days. Gamma frequency 8 × 1 tACS was administered at 60 Hz, whereas theta frequency 8 × 1 tACS was administered at 4 Hz, following previous studies suggesting stronger benefits at these frequencies 18 , 22 . On each day, participants performed five runs of the free recall task. In each run, they encoded a list of 20 words and were asked to immediately recall the words at the end of the presentation of the list. Neuromodulation was performed through the entire duration of encoding and recall of all five lists to increase functional specificity 29 , and this procedure took approximately 20 minutes ( Methods ). We examined memory performance across the five runs as a function of the serial position of the presented words. This allowed us to isolate changes in LTM and WM, separately, indexed by the primacy and recency serial position curve effects according to dual-store models 11 . In addition to these online assessments, we evaluated memory performance offline, at baseline and at 1 month after intervention. We also determined general cognitive function, quantified using the Montreal Cognitive Assessment (MoCA) 30 , and depression symptoms, assessed using the Geriatric Depression Scale (GDS) 31 , at baseline. Experiment 2 served as a control to test the frequency specificity of the effects in Experiment 1. Here, we switched the neuromodulation frequency between the two regions of interest. Sixty older participants (Table 1 ) were randomized into three groups (sham, DLPFC theta and IPL gamma; Fig. 1 ) and proceeded similarly to Experiment 1. Experiment 3 served as a test for replication of the primary findings from Experiment 1. Here, a new sample of 30 participants was randomized into the two critical conditions of interest from Experiment 1 (DLPFC gamma and IPL theta) and received neuromodulation for only three consecutive days; as in Experiment 1, we examined memory performance at baseline and during each neuromodulation session.

The theta-rate IPL and gamma-rate DLPFC HD-tACS protocols and corresponding electric field models shown on three-dimensional reconstructions of the cortical surface. The left DLPFC and left IPL were targeted, each protocol using nine electrodes configured in a center-surround, source-sink pattern to achieve maximum focality. The location and current intensity value of each modulating electrode are shown. The DLPFC protocol included (in mA): FP1 (−0.6662), Fz (0.0739), F1 (−0.4438), AF3 (1.5892), FC3 (−0.0048), F5 (−0.2312), AF7 (−0.194), AFz (−0.3744) and EX17 (0.2513). The IPL protocol included (in mA): C3 (−0.2997), T7 (−0.3386), CP1 (−0.2975), FC5 (−0.1284), CP5 (1.5818), FT7 (−0.0852), TP7 (−0.1413), PO7 (−0.2366) and EX13 (−0.0545).

DLPFC gamma modulation selectively improves LTM

In Experiment 1, free recall performance across the five word lists administered during neuromodulation was averaged and entered into a mixed ANOVA with day (baseline, day 1, day 2, day 3, day 4 and 1 month after intervention) and serial position (primacy, middle 1, middle 2, middle 3 and recency) as within-subjects factors and group (sham, DLPFC gamma and IPL theta) as a between-subjects factor. We observed a significant day × serial position × group interaction ( F 21.4,611.5  = 3.875, P  < 0.001, η p 2  = 0.120). A follow-up mixed ANOVA examining performance between the sham and DLPFC gamma groups showed a similar day × serial position × group interaction effect ( F 10.1,384.0  = 3.064, P  < 0.001, η p 2  = 0.087). Additional follow-up analyses testing the effect of day on the serial position × group interaction showed that the differences in the sham and DLPFC gamma groups were present on day 2 ( F 3.3,126.8  = 7.228, P  < 0.001, η p 2  = 0.160), day 3 ( F 2.9,110.3  = 15.331, P  < 0.001, η p 2  = 0.287), day 4 ( F 2.8,107.0  = 10.698, P  < 0.001, η p 2  = 0.220) and 1 month after intervention ( F 2.6,100.5  = 3.435, P  = 0.024, η p 2  = 0.083). Examining the effect of serial position on the day × group interaction, we observed significant improvements in memory performance for the primacy cluster in the DLPFC gamma group with respect to sham ( F 3.6,140.4  = 7.470, P  < 0.001, η p 2  = 0.164) and no differences in any other serial position cluster ( Fs  < 2.262, ps  > 0.085). Parsing the improvements in the primacy cluster, independent-sample t -tests revealed significantly higher primacy performance in the DLPFC gamma group relative to the sham group on day 2, day 3, day 4 and 1 month after intervention (Fig. 2a , top, middle). The pattern of results remained unchanged when accounting for additional factors such as age, sex, years of education, MoCA and GDS scores as covariates (Supplementary Tables 1 –3). Exploratory analyses suggested potentially greater improvements in males than females, but these effects did not survive correction for multiple comparisons (Extended Data Fig. 1 ). The results suggest that rhythmic neuromodulation in the gamma band targeting left DLPFC preferentially improved LTM in older adults. The improvements were rapidly induced by the second day of neuromodulation, persisted on all following neuromodulation days and lasted for at least 1 month after intervention.

A mixed ANOVA was performed to examine differences in recall probabilities in each experiment with the following factors: day (baseline, days 1–4 and 1 month), serial position (primacy, middles 1–3 and recency) and groups (E1: sham, DLPFC gamma and IPL theta; E2: sham, DLPFC theta and IPL gamma). Interaction effects were parsed with follow-up ANOVAs and two-sided independent-sample t -tests. a , Mean recall probabilities plotted across serial position clusters (primacy, three middles and recency) at pre-intervention baseline, day 1, day 2, day 3, day 4 and 1 month after intervention for Experiment 1 groups: sham (top, grays, n  = 20), DLPFC gamma (middle, blues, n  = 20) and IPL theta (bottom, oranges, n  = 20) neuromodulation groups. Gray dots show individual participant data. Mean of center shows the average recall probability, and the error bars show 95% CI across participants. Asterisks identify days on which significant differences were observed among the modulation groups and serial positions during the follow-up two-sided independent-sample t -tests. These indicate significantly higher recall probability within the primacy cluster in the DLPFC group, relative to the sham group, in Experiment 1, on day 2 ( t 38  = 2.075, P  = 0.045, d  = 0.66), day 3 ( t 38  = 3.660, P  = 0.001, d  = 1.16), day 4 ( t 38  = 3.381, P  = 0.002, d  = 1.07) and 1 month ( t 38  = 2.381, P  = 0.022, d  = 0.75) timepoints and significantly higher recall probability within the recency cluster in the IPL theta group, relative to the sham group, in Experiment 1, on day 3 ( t 38  = 2.631, P  = 0.012, d  = 0.83), day 4 ( t 38  = 4.650, P  = 3.9 × 10 −5 , d  = 1.47) and 1 month ( t 38  = 2.253, P  = 0.030, d  = 0.98) timepoints. b , Mean recall probabilities as in a for Experiment 2 groups: sham (top, grays, n  = 20), DLPFC theta (middle, blues, n  = 20) and IPL gamma (bottom, oranges, n  = 20). No significant differences in mean recall probabilities were observed in Experiment 2. Comparisons within the primacy and recency cluster were hypothesis driven and were not subjected to any corrections for multiple comparisons. Comparisons within the middle position clusters were exploratory and subjected to Bonferroni correction. * P  < 0.05, ** P  < 0.01 and *** P  < 0.001. CI, confidence interval; NS, not significant.

IPL theta modulation selectively improves WM

We also examined a day × serial position × group interaction effect between sham and IPL theta groups in Experiment 1, using a mixed ANOVA. This interaction effect was significant ( F 9.0,342.9  = 3.111, P  = 0.001, η p 2  = 0.076). Follow-up mixed ANOVAs demonstrated the specific days at which the serial position × group interaction was significant. Improvements in memory performance were observed on day 3 ( F 3.6,137.3  = 5.713, P  < 0.001, η p 2  = 0.131), day 4 ( F 3.1,120.6  = 18.93, P  < 0.001, η p 2  = 0.333) and 1 month after intervention ( F 2.8,109.3  = 3.852, P  = 0.013, η p 2  = 0.092). Additional ANOVAs revealed that the day × group interaction was significant only for the recency serial position cluster ( F 2.6,100.7  = 5.116, P  = 0.004, η p 2  = 0.119) but not other position clusters ( Fs  < 1.005, ps  > 0.407). Independent-sample t -tests revealed significant improvements in the recency effect in the IPL theta group relative to sham group on day 3 and day 4 of neuromodulation, and these improvements were sustained at the 1-month post-intervention timepoint (Fig. 2a , top and bottom). The pattern of effects was not affected by inclusion of additional covariates (Supplementary Tables 1 –3). The results suggest that theta-rate neuromodulation aimed at left IPL selectively enhanced WM in older individuals without behavioral costs to other memory systems. These selective memory improvements were evident by day 3 of the intervention and lasted for at least 1 month, relative to memory performance of participants in the sham group.

Specific location and frequency combinations are necessary

Experiment 1 demonstrated improved WM function with repetitive modulation of IPL theta rhythms. However, both theta and gamma frequency rhythms contribute to WM function 32 . As a result, it is important to confirm whether WM improvements occur specifically due to theta modulation in the IPL or whether they are also possible with gamma modulation in the IPL. Likewise, it is important to confirm whether LTM improvements with DLPFC modulation are specifically due to gamma entrainment or whether theta entrainment can produce similar effects. To test these possibilities, we performed Experiment 2 following the same design as Experiment 1, except that the three experimental groups received sham, IPL gamma or DLPFC theta modulation. A mixed ANOVA with day (baseline, day 1, day 2, day 3, day 4 and 1 month) and serial position (primacy, middle 1, middle 2, middle 3 and recency) as within-subjects factors and group (sham, DLPFC theta and IPL gamma) as between-subjects factor failed to find any significant differences in the recall performance (day × serial position × group: F 25.3,721.9  = 0.535, P  = 0.971, η p 2  = 0.018; Fig. 2b ). This was not influenced by inclusion of covariates ( F 24.3,633.2  = 0.630, P  = 0.916, η p 2  = 0.024). This indicates that the improvements we observed in Experiment 1 are both location specific and frequency specific: modulation of theta rhythms in the IPL, and not gamma rhythms, improved WM without affecting LTM; and modulation of gamma rhythms in the DLPFC, and not theta rhythms, improved LTM without affecting WM. Moreover, the two different frequency conditions for a given brain region across the two experiments serve as active controls for each other. Consequently, these findings confirm that the effects observed in Experiment 1 are not due to any non-specific effect of tACS such as transretinal or transcutaneous modulation 33 but due to frequency-specific entrainment of relevant brain circuits.

Validation of sham and pre-intervention baseline controls

To test the validity of the control procedures and, thus, the strength of the principal findings, we examined the recall performance at the pre-intervention baseline timepoint across groups (Experiment 1: sham, DLPFC gamma and IPL theta; Experiment 2: sham, DLPFC theta and IPL gamma; Fig. 2a,b , ‘Baseline’ timepoint) and serial positions. A mixed ANOVA comparing these groups did not find a significant interaction effect of serial position (primacy, middle 1, middle 2, middle 3 and recency) or group (Experiment 1: sham, DLPFC gamma and IPL theta; Experiment 2: sham, DLPFC theta and IPL gamma) on performance at the pre-intervention baseline timepoint with or without covariates in either experiment ( Fs  < 0.925, ps  > 0.488). These results suggest that the three groups in each experiment did not differ in their baseline memory performance for any serial position cluster. Thus, the selective effects of neuromodulation on serial positions were not driven by any inherent differences within the three groups in either experiment. Furthermore, we tested how stable and reliable the recall performance was for serial position clusters within the sham group across timepoints in each experiment (baseline, day 1, day 2, day 3, day 4 and 1 month; Fig. 2a,b , top). A repeated-measures ANOVA examining the day × serial position interaction effect within the sham group did not show any significant differences with or without covariates in either experiment ( Fs  < 1.603, ps  > 0.135). Together, these results demonstrate the stability and reliability of memory performance during the pre-intervention baseline across different groups of participants and within the same group of participants over different timepoints of assessment lasting more than 1 month, which together strengthen confidence in the validity of the control procedures and the resulting tACS improvements.

Four-day improvement rate predicts benefits 1 month later

Having established the location specificity and frequency specificity of the memory improvements, we next explored factors that predict sustainable effects. We evaluated the rates of improvement in LTM (primacy) and WM (recency) over the 4-day intervention in Experiment 1. Of the 20 participants in the DLPFC gamma group, 17 (85%) showed a positive rate of primacy improvements over the 4 days. Similarly, of the 20 participants receiving IPL theta modulation, 18 (90%) showed a positive rate of recency improvements over the 4 days. By modeling these data using linear regression, we observed a significantly higher mean rate of improvement for primacy over 4 days of DLPFC modulation relative to sham and for recency during IPL modulation relative to sham (Fig. 3 ), but the reverse was not true. Neither recency in the DLPFC gamma group nor primacy in the IPL theta group were significantly different relative to sham after Bonferroni correction (Fig. 3 ). Strikingly, the rate of improvement over the course of the intervention was highly predictive of post-intervention memory benefits: participants with greater primacy improvement rates during DLPFC modulation showed the largest primacy benefits at 1 month ( r 18  = 0.817, P corr  < 0.001), and participants with greater recency improvement rates during IPL modulation showed the largest recency benefits at 1 month ( r 18  = 0.655, P corr  = 0.002) (Fig. 4a,b ). Again, the opposite was not true (DLPFC recency: r 18  = 0.243, P corr  = 0.303; IPL primacy: r 18  = 0.385, P corr  = 0.094; Pearson test, two-sided, Bonferroni correction, P corr  < 0.0125). The results indicate that not only did the overwhelming majority of older individuals experience memory improvements—selectively for WM or LTM depending on the nature of neuromodulation—the size and, thus, the sustainability of the memory improvements 1 month later were highly predicted by the speed of memory improvements during the 4-day intervention.

Mean rates of change in primacy ( a ) and recency ( b ) over the 4-day intervention shown for the DLPFC gamma group (blue, n  = 20) compared to sham (gray, n  = 20). Gray dots show individual participant data. Center of the error bars shows the mean rate of change in primacy or recency recall probabilities across the 4 days of the intervention, and the error bars show 95% CI across participants. Insets show the strength (or slope) of each participant’s linear relationship between primacy or recency recall probabilities and time over the 4-day intervention, in gray, and the average slope for the specific group and the serial position cluster is highlighted in color. Two-sided independent-sample t -tests showed differences in mean rates of change between DLPFC gamma and sham groups in the primacy cluster ( t 29.97  = 4.090, P corr  = 2.98 × 10 −4 , d  = 1.29) but not the recency cluster ( t 38  = 2.110, P corr  = 0.042, d  = 0.67). c , Similar plot as in a showing the rate of change in the primacy cluster in the IPL theta group (orange, n  = 20) compared to sham. No significant differences were observed ( t 38  = 0.225, P corr  = 0.824, d  = 0.07). d , Similar plot as in b showing the rate of change in the recency cluster in the IPL theta group (orange, n  = 20) compared to sham. Two-sided independent-sample t -tests showed significantly higher rates of change in the IPL theta group relative to sham for the recency cluster ( t 38  = 4.361, P corr  = 9.5 × 10 −5 , d  = 1.38). These analyses were exploratory and were subjected to Bonferroni correction for multiple comparisons ( P corr  < 0.0125). * P  < 0.05, ** P  < 0.01 and *** P  < 0.001. CI, confidence interval; NS, not significant.

Regression analyses were performed to test for the presence of a linear relationship across participants between the rate of change in recall performance during neuromodulation and the recall performance 1 month after the intervention. a , Scatter plot shows the speed (rate of change) of each participant’s improvement in primacy over 4 days of DLPFC gamma neuromodulation against the same individual’s primacy score 1 month after intervention in Experiment 1. Gray dots show individual participant data ( n  = 20). The solid line indicates a regression fit, and the error bands show 95% CI. This exploratory analysis identified significant, positive linear relationships between the rate of primacy improvements and 1-month primacy performance in the DLPFC gamma group ( r 18  = 0.817, P  = 1.1 × 10 −5 ). b , Scatter plot as in a for recency in the IPL theta group ( n  = 20) in Experiment 1. Significant, positive, linear relationship was observed between the rate of recency improvements and 1-month recency performance in the IPL theta group ( r 18  = 0.655, P  = 0.002). These analyses were subjected to Bonferroni correction for multiple comparisons ( P corr  < 0.0125). CI, confidence interval.

General cognitive function moderates memory improvements

Previous studies demonstrated that the effects of tACS can be modulated by baseline behavioral 34 and neural 35 states. We, therefore, examined whether memory improvements due to neuromodulation in Experiment 1 were moderated by levels of baseline cognitive function. We performed participant-wise regression of MoCA scores, memory performance at the 1-month post-intervention timepoint and the rate of change in memory performance during days 1–4 for the primacy and recency serial position clusters (Fig. 5 ). Participants with lower baseline cognitive performance in the DLPFC gamma group showed higher rates of primacy improvement over the 4-day intervention ( r 18  = −0.822, P  < 0.001; Fig. 5a ) and showed larger primacy gains at 1 month after intervention ( r 18  = −0.795, P  < 0.001; Fig. 5b ). No such relationships held for recency in the DLPFC gamma group ( rs 18  > −0.25, ps  > 0.288; Fig. 5c,d ). Moreover, participants with lower baseline cognitive performance in the IPL theta group showed higher recency improvement rates over the 4-day period ( r 18  = −0.824, P  < 0.001; Fig. 5g ) and greater recency improvements after 1 month ( r 18  = −0.499, P  = 0.025; Fig. 5h ). Consistent with previous analyses, the level of cognitive performance did not predict changes in primacy during or after IPL modulation ( rs 18  > −0.274, ps  > 0.242; Fig. 5e,f ). Thus, older participants with relatively low baseline cognition more strongly revealed the preferential nature of the gamma-rate DLPFC and theta-rate IPL modulation effects on primacy and recency, respectively. This conclusion, which suggests distinctive functions of prefrontal gamma rhythms for LTM and parietal theta rhythms for WM, was reinforced by the absence of participant-wise correlations in the sham group between baseline cognitive behavior and primacy or recency measured during or after sham ( rs 18  > 0.064, ps  > 0.79). These results suggest that the large-scale population dynamics that support memory function in older people can be differentially modulated depending on the individual level of general cognitive performance.

Participant-wise correlations between general cognitive function, quantified by MoCA scores and memory performance measures in the DLPFC gamma ( n  = 20) and IPL theta ( n  = 20) groups. Memory performance measures include ‘online’ measures quantified by the rate of change in memory performance across days 1–4 of neuromodulation and ‘offline’ measures quantified by the memory performance at the 1-month post-intervention timepoint, separately computed for the primacy and recency clusters. a , Correlation between MoCA scores and online measure for the primacy cluster in the DLPFC gamma group ( r 18  = −0.822, P  = 9 × 10 −6 ). b , Correlation between MoCA scores and offline measure for the primacy cluster in the DLPFC gamma group ( r 18  = −0.795, P  = 2.8 × 10 −5 ). c , Correlation between MoCA scores and online measure for the recency cluster in the DLPFC gamma group ( r 18  = −0.250, P  = 0.288). d , Correlation between MoCA scores and offline measure for the recency cluster in the DLPFC gamma group ( r 18  = −0.018, P  = 0.941). e , Correlation between MoCA scores and online measure for the primacy cluster in the IPL theta group ( r 18  = −0.180, P  = 0.448). f , Correlation between MoCA scores and offline measure for the primacy cluster in the IPL theta group ( r 18  = −0.274, P  = 0.242). g , Correlation between MoCA scores and online measure for the recency cluster in the IPL theta group ( r 18  = −0.824, P  = 8 × 10 −6 ). h , Correlation between MoCA scores and offline measure for the recency cluster in the IPL theta group ( r 18  = −0.499, P  = 0.025). Solid lines indicate the regression fit across participants between the MoCA scores and the neuromodulation effects (rate of change during modulation/recall probability after 1 month) in the primacy or recency clusters. Error bands show 95% CI. These hypothesis-driven analyses were not subjected to multiple comparisons correction. CI, confidence interval.

Replication of primary findings in an independent sample

We performed an additional experiment to test whether the primary observations from Experiment 1 replicate in an independent sample. Experiment 3 consisted of 30 older participants randomized to receive either DLPFC gamma or IPL theta neuromodulation during performance of the free recall task. The neuromodulation protocol followed was largely similar to Experiment 1, except that the neuromodulation was performed for three rather than four consecutive days and did not include a long-term follow-up. Memory performance was examined at baseline and during each neuromodulation session. A mixed ANOVA with day (baseline, day 1, day 2 and day 3) and serial position (primacy, middle 1, middle 2, middle 3 and recency) as within-subjects factors and group (DLPFC gamma and IPL theta) as between-subjects factor revealed significant differences in memory performance (day × serial position × group: F 7.9,220.8  = 6.315, P  < 0.001, η p 2  = 0.184; Fig. 6a ), and this effect remained significant even after accounting for covariates ( F 7.7,176.1  = 5.887, P  < 0.001, η p 2  = 0.204). Follow-up ANOVAs revealed a significant interaction between serial position and group on days 2 and 3 of neuromodulation and a significant interaction between day and group for the primacy and recency clusters (Supplementary Table 4 ). Two-sided independent-sample t -tests showed that memory performance in the primacy cluster was significantly improved in the DLPFC gamma group relative to the IPL theta group on day 2 and day 3 of neuromodulation (Fig. 6a , top). Performance in the recency cluster was significantly higher in the IPL theta group relative to the DLPFC gamma group on day 3 of the intervention (Fig. 6a , bottom). These results parallel observations from Experiment 1 (Fig. 2a , left). Baseline performance did not differ between the two groups (Supplementary Table 4 ), thus ruling out non-specific between-group differences. Examining the relationship between baseline cognitive function and memory performance, we found that individuals with lower MoCA scores in the DLPFC gamma group showed better memory performance at day 3 only in the primacy cluster ( r 13  = −0.672, P  = 0.006; Fig. 6b,c ), whereas those with lower MoCA scores in the IPL theta group showed better memory performance on day 3 only in the recency cluster ( r 13  = −0.618, P  = 0.014; Fig. 6d,e ), similar to the findings in Experiment 1 (Fig. 5 ). Together, these observations in an independent sample of participants replicate the primary findings of Experiment 1, further strengthening confidence in the inferences drawn from them.

a , Mean recall probabilities plotted across serial position clusters on all measurement days for Experiment 3 groups: DLPFC gamma (top, blues, n  = 15) and IPL theta (bottom, oranges, n  = 15). Gray dots show individual participant data. Mean of center shows the average recall probability, and the error bars show 95% CI across participants. Following mixed ANOVAs (see text), two-sided independent-sample t -tests identified significant differences in recall probability across days, groups and serial positions (see asterisks). Participants in the DLPFC gamma group showed higher recall probability within the primacy cluster on day 2 ( t 28  = 2.2, P  = 0.037, d  = 0.80) and day 3 ( t 28  = 4.467, P  = 1.25 × 10 −4 , d  = 1.63). Participants in the IPL theta group showed higher recall probability within the recency cluster on day 3 ( t 28  = −2.868, P  = 0.008, d  = 1.05). Comparisons within the primacy and recency cluster were hypothesis driven and were not subjected to any corrections for multiple comparisons. Comparisons within the middle position clusters were exploratory and subjected to Bonferroni correction. * P  < 0.05, ** P  < 0.01 and *** P  < 0.001. NS, not significant. b , Participant-wise correlations between MoCA scores and memory performance in the primacy cluster on day 3 of neuromodulation in the DLPFC gamma group ( r 13  = −0.672, P  = 0.006). Similar correlations are shown for the recency cluster performance on day 3 in the DLPFC gamma group ( r 13  = −0.363, P  = 0.183) in c , for the primacy cluster performance in the IPL theta group ( r 13  = −0.302, P  = 0.274) in d and for the recency cluster performance in the IPL theta group ( r 13  = −0.618, P  = 0.014) in e . Gray dots indicate individual participant data. Solid line indicates a regression fit, and the error bands show 95% CI across participants. These hypothesis-driven regression analyses were not subjected to multiple comparisons correction. CI, confidence interval.

We present evidence for selective improvements in WM and LTM in older adults through dissociable spatiospectral entrainment of brain rhythms, and the improvements are sustained for at least 1 month after intervention. Experiment 1 showed that selective changes to WM and LTM function are possible through entrainment of theta rhythms in the IPL and gamma rhythms in the DLPFC, respectively. Experiment 2 showed that switching the modulation frequencies between the two regions did not produce any benefits. Consequently, it is the combination of anatomical location and rhythmic frequency that determines the appropriate substrate for memory improvement. Moreover, it confirmed that the improvements observed during Experiment 1 were due to entrainment of functionally specific brain circuits and not due to non-specific effects such as transretinal or transcutaneous stimulation 33 . In addition, we observed greater improvements in individuals with poorer cognitive function. These findings were further replicated in an independent sample in Experiment 3. We further found that the speed with which the memory function improves during the intervention predicts memory strength 1 month after the intervention, thus yielding an important metric to measure treatment responsiveness in future studies. Together, these findings suggest that memory function can be selectively and sustainably improved in older adults through modulation of functionally specific brain rhythms.

The specificity with which distinct rhythmic neuromodulation protocols affected different memory functions may seem surprising given the literature documenting general involvement of both frontal and parietal regions and both theta and gamma rhythms to WM and LTM function 36 , 37 . This is particularly the case because neuromodulation was performed during both encoding and recall of all words presented during a list. Our findings strongly suggest that our interventions manipulated two distinct cognitive operations. Following the dual-store framework, we hypothesize that IPL theta modulation improved WM operations. However, unlike in previous neuromodulation studies with visuospatial memoranda 18 , we do not think that IPL theta modulation improved WM capacity per se. If that were the case, then improvements in memory performance would have also been observed in some middle position clusters in addition to the recency cluster. We also do not expect increases in general attention function with IPL theta modulation. Although parietal theta rhythms are hypothesized to facilitate attentional sampling 38 , there is little evidence to suggest changes in attention with parietal theta entrainment 39 . Instead, we propose that IPL theta modulation may have facilitated the temporal segregation between successive memory representations, minimizing interference among them 15 . Moreover, theta rhythms are also known to facilitate temporal context-mediated recall 40 , potentially reflecting a common neurophysiological mechanism underlying preserved maintenance and context-based retrieval of WM representations. Intrinsic limitations on the WM capacity, unaffected by neuromodulation, may constrain these improvements to only the later words in the list, thereby only improving the recency cluster. If so, then these findings may reflect an additional approach for non-invasively improving WM function within the influential theta–gamma cross-frequency coupling theory 15 , besides changing memory capacity 18 . The possibility that, although IPL theta modulation may have facilitated maintenance and recall of later list items, it may not have improved the transfer of previously presented information to LTM, may have further contributed to the selectivity of effects. This could be due to the presence of distinct encoding mechanisms for the two memory stores, a possibility supported by a recent transcranial magnetic stimulation (TMS) study 14 . Alternatively, transfer of representations between the two memory systems may involve separate executive control processes 41 that were unaffected by the current neuromodulation design. Consequently, IPL theta modulation may not have affected memory representations in the primacy cluster. Instead, improvements in the primacy effect emerged selectively with DLPFC gamma modulation. This protocol may have selectively improved the ability to retrieve the representations separately encoded or transferred to LTM, by potentially affecting hippocampus and other temporal lobe structures 42 , which also simultaneously exhibit gamma activity during delayed recall 36 . A previous neuromodulation study, although examining memory function in young adults with single-session conventional tACS, aligns with this proposal 22 . Thus, although both theta and gamma rhythms, and both DLPFC and IPL regions, are known to generally contribute to WM and LTM performance, they may index distinct cognitive processes that selectively underlie the dissociable improvements observed in the current study.

The findings of the present study also contribute to the debate surrounding theoretical models of free recall. Segregated neural bases of primacy and recency effects have been a hotly debated topic in neuropsychology with conflicting evidence 14 , 43 . The selective modulation of primacy and recency effects observed in the current study support distinct underlying mechanisms, in agreement with the dual-store models 11 and neuropsychological observations 12 , 13 . However, our findings, at present, are not incompatible with alternative models of free recall. For instance, one theory attributes primacy effects to ‘long-term working memory’ in which long-term storage and retrieval operations support WM function contingent upon expertise-dependent retrieval structures 44 , 45 . This view is not inconsistent with the aforementioned hypothesis that DLPFC gamma neuromodulation may have affected retrieval from LTM, albeit— in this view—in service of WM. A way to disambiguate between these two perspectives is to use the method of personalization to modulate expertise 45 , in which case this theory would predict a stronger effect of DLPFC gamma neuromodulation in the presence of stronger expertise-dependent retrieval structures. Furthermore, although the contextual retrieval theories are not designed to explain primacy effects, deficits in primacy effects in older adults have been attributed to attentional processes 46 , which, in turn, are associated with DLPFC gamma activity 47 . It is possible that DLPFC gamma neuromodulation may have further enhanced the intrinsic gradient in the efficiency of encoding mechanisms with benefits to early events in a series 46 . Notably, increased gamma activity in the temporal lobe is associated with this effect 17 . As discussed above, DLPFC gamma neuromodulation may have led to downstream effects on gamma activity in the temporal lobe structures 42 , enhancing the primacy effect. Whether DLPFC gamma neuromodulation specifically affects LTM retrieval processes or attentional mechanisms can be potentially addressed through a granular analysis of memory performance within the primacy cluster. For instance, the LTM retrieval account predicts an additive shift to memory performance with increasing serial position in the primacy cluster due to similar benefits to retrieval processes at all serial positions, whereas the attentional account predicts a reduction in the slope of memory performance as a function of the serial position, thereby reflecting a stabilization in sustained attention 46 . The success of the neuromodulation protocol in selectively manipulating the primacy effect will be a powerful tool to test these competing predictions. Future studies that are sufficiently powered to systematically test these hypotheses can disambiguate between these competing predictions to refine and reconcile the various theories of free recall.

This work contributes to the growing literature that suggests potential clinical benefits for memory function in older adults with non-invasive techniques 7 . The protocols used in the current study demonstrate that memory function can be selectively improved for at least 1 month after a 4-day intervention. These long-lasting effects may arise due to neuroplastic changes 48 after phase-locking of intrinsic brain rhythms with tACS 49 . In addition, these findings suggest that functional differentiation, which typically reduces with aging 50 , can be promoted through functionally specific neuromodulation. Findings from the present study may motivate several lines of investigation to further examine their clinical potential. For instance, future studies should examine the generalizability of these findings to different cognitive paradigms spanning memory function across various sensory domains and replicate them in larger study samples. Moreover, how to promote sustainable effects that go beyond the 1-month duration observed in the current study needs to be determined. Personalization of the neuromodulation protocol according to individual anatomical and functional characteristics is one possible approach 6 . In addition, the specific frequency within the theta and gamma ranges, the number and duration of modulation sessions, the optimal gap between successive sessions and the interaction of baseline cognitive and neural function with these metrics can be systematically varied to determine the most optimal modulation designs. Furthermore, in addition to MoCA, future studies should use more comprehensive neuropsychological assessments to quantify baseline cognitive function and its association with tACS-induced improvements. Finally, beyond potential benefits to healthy older adults, the translational implications for people with neuropsychiatric and neurodegenerative disorders, particularly those with selective memory deficits 10 and at risk for dementia 5 , should be examined. Findings from the present study serve as a stepping stone toward investigating these questions of clinical interest.

Participants

Participants were recruited from the greater Boston metropolitan area via advertisements on local and electronic bulletin boards. In total, 156 older participants provided informed written consent to procedures approved by the Boston University Institutional Review Board. Four participants (three from Experminent 2 and one from Experiment 3) were lost to attrition, and two participants (from Experiment 1) voluntarily withdrew before completing the study. Data on the remaining 150 participants (Experiment 1, n  = 60; Experiment 2, n  = 60; Experiment 3, n  = 30) at all timepoints were analyzed. Inclusion criteria included participants aged 65 years or older, native or fluent in English and normal or corrected-to-normal vision and hearing. Exclusion criteria were any metal implants in the head; implanted electronic devices; history of seizure, stroke, neurological problems or head injury; current psychiatric or neurological disorders; substance abuse; skin sensitivity; claustrophobia; smoking; psychotropic medication; left-handedness; and severe tinnitus. At baseline, participants’ depressive symptoms were assessed using the GDS 31 , and general cognitive performance was assessed using the MoCA 30 . Participants’ demographics and neuropsychological data are summarized in Table 1 . Across the three experiments, the racial and ethnic distributions of the participants were as follows: 11% African American, 55% Caucasian, 33% Asian and 0.7% Native American/Pacific Islander; 3% of the participants identified as Hispanic. All participants were compensated $15 per hour.

The necessary sample size was estimated from a pilot experiment using a sample of 24 participants (DLPFC gamma n  = 8; IPL theta n  = 8; sham n  = 8). By conservatively pooling mean difference and s.d. values in behavioral responses between active and sham conditions for day 3, day 4, and 1-month timepoints, we estimated Cohen’s d effect size based on independent-sample two-tailed t- tests (recency IPL theta versus sham, ds  > 0.94; primacy DLPFC gamma versus sham, ds  > 0.92). We found that a sample size of 20 participants was sufficient to detect an effect of the same magnitude with 80% power at the P  = 0.05 significance level.

Stimuli and procedures

We conducted two randomized, double-blind, sham-controlled behavioral experiments (Experiments 1 and 2) involving four consecutive days of HD-tACS in an 8 × 1 source-sink configuration, and an additional randomized, double-blind replication experiment involving three consecutive days of HD-tACS in the same configuration (Experiment 3). Participants were randomly assigned to one of three (Experiments 1 and 2) or two (Experiment 3) neuromodulation groups (Experiment 1: sham, DLPFC gamma and IPL theta; Experiment 2: sham, DLPFC theta and IPL gamma; Experiment 3: DLPFC gamma and IPL theta) using block randomization stratified by age and baseline general cognitive performance. WM and LTM functions were evaluated at baseline before the intervention, during each 8 × 1 tACS session (that is, on day 1, day 2, day 3 and day 4 in Experiments 1 and 2; day 1, day 2 and day 3 in Experiment 3) and 1 month after the last day of the intervention (Experiments 1 and 2).

Experimental task

On each test day, participants performed a classic immediate free recall task consisting of five lists of 20 unrelated English high-frequency words, ranging from four to 12 letters in length. The words were drawn from the Penn Electrophysiology of Encoding and Retrieval Study word pool consisting of 1,638 words with clear meaning that could be reliably judged in size and animacy encoding tasks 51 . Words with extreme values along frequency, concreteness and emotional valence dimensions were removed to create a relatively homogenous word pool. For each participant, 30 lists of words were randomly assigned to one of six test days (five lists/100 words per day). During each experiment, words were read aloud to the participant one at a time at a rate of 1.5–2 seconds per word with an inter-word interval of approximately 2 seconds. Immediately after the presentation of each list, participants freely recalled as many words as they could within a 2-minute period. Two experimenters independently noted the remembered words and their serial position. Task duration was approximately 18 minutes. Data were collected electronically in Excel (version 16.16.27) by two experimenters, independently.

The alternating current was non-invasively delivered using an M×N nine-channel high-definition transcranial electrical current stimulator (Soterix Medical). A BrainCap (Brain Vision) embedded with high-definition plastic holders consisted of nine 12-mm-diameter Ag/AgCl ring electrodes, filled with conductive gel. The choice of DLPFC and IPL targets for modulating LTM and WM, respectively, was based on previous research 14 . Electric field modeling using HD-Targets (version 3.0.1, Soterix Medical) guided electrode number, location and intensity for each montage (see Fig. 1 for neuromodulation parameters). The left DLPFC target (Brodmann’s area 9) corresponded to the following coordinates determined from neuroimaging research: x  = −31, y  = 44 and z  = 25 52 . The left IPL coordinates, x  = −42, y  = −54 and z  = 42 (Brodmann area 40), corresponded to the left supramarginal gyrus 53 . A bipolar sinusoidal alternating current was applied at 60 Hz for DLPFC targeting and at 4 Hz for IPL targeting in Experiments 1 and 3 and at 60 Hz for IPL targeting and 4 Hz for DLPFC targeting in Experiment 2. The modulation intensity was chosen to induce a minimum voltage gradient of 0.2 volts per meter (V/m) in the targeted regions while staying within established safety guidelines. The choice of modulation intensity was also constrained by meta-analysis research showing that tACS studies using intensities above 1 mA have a greater probability of enhancing performance 54 . With these considerations, electric field modeling with specified cortical targets and the 8 × 1 source-sink electrode design determined 1.58 mA, peak-to-peak, as maximal net intensity at the scalp. All participants tolerated the intervention well, and no adverse events were reported.

We took several steps to ensure that information about the experiments would not bias the results according to previously established methods 7 , 29 , 34 , 41 , 55 , 56 , 57 . First, Experiments 1 and 2 were sham-controlled. The passive sham protocol followed the same procedure as active neuromodulation but, critically, lasted only 30 seconds, ramping up and down at the beginning and end of the 20-minute period, reproducing the warming and poking sensations participants commonly endorse and then habituate to during active neuromodulation 29 . Such sham procedures are considered the gold standard in non-invasive neuromodulation research. Second, in addition to passive sham, Experiment 1 benefited from active control procedures implemented throughout the study 7 . Both DLPFC gamma and IPL theta protocols in Experiment 1 delivered the same modulation intensity. Moreover, the DLPFC theta and IPL gamma protocols in Experiment 2 targeted the same cortical targets in Experiment 1 at the same modulation intensity but at opposite frequencies. These active control procedures built within and across the two experiments effectively eliminated potential confounds associated with shunting or peripheral co-stimulation, such as transretinal or transcutaneous stimulation 33 , and ensured robust inferences about the location specificity and frequency specificity of any observed effects. Third, we performed Experiment 3 to replicate the principal findings from the conditions of interest in Experiment 1 (DLPFC gamma and IPL theta) in a new sample of participants. Converging findings from both experiments would engender confidence in the robustness of the inferences. Fourth, the present experiments also benefited from a pre-intervention baseline control condition. We were able to examine the stability and reliability of recall performance at each position cluster within the sham group across timepoints in Experiments 1 and 2. Moreover, we were able to examine the pre-intervention baseline recall performance across modulation groups to eliminate potential confounds related to between-group differences. Fifth, we used a double-blind method in which the participant and both experimenters performing data collection were blinded to the experimental manipulation. An additional experimenter set the mode (for example, active or sham) on the neuromodulation machine but, otherwise, did not interact with the participant or the experimenters who performed data collection. Sixth, all testing was conducted in a sound-attenuated, electrically shielded chamber. Seventh, the experimental designs were between-participants to avoid potential carryover effects from different neuromodulation protocols, which is important in multi-day applications. Eighth, we confirmed that participants were blinded to the presence of the neuromodulation. After each test day, we administered a safety questionnaire 58 and visual analog scale 59 , which included questions regarding attention, concentration, mood, vision, headache, fatigue and skin sensations under the modulating electrodes. Scores on these ratings did not significantly differ between groups (Experiment 1: Fs 2,57  < 0.362, ps  > 0.698, n  = 60; Experiment 2: Fs 2,57  < 2.106, ps  > 0.131, n  = 60; Experiment 3: Fs 1,28  < 1.135, ps  > 0.296, n  = 30; one-way ANOVA). In addition, all participants were asked at the end of each experiment whether they could guess whether they were participating in an active or sham procedure and were at chance levels (Experiments 1 and 2: 33%; Experiment 3: 50%).

Data analysis

Consistent with prior research 14 , serial position effects were examined by collapsing the 20-word lists into four-word clusters of primacy (serial positions 1–4), three middles (5–8, 9–12 and 13–16) and recency (17–20). Mean recall probability was computed across lists for each cluster, participant and modulation group. Given the five serial position clusters, six measurement timepoints (baseline, days 1–4, 1 month after intervention) and three groups (Experiment 1: sham, DLPFC gamma and IPL theta; Experiment 2: sham, DLPFC theta and IPL gamma), 90 distributions of mean recall probability across participants, were examined in Experiments 1 and 2. Similarly, given the five serial position clusters, four measurement timepoints (baseline and days 1–3) and two groups (DLPFC gamma and IPL theta), 40 distributions of mean recall probability across participants, were examined in Experiment 3. We first examined whether the data were normally distributed to determine their appropriateness for parametric statistical tests. Although the Shapiro–Wilk test for normality was significant in a minority of distributions (25/90 in Experiment 1; 21/90 in Experiment 2; 2/40 in Experiment 3), the skewness statistic overwhelmingly lay between −1.96 and 1.96 (89/90 distributions in both Experiments 1 and 2, 40/40 in Experiment 3), which does not indicate a significant departure from normality 60 , 61 , 62 . Accordingly, we proceeded with parametric mixed and repeated-measures ANOVAs to test our hypotheses about selective effects of the modulation group on memory recall probability according to the serial position and measurement day. An omnibus mixed ANOVA was used to test the presence of a significant interaction effect of the within-subjects factors serial position (primacy, middle 1, middle 2, middle 3 and recency) and day (Experiment 1 and 2: baseline, day 1, day 2, day 3, day 4 and after 1 month; Experiment 3: baseline, day 1, day 2 and day 3) and between-subjects factor of group (Experiment 1: sham, DLPFC gamma and IPL theta; Experiment 2: sham, DLPFC theta and IPL gamma; Experiment 3: DLPFC gamma and IPL theta). If a significant interaction effect was observed, then follow-up mixed ANOVAs were performed to compare the group × serial position × day interaction between pairs of groups. Follow-up mixed and repeated-measures ANOVAs and two-tailed independent-sample t -tests were conducted to parse the specific serial position and days at which significant differences were observable between the two given groups. For verification of control procedures, a repeated-measures ANOVA was performed within the sham group in Experiments 1 and 2 testing the serial position × day interaction to ensure the reliability and stability of repeated recall measurements. Moreover, a mixed ANOVA testing the main and interaction effects of serial position and group at the baseline timepoint was performed to ensure that the groups did not differ in memory performance at baseline in any experiment. Additional control analyses included covariates including age, sex, years of education, MoCA and GDS scores to ensure that the observed effects were not influenced by these demographic and clinical characteristics. In an exploratory analysis, we included biological sex as an additional factor in a mixed ANOVA to examine sex differences in the group × serial position × group interaction. In another exploratory analysis, we used mean rate of change in primacy or recency recall probability over the 4-day intervention as a dependent variable and tested for differences between groups in Experiment 1 (DLPFC gamma versus sham and IPL theta versus sham) using independent-sample t -tests (two-sided), Bonferroni-corrected for multiple comparisons ( P corr  < 0.0125). We also examined whether an individual’s mean rate of change induced by DLPFC or IPL modulation later predicted their primacy or recency recall performance at 1 month after intervention using regression analyses in Experiment 1 ( n  = 20, Pearson test, two-sided, Bonferroni correction, P corr  < 0.0125). Before these analyses, we confirmed the appropriateness of these parametric procedures by examining the skewness of the rate of change distributions across participants and 1-month post-intervention memory scores across participants, for both primacy and recency clusters. Finally, for each modulation group in Experiment 1 (DLPFC gamma, IPL theta and sham), regression analyses were used to examine relationships between individual cognitive performance measured by mean MoCA scores and the rate of primacy and recency change over the 4-day intervention as well as recall performance at 1 month after intervention. To test whether these relationships between memory performance and baseline individual cognitive function replicate, we performed regression analyses between MoCA scores and the recall performance of the primacy and recency clusters on the last day of assessment (day 3 of the intervention) in Experiment 3. Data were analyzed using SPSS version 27 software.

Partial eta squared ( η p 2 ) values and Cohen’s d effect sizes are reported for the ANOVA and independent-sample t -test analyses, respectively, to facilitate comparison between studies and promote replication.

Reporting summary

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

Data availability

The data used for analysis in this study are freely and permanently available on Open Science Framework ( https://osf.io/g4wcq/ ).

Code availability

No custom codes were used for the experiment or the primary analyses.

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Acknowledgements

This work was supported by grants from the National Institutes of Health (R01-AG063775 and R01-MH114877) and a generous gift from an individual philanthropist awarded to R.M.G.R. We thank C. Willing and B. Lahner for assistance with data collection and X. (P.) Cheng and the anonymous reviewers for their thoughtful feedback on the manuscript.

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Shrey Grover, Wen Wen, Vighnesh Viswanathan, Christopher T. Gill & Robert M. G. Reinhart

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Conceptualization: R.M.G.R. and S.G.; data acquisition: R.M.G.R. and V.V.; data analysis and interpretation: R.M.G.R., S.G., W.W., V.V. and C.T.G.; writing—original draft: S.G. and W.W.; writing—review and editing: R.M.G.R., S.G., W.W., V.V. and C.T.G.; funding acquisition: R.M.G.R.; supervision: R.M.G.R.

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Extended data

Extended data fig. 1 differences in memory performance according to biological sex in the dlpfc gamma group in experiment 1..

Exploratory analyses examining the impact of biological sex showed a significant interaction effect of serial position x group x biological sex ( F 6.1,164.7 = 6.139, p = 7 × 10 −6 , η p 2 = 0.185) in Experiment 1 (N = 20 in the DLPFC gamma group, N = 20 in the IPL theta group, and N = 20 in the sham group). Follow-up analyses showed that the serial position x biological sex interaction was significant in the DLPFC gamma group ( F 2.4,43.2 = 19.160, p = 2.86 × 10 −7 , η p 2 = 0.516) but not in the IPL theta and sham groups ( Fs < 1.754, ps > 0.173). Independent samples t-tests were performed to compare the memory performance for a given serial position on a given day between males and females in the DLPFC gamma group. Better primacy performance was observed among males in the DLPFC gamma group than females on day 2 ( t 18 = 2.619, p = 0.017, d = 1.177), day 3 ( t 18 = 2.288, p = 0.034, d = 1.028), day 4 ( t 18 = 3.151, p = 0.006, d = 1.416), and 1 month ( t 13.4 = 2.477, p = 0.027, d = 1.029) timepoints. Other trends observed were improved performance in males on day 2 of neuromodulation, evident in the middle 1 ( t 18 = 2.490, p = 0.023, d = 1.119) and the middle 3 ( t 18 = 2.136, p = 0.047, d = 0.960) clusters, and better performance among females at the offline timepoint 1 month after intervention in the middle 2 ( t 18 = −2.226, p = 0.039, d = −1.001) and recency ( t 18 = −2.448, p = 0.025, d = −1.1) clusters. However, none of these effects survived correction for multiple comparisons (Bonferroni correction; p cutoff = 0.0017). Data are represented as mean values +/− S.E.M. across participants.

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Grover, S., Wen, W., Viswanathan, V. et al. Long-lasting, dissociable improvements in working memory and long-term memory in older adults with repetitive neuromodulation. Nat Neurosci 25 , 1237–1246 (2022). https://doi.org/10.1038/s41593-022-01132-3

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• Introduction
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Multiple pathways through which food insecurity may plausibly impact later-life cognitive outcomes and risk of dementia.

The analytical sample size is 7012 individuals, and 18 356 is the person-wave observations.

Estimates were made based on the regression model fit in the primary analysis. The analytical sample size is 7012 individuals, and 18 356 is the person-wave observations.

eTable 1. Six-Item USDA Food Security Module, Assessed in HRS Respondents in 2013

eTable 2. Association of Food Insecurity With Dementia Risk in Primary Analytic Sample

eTable 3. Association of Food Insecurity With Memory Levels and Age-Related Decline in Primary Analytic Sample

eTable 4. Association of Food Insecurity With Dementia Risk Under Different Exposure Definitions

eTable 5. Association of Food Insecurity With Memory Score Among Respondents Under Different Exposure Definitions

eTable 6. Association of Food Insecurity With Dementia Risk Under Different Definitions of the Dementia Risk Outcome

eTable 7. Association of Food Insecurity With Word Recall in Primary Analytic Sample

eTable 8. Association of Food Insecurity With Cognitive Outcomes in Primary Analytic Sample Excluding SNAP Receipt From Control Variables

eTable 9. Association of Food Insecurity With Dementia Risk and Memory Score in Complete Case Sample

eTable 10. Association of Food Insecurity With Dementia Risk and Memory Score in Models Not Controlling for Cognitive Outcomes in 2012

eMethods. Construction of Censoring Weights

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Qian H , Khadka A , Martinez SM, et al. Food Insecurity, Memory, and Dementia Among US Adults Aged 50 Years and Older. JAMA Netw Open. 2023;6(11):e2344186. doi:10.1001/jamanetworkopen.2023.44186

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Food Insecurity, Memory, and Dementia Among US Adults Aged 50 Years and Older

• 1 Department of Family and Community Medicine, University of California, San Francisco
• 2 Department of Epidemiology and Biostatistics, University of California, San Francisco
• 3 Kaiser Permanente Center for Health Research, Portland, Oregon
• 4 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
• 5 Global Brain Health Institute, University of California, San Francisco
• 6 Department of Epidemiology, Boston University, Boston, Massachusetts

Question   Is food insecurity among older adults associated with higher subsequent dementia risk and memory decline?

Findings   In this cohort study of 7012 older adults, food insecurity was associated with an increased estimated dementia risk. Food insecurity was also associated with lower memory scores and faster memory decline.

Meaning   The findings of this study suggest that food insecurity among older adults is associated with worse cognitive performance and higher dementia risk.

Importance   Despite existing federal programs to increase access to food, food insecurity is common among US older adults. Food insecurity may affect Alzheimer disease and Alzheimer disease–related dementias via multiple mechanisms, yet there is almost no quantitative research evaluating this association.

Objective   To examine whether food insecurity in older adults is associated with later-life cognitive outcomes.

Design, Setting, and Participants   This cohort study of US residents aged 50 years and older from the US Health and Retirement Study was restricted to respondents with food insecurity data in 2013 and cognitive outcome data between calendar years 2014 and 2018. Analyses were conducted from June 1 to September 22, 2023.

Exposure   Food insecurity status in 2013 was assessed using the validated US Department of Agriculture 6-item Household Food Security Module. Respondents were classified as being food secure, low food secure, and very low food secure.

Main Outcomes and Measures   Outcomes were dementia probability and memory score (standardized to 1998 units), estimated biennially between 2014 and 2018 using a previously validated algorithm. Generalized estimation equations were fit for dementia risk and linear mixed-effects models for memory score, taking selective attrition into account through inverse probability of censoring weights.

Results   The sample consisted of 7012 participants (18 356 person-waves); mean (SD) age was 67.7 (10.0) years, 4131 (58.9%) were women, 1136 (16.2%) were non-Hispanic Black, 4849 (69.2%) were non-Hispanic White, and mean (SD) duration of schooling was 13.0 (3.0) years. Compared with food-secure older adults, experiencing low food security was associated with higher odds of dementia (odds ratio, 1.38; 95% CI, 1.15-1.67) as was experiencing very low food security (odds ratio, 1.37; 95% CI, 1.11-1.59). Low and very low food security was also associated with lower memory levels and faster age-related memory decline.

Conclusions and Relevance   In this cohort study of older US residents, food insecurity was associated with increased dementia risk, poorer memory function, and faster memory decline. Future studies are needed to examine whether addressing food insecurity may benefit brain health.

The number of US residents aged 65 years and older living with Alzheimer disease and Alzheimer disease–related dementias (AD/ADRD) is expected to increase from 5.8 million in 2020 to 14 million by 2060. 1 Similarly, food insecurity, defined as a lack of consistent access to enough food for a healthy, active lifestyle, is persistent, and the prevalence in households with elderly individuals increased from 5.3% (2001) to 7.1% (2021). 2 - 4 Among adults between the ages of 50 and 59 years, the prevalence of food insecurity is estimated to be higher at 9.4% in 2021. 5 Older adults living with food insecurity are more likely to have lower nutrient intakes and experience poorer health outcomes, such as cardiovascular and metabolic diseases, increased stress and depression, and increased dementia risk. 6 - 10

The Lifecourse Health Development Framework, which explains how health trajectories develop over a lifetime, informs several plausible mechanisms by which food insecurity impacts dementia risk ( Figure 1 ). 11 First, food insecurity arises due to financial constraints, which limit access to healthful foods and contribute to a lower quality, quantity, and variety diet. 12 , 13 Next, food insecurity may lead to poor nutrition, trigger stress pathways, or increase the likelihood of poor cardiometabolic health and mental illness. Ultimately, these factors, including food insecurity, may increase the risk of cognitive decline. 9 , 14 - 18

Few studies have rigorously investigated food security in terms of its association with AD/ADRD. Earlier studies on this topic have been conducted primarily on cross-sectional data with small and selected subpopulations or have used inconsistent measures of food insecurity and later-life AD/ADRD risk. 19 A recent systematic review 20 identified only 1 longitudinal study examining food insecurity’s association with subsequent cognitive decline in older adults, with an association over a 2-year follow-up, consistent with several cross-sectional studies that also documented associations between food insecurity and cognitive decline. 21 - 25

As food insecurity may be modifiable through existing government programs (eg, the Supplemental Nutrition Assistance Program [SNAP]) and there are limited existing treatment options for dementia, it is important to evaluate whether food insecurity is associated with increased dementia risk. 26 , 27 We rigorously evaluated the association between food insecurity in later life and cognitive health, including dementia risk and age-related memory decline, in a large and diverse sample of US older adults. Our work builds on prior literature by using longitudinal data, validated measures of food insecurity and dementia risk, and robust adjustment for life course socioeconomic variables.

This cohort study used data from the Health and Retirement Study (HRS), a nationally representative, biennially fielded longitudinal survey of noninstitutionalized individuals aged 50 years and older and their spouses. 28 The HRS and its various substudies collect rich data on life course demographic characteristics, health, labor market, and socioeconomic status. This study was waived from institutional board review because it did not involve human participation by the Human Research Protection Program at the University of California, San Francisco. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We used food security status data from the 2013 Health Care and Nutrition Study (HCNS), an HRS substudy regarding health care access, food purchases, food consumption, and nutrition (N = 8073). 29 We combined HCNS data with outcome and covariate data from the broader HRS survey. Our study period was from calendar years 2013 to 2018.

Among HCNS participants, we excluded those younger than 50 years (n = 176, typically spouses of age-eligible participants) or those not included in the HRS cross-wave respondent tracker data set (n = 2). We also excluded participants who did not provide complete data on food security status (n = 501) or had missing cognition data for the entire study period (n = 382).

Food security status was assessed using the validated US Department of Agriculture (USDA) 6-Item Short Form US Household Food Security Survey Module. 30 Respondents were asked a series of questions related to food purchases and consumption over the past 12 months to determine food security status, such as whether they were able to afford the food they needed or whether they ate less due to financial constraints (full questionnaire presented in eTable 1 in Supplement 1 ). Using USDA guidelines, we summed the number of affirmative answers to the 6 questions (score range, 0-6), where higher raw scores indicate higher food insecurity levels. We then categorized the total score into 3 levels in accordance with the USDA guidelines: food secure (high and marginal statuses, scores: 0-1), low food secure (scores: 2-4), and very low food secure (scores: 5-6).

The outcomes were algorithmically defined and previously validated dementia probability and memory scores measured in the 2014, 2016, and 2018 waves of the HRS. 31 Both measures were developed specifically in the HRS by training prediction models on the Aging, Demographics, and Memory Study (ADAMS) substudy. The ADAMS substudy focused on HRS respondents aged 70 years and older in 2000 and 2002 and had participants go through a full neuropsychological battery and dementia diagnosis. 32

Dementia probability is the risk of a respondent being demented at the time of the survey. It is a composite measure that combines responses to various cognitive questions in the HRS as well as demographic information. 31 , 33 Dementia probability values range from 0 to 1, and higher values reflect higher dementia risk. Compared with the standard clinical diagnosis of dementia, the dementia probability algorithm achieved a C statistic of 0.67, indicating acceptable performance.

Memory score is a measure of a respondent’s memory-related cognitive function at the time of the survey. It is also a composite measure that combines responses from various cognitive questions and demographic information. Higher values of memory score reflect better memory performance. We standardized memory score in our sample to the 1998 memory score distribution in the HRS for ease of interpretation.

We adjusted for the following potential confounders: age at food security assessment (in 2013), sex (male and female), self-reported race and ethnicity as part of the HRS surveys (non-Hispanic Black, Hispanic, non-Hispanic White, and Other), educational level (in years, linear and quadratic terms), birthplace (by census regions), married or not married status (in 2012, the year preceding the food security assessment), age at each interview wave (centered at 70 years, linear and quadratic terms), and body mass index (self-reported, 2012). Race was included as a proxy for racialized experiences that structurally minoritized groups face, which is associated with both the exposure and outcome. The Other race category included non-Hispanic categories of Alaska Native, American Indian, Asian, Native Hawaiian, Pacific Islander, and any other self-specified race.

We also included flexible specifications of several baseline (2012) measures of income and wealth to account for confounding by socioeconomic status: total earnings; total wealth; total assets, including second home; poverty status (yes or no); mother’s and father’s years of education (in years, linear and quadratic terms); labor force status (yes or no); home ownership (categorical variable: own or buying, renting, or living rent-free with relative, employer, friends, or others), veteran status (yes or no); Social Security income; welfare benefits; veteran benefits; and SNAP benefits the household received. As these socioeconomic status measures may all be correlated, we examined the covariance matrix to evaluate collinearity and found that no confounders were correlated above 0.8. All income, wealth, and social benefits were inflation adjusted to 2018 US dollars. When covariate data were missing in 2012, we used the 2010 value instead. We also created missing indicators (1 = data missing, 0 = otherwise) for mother’s educational level and father’s educational level as missing parental education data may indicate that the respondent did not reside with their parents during childhood, which is a marker of social capital. 34 , 35 To minimize the possibility that those with lower cognition are less likely to apply for SNAP benefits, we additionally controlled for preexposure cognition, ie, dementia risk and memory score in 2012. 36

To reduce selection bias due to missing data, we assumed the missingness mechanism was missing at random and used multiple imputation chained equation to impute missing covariates (558 [7.1%] individuals for dementia risk; 556 [7.0%] individuals for memory score) and outcome data (382 [5.4%] for the entire study period, 157 [2.2%] for baseline dementia risk, and 154 [2.2%] for baseline memory score). 37 We constructed 10 imputed data sets. In each imputed data set, we estimated inverse probability weights to address censoring due to death (details in the eMethods in Supplement 1 ). 38 , 39

In each imputed data set, we estimated the food insecurity and dementia risk association using generalized estimating equations specifying an independent correlation structure and applying robust SEs to account for repeated-outcome measures. 40 - 45 Similarly, in each imputed data set, we estimated the food insecurity and memory association using a linear mixed-effects model. We modeled the food insecurity association with age-related memory decline, using an interaction between the exposure and age. We used Rubin rules to combine estimates across all imputed data sets. 46

After combining the estimates, we contextualized the food insecurity association with memory by dividing coefficients on the exposure variable by the coefficient on the age covariate in our model to translate our primary associations into years of excess cognitive aging. We plotted predicted memory values by age and food insecurity status to visualize the food insecurity and memory association.

To test whether our results were robust to different exposure specifications, we redefined food insecurity status using alternative USDA cut points: first, as a binary indicator for food secure and insecure status (food secure = high or marginal food security; food insecure = low and very low food security), and second, as a 4-level categorical variable for food secure status (high vs marginal vs low vs very low).

We also tested whether our results were robust to different outcome specifications. For dementia probability, we used 3 additional algorithmically defined measures of dementia risk developed in the HRS data by Gianattasio et al. 47 These were based on a modified Hurd algorithm, an algorithm developed using expert input to select covariates, and a machine learning–based least absolute shrinkage and selection operator–reduced logistic regression algorithm. Data on all 3 dementia risk measures were available for direct download from the HRS website. For memory score, we used immediate and delayed word recall as alternative outcomes. These scores were not algorithmically defined and were only available for direct respondents to the HRS.

Additionally, we refit our primary models by excluding SNAP benefits as a covariate. This is because SNAP benefits could potentially be endogenous with the food insecurity exposure, as there may be some temporal overlap between SNAP receipt status and the 1-year period during which food insecurity is measured. We also conducted the primary analyses in the complete case sample to assess our method of handling missing data. To acknowledge that adjusting for preexposure cognition may introduce regression-to-the-mean bias in change score analysis, we also performed sensitivity analyses without adjusting for the 2012 memory score. 48

Regression models were adjusted for age at baseline, age centered at 70 years (linear and quadratic terms), 2012 dementia risk, sex, race and ethnicity, years of education, mother’s education, father’s education (linear and quadratic terms), birthplace, marital status, self-reported body mass index, income and wealth (linear and quadratic terms), poverty status, labor force status, home ownership, amount received from food stamps, welfare benefits, veteran status, veteran benefits, and Social Security income. Regression model parameters were estimated after imputing the primary analytic sample 10 times to fill in missing values using inverse probability of censoring weights to account for potential differential attrition and combining the results from all imputed data sets using the Rubin rules.

All analyses were conducted in Stata MP, version 18 (StataCorp LLC). One of us (A.K.) reviewed the entire code as is recommended practice. 49 Analyses were conducted from June 1 to September 22, 2023. We report 2-sided 95% CIs. All statistical tests were 2-sided, with a significance threshold of P  < .05.

Our primary analytic sample included 7012 participants (18 356 person-waves; mean person-waves per respondent, 2.6). Respondents’ mean (SD) age was 67.7 (10.0) years, 4131 (58.9%) were women, 2881 (41.1%) were men, 1136 (16.2%) were non-Hispanic Black, 4849 (69.2%) were non-Hispanic White, 4357 (62.4%) were married in 2012, and mean (SD) duration of schooling was 13.0 (3.0) years ( Table ). Overall, 18.4% of the analytic sample were food insecure: 10.3% experienced low food security and 8.1% experienced very low food security. In the analytic sample, approximately 11% of individuals aged 65 years or older at baseline reported being food insecure. In contrast, approximately 28% of individuals younger than 65 years in our analytic sample reported being food insecure. Compared with those who were food secure (marginal or high), those with low and very low food security were younger, more likely to be women and non-Hispanic Black or Hispanic, had fewer years of schooling, lived in poverty, earned less, received greater welfare support, and were renters. Respondents experiencing low and very low food security were also less likely to be married.

Compared with older adults with food security, those who experienced low food security had 1.38 times higher odds of dementia (95% CI, 1.15-1.67; in log odds: 0.33; 95% CI, 0.14-0.51), and those who experienced very low food security had 1.37 times higher odds of dementia (95% CI, 1.11-1.69; in log odds: 0.31; 95% CI, 0.10-0.52) ( Figure 2 ; eTable 2 in Supplement 1 ). Translated to years of excess cognitive aging, point estimates showed that food insecurity is associated with increased dementia risk equivalent to approximately 1.3 excess years of aging.

Compared with older adults with food security, those who experienced low and very low food security had worse memory levels at age 70 years (low β = −0.04; 95% CI, −0.08 to 0.00; very low β  =  −0.06; 95% CI, −0.1 to −0.01) (eTable 3 in Supplement 1 ). Translated to years of excess cognitive aging, low food insecurity was associated with decreased cognitive levels equivalent to approximately 0.7 years of excess aging per year, and very low food insecurity was associated with 1 year of excess aging per year. Older adults with food insecurity also had a marginally faster rate of age-related memory decline (low × age β = −0.005; 95% CI, −0.008 to −0.001; very low × age β = −0.009; 95% CI, −0.014 to −0.003) compared with food-secure individuals (eTable 3 in Supplement 1 ; Figure 3 ).

Results were substantially similar (ie, point estimates and 95% CIs may vary, but conclusions are the same) under different exposure specifications (eTable 4 and eTable 5 in Supplement 1 ) and using different dementia algorithms (eTable 6 in Supplement 1 ) as well as immediate and delayed word recall scores (eTable 7 in Supplement 1 ). Estimates in models without adjusting for SNAP receipt (eTable 8 in Supplement 1 ), using complete case data (eTable 9 in Supplement 1 ), or without controlling for preexposure cognitive outcomes (eTable 10 in Supplement 1 ) were also quantitatively similar to the primary results.

This cohort study evaluated the association between food insecurity among older adults and subsequent cognitive health using validated measures from the large, diverse longitudinal US HRS. We found that individuals who experienced low or very low food security had a higher probability of dementia, worse memory level, and faster rate of memory decline compared with those who were food secure. Memory decline diverged slightly faster among the very low food security group than the low food security group. Our primary results were robust to alternative specifications of the exposure, outcome, and analytic models.

In the current study, 11% of HRS participants aged 65 years and older experienced food insecurity, which is approximately 4% higher than reported among older adult participants (age ≥60 years) in the 2020 Current Population Survey. 50 The higher prevalence of food insecurity in our data was possibly a consequence of the Great Recession (2007-2009) in this population, which is consistent with examined trends in food insecurity. 51 , 52 Current population survey data showed that nearly 9% of older adults experienced food insecurity with the onset of the Great Recession, with lingering effects into 2014. 53 We also found that individuals experiencing food insecurity were, on average, younger and had lower educational attainment compared with those with food security. These findings suggest that food insecurity is more common among the same socioeconomically disadvantaged groups who are at high risk of dementia. 54 - 57 Together, these findings highlight the role that social determinants of health may play later in life.

While much of the literature regarding food insecurity focuses on early life factors, our study is among the first highlighting the outcomes associated with food insecurity later in life. 24 , 58 - 60 Recent findings by Lu and colleagues 61 examined SNAP participation among SNAP-eligible adults—a group that is vulnerable to food insecurity—and found faster memory function decline among those experiencing food insecurity or not participating in SNAP compared with their counterparts. Our study examined food insecurity as the exposure, which builds on findings by Lu and colleagues given that SNAP participation among eligible older adults is underused compared with other segments of the population (48% compared with 86% overall in 2018). 36 , 62

Our study has strengths. Using a large and diverse data set, our study is in line with and adds evidence to the limited previous literature on food security and brain health by using both validated exposure and outcome data: the 6-item USDA Household Food Security Module and algorithmically defined, validated dementia probability and memory scores. Additionally, our outcome measures allowed us to incorporate information from both direct and proxy respondents in the HRS. Another strength is that food security was assessed before the outcome; therefore, we were able to examine exposure that temporally preceded the outcome.

Our study has limitations. A previous study using HRS data found that among SNAP-eligible adults, participants with reduced levels of cognitive function were less likely to participate in SNAP. 36 However, in an advance over earlier work, we adjusted for preexposure cognition, minimizing this potential bias. Second, residual confounding remains a possibility in our study. To mitigate residual confounding, we included variables that reflected childhood and adulthood socioeconomic status and included flexible specifications of these variables. Weight loss in subclinical AD could exacerbate the effects of food insecurity on cognitive outcomes, although we adjusted for preexposure body mass index in 2012. Another potential confounder is food insecurity status before 2013, which was not available using the 6-item questionnaire. Future studies could examine food insecurity over a longer period. In addition, clinical diagnosis of dementia was not directly observed.

Our cohort study has several policy implications. The findings suggest that food insecurity remains high among older adults. Additionally, given limited treatment options for dementia, reducing its risk by targeting modifiable risk factors, such as food security, may be necessary. Food security may be easier to modify than other potentially modifiable risk factors, such as education and exercise, as it may be addressable through existing federal programs, such as SNAP. 63 Increasing SNAP participation rates among older adults might constitute a viable population-level solution to reduce AD/ADRD disparities and improve brain health. The take-up rates of SNAP among eligible individuals varies substantially by state, ranging from 55% in Wyoming to an estimated 100% in a number of states, such as Oregon, Washington, and Illinois. 62 , 64 In general, one reason for lower uptake is high administrative burden in completing paperwork among eligible participants, which suggests that simplifying the application process might increase the take-up rate, especially for those with any cognitive impairment. 65

In this cohort study, we found that food insecurity in older adulthood was associated with increased dementia risk and faster memory decline. Our study contributes to a limited literature by capitalizing on a large and diverse sample, validated exposure and outcome measures, and longitudinal data to robustly evaluate these associations, providing evidence in support of the connection between food insecurity in older adulthood and subsequent brain health. Our findings highlight the need to improve food security in older adults and that doing so may protect individuals from cognitive decline and dementia. Bolstering SNAP by making it easier for older adults who are SNAP eligible to apply could potentially mitigate the negative association food insecurity has with brain health.

Accepted for Publication: October 10, 2023.

Published: November 21, 2023. doi:10.1001/jamanetworkopen.2023.44186

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Qian H et al. JAMA Network Open .

Corresponding Author: Aayush Khadka, PhD, Department of Family Community Medicine, University of California, San Francisco, 1001 Potrero Ave, #2211, San Francisco, CA 94110 ( [email protected] ).

Author Contributions: Drs Qian and Khadka had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Qian, Martinez, Zeki Al Hazzouri, Glymour, Vable.

Acquisition, analysis, or interpretation of data: Qian, Khadka, Martinez, Singh, Brenowitz, Hill-Jarrett, Vable.

Drafting of the manuscript: Qian, Khadka, Martinez, Singh.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Qian, Khadka, Glymour.

Obtained funding: Martinez, Vable.

Administrative, technical, or material support: Qian, Singh.

Supervision: Qian, Martinez, Vable.

Conflict of Interest Disclosures: Dr Brenowitz reported grants from the National Institute on Aging (NIA) outside the submitted work. Dr Hill-Jarrett reported receiving Neuropsychologist consulting fees for Cogstate. Dr Glymour reported receiving grants from the National Institutes of Health NIA during the conduct of the study. No other disclosures were reported.

Funding/Support: The study was supported by NIA grants RF1AG079202 (Martinez, Vable) and R01AG074351 (Vable).

Role of the Funder/Sponsor: The NIA had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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The Well-Being of the Elderly: Memory and Aging

Affiliations.

• 1 Financial Economy and Accounting Department, University of Extremadura, Badajoz, Spain.
• 2 Evolutionary and Educational Psychology Department, University of Burgos, Burgos, Spain.
• 3 Association of Developmental and Educational Psychology for Children, Youth, Elderly and Disabled People (INFAD), Badajoz, Spain.
• PMID: 32528338
• PMCID: PMC7265135
• DOI: 10.3389/fpsyg.2020.00778

The world population increases every day as a consequence of the increase in life expectancy and longevity of humans. There are several factors analyzed in the different studies that have been developed on this topic. The research carried out in this field distinguishes biological, cultural, and cognitive factors; some of them describing similar results, while many others showed antagonistic results. Our study was oriented to the accomplishment of a bibliographical revision with the objective to verify the scientific production on "memory, cognitive development, and aging linked with longevity"-international/ national studies were analyzed and identified. The method carried out was through a research in the databases: SciELO, UAM, PePSIC, LILACS, PubMed, PsycINFO, Dialnet, and Teseo; in a period of 10 years, considering the studies published between January 2008 and December 2017. From the results found at first, 16 articles were analyzed after the application of the exclusion criteria. Likewise, we analyzed the relationship of longevity with the level of studies in Spain from a group of people over 60 years of age counted in January 2017. The literature review determined that there are psycho-cultural aspects that have a decisive influence on the increase in longevity, such as the performance of activities with positive mental states, positive emotions and experiences, and the level of studies.

Keywords: cognitive development; health; longevity; memory; well-being.

Copyright © 2020 Maldonado Briegas, Sánchez Iglesias, Ballester and Vicente Castro.

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• DOI: 10.1177/070674370805300603
• Corpus ID: 27969867

Aging and Memory: A Cognitive Approach

• L. Luo , F. Craik
• Published in Canadian journal of… 1 June 2008
• The Canadian Journal of Psychiatry

278 Citations

Impairment in associative memory in healthy aging is distinct from that in other types of episodic memory, memory changes in normal and pathological aging, episodic memory in normal aging and alzheimer disease: insights from imaging and behavioral studies, aging and recognition memory: a meta-analysis.

• Highly Influenced
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Aging Influences the Efficiency of Attentional Maintenance in Verbal Working Memory.

Maintained frontal activity underlies high memory function over 8 years in aging., multiple determinants of lifespan memory differences, characterising neural signatures of successful aging: electrophysiological correlates of preserved episodic memory in older age, mind over memory, multiple determinants of lifespan memory differences access benefits you. matters, 38 references, false memories and aging, differential effects of aging on memory for content and context: a meta-analysis., feature memory and binding in young and older adults, cognitive rehabilitation in the elderly: effects on memory, working memory, comprehension, and aging: a review and a new view, aging and prospective memory: examining the influences of self-initiated retrieval processes., aging and cognitive deficits, adult age differences in memory performance: tests of an associative deficit hypothesis., the processing-speed theory of adult age differences in cognition., on the transfer of information from temporary to permanent memory [and discussion], related papers.

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9 Long-Term Memory and Aging: A Cognitive Neuroscience Perspective

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This chapter provides an updated view of what is known about aging and memory, integrating behaviorally based research with more recent neurally-based findings. It argues that with age, memory function is characterized by (1) decreased engagement of the hippocampus and other medial temporal areas; (2) relatively reliable age differences in left frontal activations, with some studies showing heightened activity and others less activity with age; and (3) bilaterality in the frontal cortex in older adults when young adults show unilateral activity.

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Frontiers for Young Minds

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Memory Loss and Aging: How Can We Use Smartphones to Better Remember?

Our brains grant us the amazing ability to remember and relive the events from the past—however, memory for these events tend to worsen as people get older. Our memories serve several important functions, helping us to guide our future actions, connect with others, and understand ourselves. As a result, memory loss can greatly impact the lives of both those who lose their memory and their loved ones. Fortunately, there are things that people can do to help support memory as we age! For example, by combining smartphone technology and findings from decades of memory research, scientists can develop new and exciting tools to improve memory. In this paper, we will describe some of our work creating and testing a smartphone application that helps older adults better remember the unique moments from their lives.

Memory And Forgetting: What Is Normal And What Is Not?

Let us try a little exercise! Take a minute to reflect upon some events from your life. Can you remember what you had for dinner yesterday? A fun day you had at an amusement park? How about when you learned about something fascinating in school?

As you reflect on these memories, you will likely find that details for these events come flooding back. This might include who was there, what you were doing, where you were, and when the event took place. The ability to remember personal events like these is called episodic memory . One famous scientist even called episodic memory “the time machine in the brain”, because it allows people to re-experience past events in the mind’s eye [ 1 ]. Episodic memories are critical for helping people make decisions in the future (e.g., “I tried hummus for the first time yesterday and it was great—I will make sure to get it next time!”), connect with others (“That was the tallest roller coaster I have ever been on—I need to make sure I tell my friend Jenny next time I see her!”), and better understand who we are (“I loved learning about the brain—I am really passionate about neuroscience!”).

Episodic memory is separate from other types of memory, such as semantic memory or procedural memory. Semantic memory is memory for facts and general knowledge about the world, such as knowing the capital city of Canada, while procedural memory is memory for how to perform actions or motor skills, such as knowing how to ride a bicycle.

One brain region that is particularly important for supporting episodic memory is the hippocampus , a seahorse-shaped structure buried about 1.5 inches deep inside the brain on each side of the head ( Figure 1 ). The hippocampus is critical for preserving episodic memories as they are first being learned. If someone’s hippocampus is not working properly, their semantic and procedural memory will be largely unaffected, but they will have difficulty forming episodic memories for new events. Interestingly, memories for events that took place when the hippocampus was healthy would likely still be remembered because older memories become less reliant on the hippocampus with time. If you want to learn more about a famous patient who helped us learn about the role of the hippocampus in memory, check out this Frontiers for Young Minds article .

• Figure 1 - This is a side view of the brain with the hippocampus in teal.
• In the box, you can see an outline of the hippocampus compared to one of a seahorse—the name hippocampus comes from the Greek word for seahorse because of their similar shape. Brain image adapted from Gray’s Anatomy of the Human Body (1918).

Forgetting is not necessarily a bad thing though—as you were thinking back on your life events a moment ago, you likely experienced some forgetting yourself. For example, do you remember what shirt you were wearing in your memory of learning something interesting at school? Forgetting is a completely normal process that is actually useful because people do not need to hold onto every single piece of information that they encounter. However, as people age into older adulthood, they may notice that their episodic memory starts to decline, making it more difficult to relive past events. This is because, after approximately age 65, the hippocampus tends to dramatically decrease in size. Episodic memory problems can be especially severe for those with conditions that affect the hippocampus, including dementias like Alzheimer’s disease. Given the importance that episodic memories hold in people’s lives, losing the ability to remember past events can make people lose confidence in themselves, isolate from others, and experience depression.

How Can People Preserve Their Memories?

The good news is that people can take steps to protect against memory loss. It is estimated that over 40% of dementias could be prevented or delayed by lifestyle changes, such as increasing exercise, improving diet, and reducing smoking and alcohol consumption [ 2 ]. Additionally, keeping engaged with new activities can improve memory and promote healthy aging.

Moreover, people can use technologies to better remember the activities they participate in. In fact, one powerful piece of technology can be found in many people’s pockets or bags—a smartphone! Smartphones can perform a wide variety of functions that can benefit memory, including keeping in contact with others, setting reminders, and making information available. One feature that people commonly use to help preserve their memories of specific events is the camera. You have probably taken a picture or video of an event that you wanted to commemorate, and with smartphones being so commonplace, many people can easily do so.

However, research suggests that people may need to be careful, because taking photos can actually impair memory. This is called the photo-taking-impairment effect , where information that is photographed is remembered more poorly than information that is not [ 3 ]. What might explain this? It could be that people pay less attention to the event itself because they are too concentrated on taking a good photo. They might also feel less motivated to focus on an event in great detail because they know they have a photo to jog their memory later—however, as we take and collect more and more photographs, it becomes harder to find a specific photo to cue a given memory.

Fortunately, using smartphones to take photos or videos of an event does not necessarily need to impair memory. For decades, scientists have been studying different strategies that people can use to improve memory. By taking what scientists know about how people best remember, scientists can actually use photos or videos to benefit memory.

For example, one important aspect to keep in mind when first trying to remember something is the level of processing , which describes how much effort and engagement a person puts into remembering. This can range from shallow to deep, and people are better able to remember information if they engage with it using deeper levels of processing. As you are reading this article, let us say that you want to remember that episodic memory allows people to remember specific personal events. If you are engaging with the material at a deep level, you will focus on how what you learn relates to other things you know, such as how episodic memory compares to other types of memory. If you are engaging with the material on a shallow level, you might focus on more superficial aspects, such as the shapes or sounds of the letters in the words “episodic memory”. Although it often takes more time and effort to engage in deep levels of processing, it is an effective strategy to boost memory.

Additionally, when people need to study material, the way they study can impact memory of what they learn. To understand this, let us pretend that you have a big test in a week, for which you must remember a lot of information. One way you might study is by trying to cram—to review everything you need to know for the test on the day before. This is referred to as massed practice , in which you review a lot of information in a single study session. On the other hand, you could break up what you need to know and study smaller amounts every day in the week leading up to the test. This is referred to as distributed practice , in which you review information in multiple study sessions spaced out over time. Massed practice might be sufficient if you only need the information for a short period of time, but distributed practice helps you retain information for much longer.

Combining Smartphone Technology And Memory Science

Our research group developed a smartphone application called HippoCamera ( Figure 2 ) to help overcome the photo-taking-impairment effect. With HippoCamera, users record and review cues for life events using key strategies and best practices from memory science [ 4 ]. This makes it different from simply using a smartphone to capture photos and videos in the typical way. When a user has an event that they wish to remember, they stop to capture a video snippet and an audio description of the event. This multistep process makes users stop to think about the event and why it is important. In this way, HippoCamera forces a deep level of processing and makes people pay more attention to the events of their lives.

• Figure 2 - (A) HippoCamera guides users to record a video snippet and an audio description of an event they wish to remember.
• (B) HippoCamera then combines these into a powerful cue, which can be replayed using effective learning strategies. (C) Our experiments showed that participants recalled over 50% more details for events that were recorded and reviewed using HippoCamera. This was accompanied by changes in how memories were stored in the hippocampus. In the figures, Early Test refers to memory during or immediately after using HippoCamera, while Delayed Test refers to memory 3 months after participants stopped using the app.

To create a memory cue, HippoCamera combines the audio description and a sped-up version of the video, providing a lot of distinctive information about the recorded event. This helps people to mentally travel back in time to re-experience it. HippoCamera puts together replay sessions that show up to five memory cues, and users can review these in their free time. Each cue is played in multiple replay sessions that are spaced out over time, meaning that HippoCamera uses the principle of distributed practice to preserve these memories for the long term. Altogether, recording and replaying events with HippoCamera can be done in a few minutes each day.

In two experiments, we had older adults use HippoCamera for either 2 or 10 weeks, to record and replay events from their daily lives. Later, when we asked them to describe these events, we found that participants were able to recall over 50% more details for events that were recorded and reviewed using HippoCamera. These memory benefits were seen even 3 months after users stopped using the app. By using functional magnetic resonance imaging to measure brain activity, we also found that reviewing memory cues with HippoCamera changed the way that participants’ memories of those events were stored in the brain. Specifically, we found enhanced activity in the hippocampus, with memories being made more distinct from one another. This means that memories were less likely to be confused with one another, making them easier to recall in great detail. Our work provides an example of how new tools can be created that combine scientific research and technology to help people improve their memories.

Summing It All Up

Memories make people who they are, so creating solutions to improve memory can significantly benefit the lives of those affected by memory loss. One way to create easy-to-use, effective, and inexpensive tools that support memory is by using the technologies that people interact with daily, like smartphones. By combining the current scientific understanding of memory with modern technology, researchers can create new and exciting innovations that complement how the memory system works, helping people to better re-experience the moments that make their lives meaningful.

Episodic Memory : ↑ Memory for specific events that people have personally experienced.

Hippocampus : ↑ A seahorse-shaped brain region that is important for supporting episodic memory.

Dementia : ↑ A term describing decline of cognitive function, including memory, language, and decision-making, that is severe enough to affect daily living. This results from diseases affecting the brain, like Alzheimer’s disease.

Photo-Taking-Impairment Effect : ↑ A phenomenon in which people show poorer memory for information that they photograph compared to information that they do not photograph.

Level of Processing : ↑ A term describing the amount of effort and engagement people put into remembering something—people are more likely to remember information when they use deep vs. shallow levels of processing.

Massed Practice : ↑ A learning strategy in which people review information in a single, long study session, like cramming for a test.

Distributed Practice : ↑ A learning strategy in which people review information in multiple, short study sessions over time. This is more effective for long-term retention than massed practice.

Conflict of Interest

BH and MB own shares in Dynamic Memory Solutions Inc., a company focused on developing digital tools to improve memory. The University of Toronto holds the ownership rights to the HippoCamera technology used to conduct the research described herein, but has given Dynamic Memory Solutions the rights to commercialize. The authors also have a patent to disclose, Patent No.: 11,397,774. No person, nor organization received any financial remuneration for the use of the HippoCamera application in the studies described here. At the time of publication, this is a research-dedicated application that we will make available to other memory scientists at no charge.

Acknowledgments

This work was supported by Project Grants from the Canadian Institutes for Health Research to MB (PJT-173336 and PJT-126003), a Scholar Award from the James S. McDonnell Foundation to MB, a Connaught Innovation Award to MB, a Centre for Aging & Brain Health Innovation (CABHI) Researcher Clinician Partnership Program Grant to MB, and an AGE-WELL AgeTech Implementation Response Program Grant (AWAIR-2022-01) to MB. MB was supported by a Canada Research Chair and a Max and Gianna Glassman Chair in Neuropsychology. BH was supported by a Postdoctoral Award in Technology and Aging and an Early Professionals, Inspired Careers in AgeTech Fellowship from AGE-WELL.

Original Source Article

↑ Martin, C. B., Hong, B., Newsome, R. N., Savel, K., Meade, M. E., Xia, A., et al. 2022. A smartphone intervention that enhances real-world memory and promotes differentiation of hippocampal activity in older adults. Proc. Natl. Acad. Sci. U. S. A. 119, e2214285119. doi: 10.1073/pnas.2214285119

[1] ↑ Tulving, E. 2002. Episodic memory: from mind to brain. Ann. Rev. Psychol . 53:1–25. doi: 10.1146/annurev.psych.53.100901.135114

[2] ↑ Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., and et al. 2002. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396:413–46. doi: 10.1016/S0140-6736(20)30367-6

[3] ↑ Henkel, L. A. 2014. Point-and-shoot memories: the influence of taking photos on memory for a museum tour. Psychol. Sci . 25:396–402. doi: 10.1177/0956797613504438

[4] ↑ Martin, C. B., Hong, B., Newsome, R. N., Savel, K., Meade, M. E., Xia, A., and et al. 2022. A smartphone intervention that enhances real-world memory and promotes differentiation of hippocampal activity in older adults. Proc. Natl. Acad. Sci. U. S. A . 119:e2214285119. doi: 10.1073/pnas.2214285119

Understanding aging brains, how to improve memory and when to seek help

Scientists have identified ways to minimize age-related changes and improve everyday memory function.

• Older Adults and Aging
• Learning and Memory

Memory and aging

Losing keys, misplacing a wallet, or forgetting someone’s name are common experiences. But for people nearing or over age 65, such memory lapses can be frightening. They wonder if they have Alzheimer’s disease or another type of dementia. Developing Alzheimer’s is a widespread fear of older adults.

The good news is that Alzheimer’s disease is not a normal part of aging. Most older adults don’t get Alzheimer’s! Fewer than 1 in 5 people age 65+ and less than half of those age 85+ have the disease. However, it is important to understand that our brains change over time, and it is helpful to be able to distinguish normal changes from those that require medical and psychological attention.

What brain changes are normal for older adults?

Although new neurons develop throughout our lives, our brains reach their maximum size during our early twenties and then begin very slowly to decline in volume. Blood flow to the brain also decreases over time. The good news is that many studies have shown that the brain remains capable of regrowth and of learning and retaining new facts and skills throughout life, especially for people who get regular exercise and frequent intellectual stimulation. Although there are tremendous differences among individuals, some cognitive abilities continue to improve well into older age, some are constant, and some decline.

Some types of memory improve or stay the same

A type of memory called semantic memory continues to improve for many older adults. Semantic memory is the ability to recall concepts and general facts that are not related to specific experiences. For example, understanding the concept that clocks are used to tell time is a simple example of semantic memory. This type of memory also includes vocabulary and knowledge of language. In addition, procedural memory, your memory of how to do things, such as how to tell time by reading the numbers on a clock, typically stays the same.

Some types of memory decline somewhat

Do you sometimes arrive at the grocery store and have trouble remembering what you are there to get? Do you occasionally have trouble remembering where you left your car in the parking lot? Or do you have difficulty remembering appointments such as what time you’re supposed to meet your neighbor for coffee? Episodic memory, which captures the “what,” “where,” and “when” of our daily lives, is to blame. Both episodic and longer term memory decline somewhat over time.

Other types of brain functions that decrease slightly or slow down include:

• information processing and learning something new
• doing more than one task at a time and shifting focus between tasks

Possible causes of memory problems

If you or a loved one is having memory problems that are more bothersome than you would normally expect, don’t assume that Alzheimer’s or another form of dementia is the culprit. Glitches in memory can be caused by many physical and psychological conditions that are reversible. Identify and treat the condition, and your memory will improve! For example, the following common conditions can lead to memory problems:

• Dehydration
• Medication side effects
• Poor nutrition
• Psychological stress
• Thyroid imbalance

It is important to discuss these and other possible causes of memory problems with your medical doctor and to have a complete medical workup. Also, ask to see a psychologist for a complete neuropsychological evaluation to rule out anxiety, depression, or other psychological stresses and to test for cognitive changes.

Tips for maintaining and improving your memory

Here is good news about our aging brains. Scientists have identified ways to minimize age-related changes and improve everyday memory function. Here are some of their tips:

Socialize. Participation in social and community activities improves mood and memory function.

Get moving.  Physical activities and exercise, such as brisk walking, help boost and maintain brain function.

Train your brain. Using mnemonic strategies to remember names improves learning and memory. (Mnemonics are tricks and techniques for remembering information that is difficult to recall: An example is the mnemonic “Richard of York Gave Battle in Vain” to remember the first letters of the colors of the rainbow in order of their wave lengths: red, orange, yellow, green, blue, indigo, and violet.)

Don’t buy into ageist stereotypes about memory decline. Studies have shown that having positive beliefs about aging can improve memory performance in older adults.

It’s difficult to gain knowledge if you can’t see or hear well.  Make sure you wear your prescription glasses or hearing aids if you have them. And have your eyes and hearing tested regularly.

Keep a sense of control and confidence in your memory. Don’t assume that little memory lapses mean you have dementia. Use memory aids to gain and maintain confidence (see Memory Aids on next page).

Avoid distractions that divert your attention. Distractions can range from trying to do several things at once to loud noises in the background. Even your thoughts can distract your attention. For example, if you’re preoccupied with a stressful job or home environment and you’re not paying attention when your friend gives you directions to her new home, you will not be able to recall how to get there.

Memory aids

Keep “to do” lists.

Keep “to do” lists and put them where you will see them often. Mark off items as you accomplish them.

Establish a routine

Establish a routine and follow it. For example, if you take your medicines at the same times every day, you are more likely to remember them.

Don’t rush. Give yourself time to memorize a new name or recall an old one.

Everything in its place

Keep everything in its place: If you always put your reading glasses in the same place, you will always know where they are. Put items that you don’t want to forget in a place where you will see them when you need to. For example, hang your keys by the exit door you use most often.

Use associations

Use associations. For example, picture an apple on top of a gate to recall Mrs. Applegate’s name.

Tag new information

Tag new information by relating it to something that you already know and that is easy to recall. Let’s say you are in your car on the way to the hardware store and you have forgotten to make a list of the five items you need. While you still remember them, relate each item to one of five pieces of furniture in your family room: a shiny new hammer on top of the TV, a role of duct tape on the seat of your favorite chair, and so on. When you get to the store, visualize the five pieces of furniture and their five items.

Keep a calendar

Keep a paper or electronic calendar of important dates. Make sure to check it a couple of times a day.

When to seek professional help

Here is an important tip: Normal memory problems do not affect your everyday living. If you forget where you put your keys, you probably just need to get better organized. However, if you forget what keys are used for or how to unlock doors, you should see a psychologist for a complete assessment and/or speak with your primary health care provider. This type of memory problem is not a normal part of aging.

Other tip-offs that a memory problem may require professional attention include:

• Forgetting how to carry out everyday tasks, such as handling money or paying bills
• Not being able to learn new things, such as how to operate a new microwave or to take an alternate route to the grocery store
• Not recalling the names of loved ones

The memory glitches that occur normally during older age are subtle and do not have to interfere with daily life. In fact, you can easily adapt to them by making lists, establishing routines, using associations, and employing other memory aids.

Alzheimer’s Disease Education and Referral Center P.O. Box 8250 Silver Spring, MD 20907-8250 (800) 438-4380

Alzheimer’s Association 225 N. Michigan Avenue, Suite 1700 Chicago, IL 60611-7633 (800) 272-3900

Eldercare Locator (800) 677-1116

Find a Psychologist

APA Office on Aging Web site

This fact sheet was developed by the APA Office on Aging and Committee on Aging, in cooperation with Elizabeth Vierck, health writer.

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One Cohort at a Time: A New Perspective on the Declining Gender Pay Gap

This paper studies the interaction between the decrease in the gender pay gap and the stagnation in the careers of younger workers, analyzing data from the United States, Italy, Canada, and the United Kingdom. We propose a model of the labor market in which a larger supply of older workers can crowd out younger workers from top-paying positions. These negative career spillovers disproportionately affect the career trajectories of younger men because they are more likely than younger women to hold higher-paying jobs at baseline. The data strongly support this cohort-driven interpretation of the shrinking gender pay gap. The whole decline in the gap originates from (i) newer worker cohorts who enter the labor market with smaller-than-average gender pay gaps and (ii) older worker cohorts who exit with higher-than-average gender pay gaps. As predicted by the model, the gender pay convergence at labor-market entry stems from younger men's larger positional losses in the wage distribution. Younger men experience the largest positional losses within higher-paying firms, in which they become less represented over time at a faster rate than younger women. Finally, we document that labor-market exit is the sole contributor to the decline in the gender pay gap after the mid-1990s, which implies no full gender pay convergence for the foreseeable future. Consistent with our framework, we find evidence that most of the remaining gender pay gap at entry depends on predetermined educational choices.

We thank Patricia Cortés, Gordon Dahl, Fabian Lange, Claudia Olivetti, Michael Powell, Uta Schönberg, as well as participants at various seminars and conferences for helpful comments. We thank Sergey Abramenko, Thomas Barden, Carolina Bussotti, Sean Chen, and Chengmou Lei for outstanding research assistance. The realization of this article was possible thanks to the sponsorship of the “VisitINPS Scholars” program. The views expressed in this paper are those of the authors only and should not be attributed to the Bank of Italy, the Eurosystem, or the National Bureau of Economic Research.

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Aging, Housing, and Macroeconomic Inefficiency

Yasutaka ogawa, jiro yoshida.

This study quantifies the macroeconomic impact of population aging with a focus on large houses owned by elderly households for bequest motives, although younger generations may leave the inherited houses vacant. A quantitative overlapping generations model incorporates age-specific mortality rates and bequest motives to generate a hump-shaped age profile for consumption and an upward-sloping age profile for housing and savings. When calibrated to the Japanese economy, the model suggests that bequest-driven housing demand raises the output level but reduces consumption, the natural rate of interest, capital allocation to the goods sector, and housing affordability. These effects are more pronounced when households intend to bequeath housing rather than financial assets and when more houses become vacant upon inheritance.

Keywords: Aging; Natural Rate of Interest; Overlapping Generations Model; Bequest Motives; Intergenerational Transfer of Housing; Japan

Views expressed in the paper are those of the authors and do not necessarily reflect those of the Bank of Japan or Institute for Monetary and Economic Studies.

Copyright © 2024 Bank of Japan All Rights Reserved.

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• Clin Interv Aging

Cognitive decline in normal aging and its prevention: a review on non-pharmacological lifestyle strategies

Blanka klimova.

1 Department of Applied Linguistics, Faculty of Informatics and Management, University of Hradec Kralove

2 Department of Neurology

Martin Valis

3 Biomedical Research Centre, University Hospital Hradec Kralove

4 Department of Chemistry, Faculty of Science, University of Hradec Kralove, Hradec Kralove, Czech Republic

The purpose of this study is to examine the effects of the selected non-pharmacological lifestyle activities on the delay of cognitive decline in normal aging. This was done by conducting a literature review in the four acknowledged databases Web of Science, Scopus, MEDLINE, and Springer, and consequently by evaluating the findings of the relevant studies. The findings show that physical activities, such as walking and aerobic exercises, music therapy, adherence to Mediterranean diet, or solving crosswords, seem to be very promising lifestyle intervention tools. The results indicate that non-pharmacological lifestyle intervention activities should be intense and possibly done simultaneously in order to be effective in the prevention of cognitive decline. In addition, more longitudinal randomized controlled trials are needed in order to discover the most effective types and the duration of these intervention activities in the prevention of cognitive decline, typical of aging population groups.

Cognitive skills play a crucial role in the daily functioning of older people. Unfortunately, some of these cognitive skills (eg, memory, problem-solving activities, or speed processing) decline in the process of aging. 1 – 4 There are several risk factors which appear to have an impact on cognitive decline. 5 These risk factors can be divided into modifiable and non-modifiable risk factors. The non-modifiable risk factors include age, race and ethnicity, gender, and genetics. 6 In fact, it has been proved that 60% of general cognitive ability is of genetic origin. 7 The modifiable risk factors mainly involve diabetes, head injuries, lifestyle, and education. 8

Furthermore, Klimova and Kuca 9 divide the non-pharmacological lifestyle intervention activities into three most influential groups which have a positive impact on cognitive decline: physical activities, healthy diet, and cognitive training. These activities have a positive effect on the maintenance of synaptic function whose loss is usually connected with toxic forms of amyloid-β protein, which can result in the onset of aging diseases such as dementia. However, thanks to the non-pharmacological activities and their intensity, this synapse and synaptic protein loss may be prevented. In addition, physical activities can contribute to the increase of vascularization, energy metabolism, and resistance against oxidative stress, which has a positive effect on cognitive functions. 10 Recent research 11 , 12 has also revealed beneficial effects of music therapies in the prevention of cognitive decline. Since there are findings 13 – 16 that certain non-pharmacological lifestyle activities such as physical activities or adherence to Mediterranean diet (MedDiet) may contribute to the prevention of cognitive aging, the purpose of this study is to examine the effects of the selected non-pharmacological lifestyle activities on the delay of cognitive decline in normal aging. This is done on the basis of the findings from the selected studies. Since pharmacological intervention is relatively costly, might have serious side effects, and impose a severe social burden, the main purpose of this review is to analyze and compare the effects of non-pharmacological activities – physical activities/exercises, adherence to MedDiet, music therapy, and cognitive training such as solving crosswords – on the prevention of cognitive decline in normal aging. 17

The methodology used in this review is based on the study of Moher et al. 18 The relevant studies were searched using key words in four acknowledged databases: Web of Science, Springer, Scopus, and MEDLINE. This review screened for studies conducted in the period from 2000 to June 2016, using the following key words: cognitive skills in normal aging AND intervention strategies; healthy older people AND cognitive decline AND physical activities; healthy older people AND cognitive decline AND physical exercises; healthy older people AND cognitive decline AND Mediterranean diet; and healthy older people AND cognitive decline AND music.

A study was included if it matched the corresponding period, from 2000 up to June 2016. The selection period started with the year of 2000 because this is the year when the studies on the research topic conducted among older people started to appear. Furthermore, the study was included if it involved healthy older people without any cognitive impairment or dementia, aged 60+ years, and focused on the topic of non-pharmacological lifestyle intervention strategies, that is, physical activities/exercises, adherence to MedDiet, music therapy, or mental activity such as solving a crossword. All studies had to be written in English to be included in this review.

This review included randomized controlled trials (RCTs), observational studies, descriptive studies, retrospective studies, theoretical articles, reviews, books, book chapters, web pages, study protocols, seminar papers, and abstracts on the research topic. The majority of the included studies were detected in the Springer database (14,641 studies), followed by Web of Science (110 studies), Scopus (61 studies), and MEDLINE (38 studies). Altogether 14,850 studies were found via the database search, and 176 from other available sources (ie, web pages, conference proceedings, and books outside the scope of the databases described above). Then, the titles of all the included studies were checked in order to confirm whether they focus on the research topic, and irrelevant studies were excluded. In addition, duplicate studies were also excluded. After this step, 119 studies remained for further analysis. Afterwards, the authors checked the content of the abstracts to verify whether the study examined the relevant research topic. Finally, 42 studies/articles were selected for the full-text analysis, out of which the findings of 30 studies are compared in the present review and the remaining 12 studies were used for the detailed analysis of the research topic ( Figure 1 ). The rigid process of selection of these studies is described in Figure 1 .

Flowchart of the selection procedure.

Altogether 12 studies focusing on the research topic were identified. Out of these studies, six exclusively focused on physical activities, two on MedDiet, two on a music therapy, one on an educational program, and one on doing crosswords. The intervention period in the studies ranged from 2 weeks to 6.5 years. The subject samples also varied; the smallest sample of subjects consisted of 21 older individuals, whereas the largest one involved 716 older adults. The efficacy of the intervention therapies focused on the prevention of cognitive decline in the studies was measured with the available validated cognitive assessment tools such as cognitive tests and/or magnetic resonance imaging. Table 1 19 – 28 provides an overview of the main findings of these studies. The results are summarized in alphabetical order of their first author name.

Overview of the 12 selected studies focused on cognitive decline and its prevention by intervention activities

Abbreviations: AD, Alzheimer’s disease; ADL, activities of daily living; CI, confidence interval; EET, endurance exercise training; EVOO, extravirgin olive oil; IPI, individualized piano instruction; MedDiet, Mediterranean diet; MMSE, Mini Mental State Examination; NA, not available; PVF, phonemic verbal fluency; RCT, randomized controlled trial; SD, standard deviation.

The findings of all studies in Table 1 exhibit positive results as far as cognitive functions (ie, executive functions, better memory, learning, attention, or verbal fluency) are concerned. Based on the evidence of the outcome measures, all studies indicate that cognitive functions can be maintained or even enhanced by intervention activities such as sport (eg, walking or aerobic exercises), music, healthy MedDiet, or doing crosswords. The findings also suggest that the immediate results may be detected after intervening for just 1 month. However, this is questionable because according to Kurz and van Baelen, 29 the minimum period for medication assessment as suggested by regulatory authorities is 24 weeks.

The findings from the selected studies in Table 1 are discussed in this section according to their representation of intervention activities. Thus, the majority of studies deal with physical activities. The results show that people who do more physical activities per day have a lower incidence of cognitive decline compared with those doing them occasionally. The same is true for their intensity. For example, Larson et al 30 claim that intensive physical activities may delay the decline of cognitive functioning. They indicate that people who exercise three times a week or more fall ill with dementia less compared with those who conduct physical exercises less than three times a week. Thus, it seems that healthy older people who do physical activities regularly and intensively at least two to three times a week may enhance their cognitive functions. However, another study performed for a period of 15 years by Sorman et al 31 among 1,475 healthy individuals aged 65 years and above shows that the positive outcomes of these physical activities may be valid just for a shorter time span, but there is a little support for the effect of the physical activities in the prevention of cognitive decline in a longer time span. This is also supported by a review, 19 whose authors report that the benefits of physical activity are small and cumulative over many years and they may be beyond resolution by RCTs.

Studies 11 , 12 , 32 , 33 also emphasize the role of music therapy, especially listening to Buddhist hymns, 12 in the improvement of cognitive competences because music may recall past memories and activate patient’s brain more as affects both the hemispheres of the brain. In fact, music comprehension is connected with one specific area of the brain that remains intact even after all other functions, such as verbal language, are impaired. 34 For example, a study 12 shows that the results obtained over a 4-month follow-up period revealed a significant difference between the experimental group and comparison group in cognitive scores. After 4 months, a significant decline in the Mini-Mental State Examination score compared with the initial score was observed in the comparison group, but not in the experimental group. Furthermore, Craig 33 suggests that music therapies should be done in groups in order for them to be effective, that is, regularly twice a week, and patients should be exposed to such kind of music that is familiar to them and in which they are engaged.

Furthermore, some studies 22 , 28 prove the benefits of MedDiet, especially if it is rich in olive oil and nuts. Recent studies 35 – 37 indicate that a proper dietary regime plays a crucial role in the maintenance of optimal synoptic function.

The findings of the analyzed studies also prove the efficacy of solving crosswords on cognitive decline 24 because learning something new and training the brain may lower the risk of cognitive decline. 10 However, they must be done several times a week. For example, Verghese et al 38 in their study confirm that if a person solves crosswords four times a week, she/he can reduce the risk of dementia by 47% compared with the person who does it only once a week.

Recent studies have revealed that the most effective type of intervention activity seems to be the one which consists of several intervention activities. 39 , 40 Nevertheless, these activities must be done on a regular basis, ideally several times a week, and people should start with them in their middle age. 41

The limitations of this review are the lack of available RCTs on the research issue and the different methodologies of the included publications such as the use of passive control groups, or no intervention group at all, or small subject samples. These insufficiencies may result in the overestimated effects of the discussed findings and have a negative impact on the validity of the reviewed studies. 42 – 44 The heterogeneity of the selected studies also prevented the authors from conducting the meta-analysis.

The results of this review indicate that all the selected non-pharmacological lifestyle intervention strategies explored appear to have positive effects on the cognitive functions in older people. These therapies are generally less costly and noninvasive in comparison with the pharmacological treatment, which is usually lengthy and brings about possible side effects. Older people also have a chance to meet with their peers while performing these preventive non-pharmacological therapies. Nevertheless, the findings of this study also suggest that future research should focus on conducting more longitudinal RCTs with larger samples of subjects in order to discover the most effective types and the duration of these intervention activities for the prevention of cognitive decline, typical of aging population groups.

Acknowledgments

This paper was supported by the project Excelence 2017 run at the Faculty of Informatics and Management of the University of Hradec Kralove and the long-term development plans of the University Hospital Hradec Kralove and University of Hradec Kralove.

The authors declare that they have no competing interests to declare in this work.

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