Protecting hippocampal structures, such as the cornu ammonis 3 and dentate gyrus (CA3/DG), that are tied to mnemonic discrimination could be a promising approach to mitigate the age-related decline in episodic memory. This study examines the potential benefits of cognitive and physical engagement, specifically language learning and moderate aerobic exercise, for maintaining hippocampal subfield integrity and memory performance. In a randomized controlled trial involving 142 healthy older adults (aged 63-78 years), we investigated the effects of a 6-month regime comprising language learning, aerobic exercise, and their combination. Participants were assigned to one of four groups: an active control, language-only, exercise-only, or combined language and exercise group. CA3/DG volume remained stable in the language-only and combined groups, while the non-language groups showed a significantly greater decrease in volume, suggesting that foreign language learning may protect against normal aging-related CA3/DG volume loss. Comparatively, the exercise-only and combined groups showed significant improvements in mnemonic discrimination performance, while the non-exercise groups did not, indicating that aerobic exercise can improve this aspect of memory in older adults. Notably, the combined group was the only group to exhibit both preserved CA3/DG volume and improved mnemonic discrimination. No evidence for a boosting effect of the combined intervention on either outcome was observed. This suggests that, while the effects of language learning and aerobic exercise may be independent at the level of measured outcomes, they provide complementary benefits when combined. Finally, the initial volume of the subiculum was found to predict changes in mnemonic discrimination performance across all groups, indicating that this region may play a potential role in episodic memory changes over time. Contrary to our expectations, changes in hippocampal subfields were not directly associated with changes in mnemonic discrimination. In conclusion, this study highlights the potentially separate roles of foreign language learning and aerobic exercise in enhancing aspects of episodic memory in older adults; future research may address whether their combination may yield broader functional benefits than either intervention alone.

Abstract Background In many countries, lifespan has been increasing faster than healthspan, leading to more years spent with late-life disease and highlighting the need for reliable biomarkers to measure biological aging. Methods We used data from the Berlin Aging Study II (BASE-II, 60–80 years of age at baseline, average follow-up 7.4 ± 1.5 years, range 3.9–10.4, n = 1,083) to compare 14 biomarkers of aging recently consented by an expert panel for the use as outcome measures in intervention studies: physiological (insulin-like growth factor 1 (IGF-1), growth-differentiating factor-15 (DNA methylation derived, DNAmGDF15)), inflammatory (high sensitivity C-reactive protein (CRP), interleukin-6 (IL-6)), functional (muscle mass, muscle strength, hand grip strength (HGS), Timed-Up-and-Go (TUG), gait speed, standing balance test, frailty phenotype (FP), cognitive health, blood pressure), and epigenetic (epigenetic clock, DunedinPACE). Cox proportional hazard regression analyses were performed to investigate their role in prediction of all-cause as well as cause-specific mortality. Results were adjusted for age, sex, lifestyle factors, and genetic ancestry. Results In adjusted models of all-cause mortality, HGS, IL-6, standing balance, cognitive health, and the epigenetic clock (DunedinPACE) statistically significantly predicted mortality, with the epigenetic clock (DunedinPACE) emerging as the strongest predictor. CRP, gait speed, IGF-1, blood pressure, muscle mass, DNAmGDF15, FP and TUG were not associated with mortality in this study. These results were corroborated in subgroup analyses stratified by cause of death. Feature selection identified a minimal biomarker set consisting of muscle mass, standing balance, and epigenetic clock (DunedinPACE) that predicted mortality with nearly the same discriminative accuracy (C-index = 0.63) as the full model including all biomarkers (C-index = 0.65). Conclusion Among the fourteen investigated biomarkers of aging, DunedinPACE emerged with the strongest and most consistent association with mortality.

1 Abstract Background Epigenetic clocks based on DNA methylation (DNAm) have emerged as promising biomarkers of biological aging, yet their associations with cognitive performance remain inconsistent. This study investigates the relationship between epigenetic age acceleration and cognitive performance in older adults using 14 DNAm clocks from five generations of development. Methods We analyzed data from the Berlin Aging Study II (BASE-II) using genome-wide DNAm profiles and cognitive assessments ascertained at baseline (T0) and two follow-up time points (T1, T2) in up to 1,014 individuals. DNAm-based age and age acceleration estimates were calculated using Biolearn and MethylCIPHER. Analyses focused on cross-sectional and longitudinal associations between DNAm clock estimates and cognitive performance, including sex-specific effects and comparisons with frailty as non-cognitive positive control. Results Among all tested DNAm clocks, DunedinPACE (a third-generation clock) showed the strongest and most consistent associations with cognitive performance. In addition, the fifth-generation SystemsAge framework also demonstrated robust associations with cross-sectional and longitudinal cognitive outcomes. In contrast, second-generation clocks (GrimAge [v2], PhenoAge) showed occasional nominal associations, while first-generation clocks (Horvath [v1], Hannum) and the causally-informed, fourth-generation clocks (e.g. YingCausAge, YingDamAge) showed no noteworthy signals. Likewise, telomere length estimated from DNAm was not strongly associated with cognitive performance in this dataset. Conclusions Our findings highlight DunedinPACE as a particularly informative biomarker for various aspects of cognitive aging, while other DNAm aging measures showed no consistent associations. Future work should further refine domain-specific epigenetic biomarkers to improve biological aging assessments and achieve a more reliable early detection of cognitive decline.

OBJECTIVES:Awareness of age-related change (AARC) reflects how individuals perceive age-related gains and losses, with positive perceptions consistently predicting better health and well-being. The Awareness of Age-Related Change theoretical framework has long highlighted physical and psychosocial health as distal antecedents of AARC, but their actual importance remains poorly understood. In this preregistered study, we empirically test this key model component. METHODS:We operationalize distal antecedents as 9-year trajectories in physical health (morbidity, grip strength, hearing, vision), cognitive functioning (recall, processing speed, working memory), and self-reported loneliness. We apply structural equation modeling to three-wave longitudinal data from the Berlin Aging Study II (N = 753, aged 61-81 at baseline, 49% female) and test if these trajectories predict late-life AARC. RESULTS:Older adults with better baseline recall and grip strength reported fewer AARC-losses, whereas those with higher baseline loneliness perceived more losses and fewer gains. Steeper declines in recall and stronger increases in morbidity were associated with heightened awareness of losses. Processing speed, working memory, hearing, and vision were not associated with AARC. DISCUSSION:Our results suggest several refinements of the Awareness of Age-Related Change framework: (a) within a given construct space (e.g., cognition), some dimensions (e.g., recall) are more strongly associated with perceiving age-related losses than others (e.g., processing speed), (b) long-term trajectories of recall memory and morbidity (rather than status alone) are particularly relevant for loss-focused awareness of aging, and (c) higher levels of loneliness are most consistently associated with awareness of age-related gains.

Abstract This tutorial seeks to facilitate the use of mobile electroencephalography (EEG) in young children. Mobile EEG allows researchers to investigate neural correlates of behavioral and cognitive processes in ecologically valid settings. While mobile EEG has been widely adopted in adult research, studies applying it to freely moving children remain scarce. However, investigating neural processes during active behavior and in interaction with movement is indispensable for advancing our understanding of neural correlates of cognitive development in early childhood. Here, we provide a practical tutorial on the collection and preprocessing of mobile EEG data from children. Drawing on experience and data from a large-scale study with toddlers, we summarize key methodological considerations, discuss common challenges and practical recommendations, and present a preprocessing pipeline developed for developmental mobile EEG data. In addition, we provide example EEG datasets from naturally moving 18-20-month-old toddlers. We argue that incorporating mobile EEG into developmental cognitive neuroscience allows researchers to (i) investigate cognitive development in naturalistic environments, (ii) examine associations among bodily movement, neural activity, and behavior, and (iii) study samples that are difficult to reach with stationary research. Highlights Mobile EEG enables investigation of neural processes during active behavior Mobile EEG in moving freely children comes with unique methodological challenges We provide practical recommendations for developmental mobile EEG studies A dataset and preprocessing pipeline from a toddler mobile EEG study are provided

Abstract Background It is an everyday observation that people of the same chronological age differ with respect to their physical and mental capacity. However, assessing these differences in biological age remains challenging. Methods Here, we aggregate 89 age-associated variables from the Berlin Aging Study II (BASE-II, n=1,631) to generate MultiAge, a new marker of biological age that summarizes information from ten domains reflecting organ health and global biological age. We then used methylation data obtained from an Illumina MethylationEPIC array and supervised machine learning to translate MultiAge into a DNA methylation signature, MultiAgeEpi (309 CpGs), which was subsequently validated in four independent external validation cohorts (KORA FF4, KORA Age, SHIP-TREND, BiDirect, total n=4,339). MultiAgeEpi results were compared with previously published epigenetic clocks (GrimAge, DunedinPACE, SystemsAge). Results We report that MultiAgeEpi showed similar, and in several cases, stronger associations with age-associated outcomes such as diabetes, metabolic syndrome, multimorbidity, frailty and mortality (q < 0.05) compared to the other clocks. Conclusions MultiAge and MultiAgeEpi thus provide a comprehensive assessment of biological age through aggregation of numerous age-associated variables and the use of the high-resolution methylomics data makes transfer of this marker to other cohorts possible.

Errors play a crucial role in learning and goal-directed behavior by triggering cognitive adjustments to optimize future task performance. One such adjustment is post-error slowing (PES), the tendency to respond more slowly after an error. In adults, PES has been associated with regions implicated in error processing, including the anterior cingulate cortex (ACC) and anterior insula. The prolonged maturation of these regions is thought to contribute to less efficient error processing in children and PES compared to adults. Additionally, while some errors may be immediately corrected, resulting in isolated errors, others may require multiple correction attempts, resulting in consecutive errors. Compared to adults, children may need more attempts to correct their errors due to the ongoing neurodevelopment of error processing. We investigated age differences in error types and in PES between children (N = 159, 8-11 years) and adults (N = 40, 20-30 years) during task switching. We tested whether individual differences in error processing-related activation contributed to PES within a subsample of children that performed the task during scanning (N = 72). Children made mostly consecutive errors, whereas adults made mostly isolated errors. PES magnitudes were larger in adults than in children. Children showed enhanced error-related activity in dorsal ACC and the anterior insula. Enhanced error-related activity in the insula was associated with better performance and reduced switch costs. These findings suggest that the neurodevelopment of error processing in late childhood contributes to the improved ability to adjust behavior following errors, and consequently to task-switching performance.

Interpersonal interaction shapes brain dynamics in ways that often elude traditional linear analyses. Using electroencephalography (EEG) hyperscanning and nonlinear dynamical methods, we investigated how neural complexity and coupling within and between brains differ between joint and solitary conditions across four ecologically valid contexts: romantic kissing, resting state, pendulum observation, and music listening. Fourteen romantic couples completed each task while EEG was recorded simultaneously. Linear mixed-effects models showed that joint conditions generally enhanced inter-brain coupling, with kissing producing the strongest effects—combining elevated coupling with increased complexity. Even in resting state, with eyes open or closed, coupling increased in the absence of external stimuli, suggesting internally driven partner-oriented synchrony. Chaotic pendulum motion selectively boosted mutual synchrony, likely via heightened attentional engagement, while music listening effects varied with musical style, sex, and the frequency range of spectral estimates of nonlinear measures. Psychological ratings of romantic bond, relational dominance, and kiss quality exhibited nonlinear, often U- or V-shaped, associations with neural measures. These findings extend social neuroscience by demonstrating that nonlinear hyperscanning approaches capture flexible “hyper-brain” network dynamics tuned to the affective, cognitive, and sensorimotor demands of real-world interaction.

BACKGROUND:The disproportionate increase in lifespan compared to healthspan over the past decades results in a growing proportion of life marked by diseases, even if incidence rates are falling in some cases. However, not everyone ages at the same pace and some people remain in good health and preserve physical and cognitive function into old age. To quantify inter-individual differences in the biological aging process, numerous indicators of biological age have been developed. METHODS:In this study, we analyzed 16 measures of biological aging including epigenetic clocks, proteomics clock, telomere length, and SkinAge, laboratory composite markers (BioAge, Allostatic Load), psychological aging, and Brain Age. These age markers were evaluated cross-sectionally as well as longitudinally in the context of age-associated outcomes covering frailty, mobility, cognitive function, depressive symptoms, autonomy in daily life, nutrition, morbidity, and chronic disease in participants of the Berlin Aging Study II (BASE-II). RESULTS:Here, we analyze longitudinal data from 1083 participants (mean age of 68.3 years at baseline, 52% women) with an average follow-up period of 7.4 years. Allostatic Load Index and DunedinPACE show the strongest and most consistent cross-sectional and longitudinal associations with age-associated phenotypes. Furthermore, both biomarkers individually increase the accuracy of a logistic regression model trained to predict incident cases of Metabolic Syndrome, high cardiovascular risk (Lifes's Simple 7) as well as incident frailty (Fried's frailty index) 7.4 years after baseline examination by up to 24 percentage points. CONCLUSIONS:Our findings support the previously shown distinction between indicators of aging and provide a comprehensive overview of their individual strengths and weaknesses in the context of wide variety of age-associated phenotypes.

This study investigates the interplay of cardiac, respiratory, and vocal activity during choral singing using recurrence quantification analysis (RQA) to capture underlying nonlinear dynamics. It was conducted to better understand how dynamic physiological and vocal subsystems interact and synchronize across different singing conditions. Our findings demonstrate significant differences in recurrence dynamics between resting and singing states and across different singing conditions. Singing altered recurrence dynamics, with increased synchronization and phase space integration compared to the resting state, especially for respiration. Subsystems exhibited distinct dynamic properties both within choir members and in interactions between them. Singing increased synchronization and reduced attractor fragmentation in the phase space, particularly for respiration. Subsystem dynamics varied by condition: singing in unison was characterized by more fragmented and irregular vocal dynamics, while singing in part led to more integrated and complex interactions. Bidirectional coupling between subsystems was observed, with the respiration and voice subsystems generally playing a leading role. The respiratory subsystem displayed stronger coupling with the vocal subsystem and often acted as the driving force in subsystem interactions. Periodic oscillatory patterns, particularly at frequencies aligning with the phrasing of the sung canon, were detected across all singing conditions, reflecting the dynamic nature of choir interactions. These findings underscore the complexity of choral singing, where subsystems interact dynamically in a context-dependent manner, with multilayered coupling mechanisms shaping overall system behavior. Our results suggest that the intricate, interwoven dynamics of choral singing can only be fully understood through detailed analysis of recurrence and coupling dynamics.

The association between the physical living environment and brain structural changes in older adults remains considerably unexplored despite its potential to benefit a rapidly-growing proportion of the world’s population. We investigated whether neighborhood-level environmental features are associated with changes in whole-brain structure and microstructure during healthy aging. By means of structural equation modeling, we estimated change in gross brain features over about two years in a longitudinal sample of participants from the Berlin Aging Study II (at T1: N=334, mean age = 70 years, 128 women; https://www.base2.mpg.de/en). We used the machine learning algorithm BORUTA on neighborhood-level (Lifeworld-Oriented Spaces) environmental information (N = 201) to identify correlates of brain absolute measures and brain change. We observed typical trajectories of brain aging at the construct and measurement levels. Our findings indicate that of the 25 neighborhood features examined, air pollutants, population density and the land-use composition of individuals’ neighborhood are associated with changes in subcortical gray matter volume, cortical integrity, white matter volume, and ventricle size. Weaker associations were found for mean diffusivity and cortical thickness. These findings underscore the relevance of the physical environment for neural outcomes during aging.

Individual differences in the volumes of brain structures are often linked to various conditions, including Alzheimer's disease, schizophrenia, and overall brain health. However, it remains unclear to what extent these differences reflect individual levels present from young adulthood or diverging aging trajectories from later ages. In this study, we analyze the aging dynamics of the volumes of six brain structures based on magnetic resonance imaging (MRI) scans from a large cross-cohort longitudinal sample of cognitively healthy adults (n = 8,311 with 18,520 MRIs, ages from 18 to 97 years). From general assumptions about structural brain dynamics and measurement noise, a stochastic dynamical model was fitted to the data to estimate both the variability and persistence of structural changes across adulthood. Using this model, we calculated how much of the variance of volumetric differences between individuals can be attributed to stable levels from young adulthood versus systematic changes at older ages, as well as the theoretical sensitivity of longitudinal studies to detect individual differences in change. The findings were as follows: (1) Before age 60 years, inter-individual differences in neuroanatomical volumes almost exclusively reflect stable differences between individuals, while the influence from systematic differences in rate-of-change increases thereafter: up to 50% of the variation being due to differences in change at 80 years. In contrast, ventricular volume reflects differences in change from early adulthood. (2) Current brain-age models are unlikely to be sensitive to detect differences in aging trajectories. (3) Imaging studies have low reliability in detecting inter-individual brain changes before age 60 years. After 60 years, the study reliability increases sharply with longer intervals between scans and more modestly with additional intermediate observations. In conclusion, our results reinforce the view that it is critical to distinguish stable early adulthood levels from systematic differences in change when studying adult brain aging.

Age-related loss of dopamine (DA) integrity has been linked to cognitive decline. Relatedly, education and leisure activity engagement have been highlighted as neurocognitive protective factors, but their associations with DA integrity remain poorly understood. Using Bayesian structural equation modeling, we analyzed longitudinal data from the Cognition, Brain, and Aging (COBRA) prospective cohort study with 181 older adults at baseline to examine correlations among DA D2-like receptor (DRD2) availability in the caudate and putamen, measured using [11C]raclopride positron emission tomography (PET), cognition (working memory, episodic memory, and perceptual speed), education, and self-reported physical, cognitive, and social leisure activity measures. Our research questions target whether (i) education or leisure activities are associated with baseline levels or 5-year changes in DRD2 availability; (ii) changes in leisure activities covary with DRD2 changes; and (iii) education or leisure activities moderate DRD2-cognition change-change correlations. Results showed declines in DRD2 availability in the caudate and putamen, with weak overall DRD2-cognition change-change correlations. For both baseline levels and changes in DRD2 availability, the associations with education and leisure activities were uniformly negligible or small and not strongly supported. Neither education nor leisure activities moderated DRD2-cognition change-change correlations.

Integrating multiple neuroimaging datasets is a key strategy to obtain the increasing sample sizes needed to advance neuroscience. However, this requires addressing known biases due to differences in scanners and segmentations. ComBat is the most widely used solution to address this problem. Existing validations are promising but largely limited to scanner effects and mostly rely on indirect indicators of success. Thus, we assessed ComBat’s performance with compounded scanner and segmentation effects in this preregistered study. We leveraged a travelling participant dataset (121 individuals scanned on 2 scanners and processed with 4 segmentation pipelines) and used another large dataset (n = ~1000 with 2 timepoints ~6 years apart) to evaluate harmonization in longitudinal data. To assess the agreement between batches, we calculated intraclass correlation coefficients, percent differences and tests for the significance of differences. Furthermore, we employed intraclass effect decomposition (ICED) to gauge the contributions of scanners, segmentations, individual differences and measurement error to total variance. ICED illustrated how the upper limit of the attainable agreement is determined by the amount of random measurement error. In the cross-sectional data, harmonization met all preregistered criteria (ICC &gt; 0.75; p-value &gt; 0.05; percent difference &lt; 2%). In contrast, the harmonization of change scores was not satisfactory across all regions of interests. When evaluated as observed values rather than changes, the results of the longitudinal harmonization with the exception of white matter hyperintensity volumes were acceptable, though. The discrepancy of the results is primarily attributable to the size of true between-individual variance relative to measurement error. Even with a follow-up period of six years, changes in most brain volumes are small compared to measurement error. Overall, our results support the utility of ComBat for the harmonization of heterogenous neuroimaging data but also delineate when harmonization fails. Further, our analyses demonstrate that the relative size of measurement error is a crucial determinant of the achievable agreement. We conclude that ComBat can be applied confidently in cross-sectional settings but caution is warranted in longitudinal designs.

Influential work in the gerontological literature in the 1990s has demonstrated that the discrepancy between objective and subjective evaluations of health increases with age. It is an open question though whether the direction and size of the age gradient in the discrepancies has changed over historical time. In this study, we examine cohort differences in age-related divergences in health evaluations using two markers of objective health (illnesses/functioning) and two markers of subjective health (overall/compared to others) obtained from cross-sectional samples of older adults in the Berlin Aging Studies in 1990-1993 (n = 516, aged 70-103) and 2018-2020 (n = 625, aged 68-94). We use both person-oriented and variable-oriented methodological frameworks that specifically facilitate study of aging as a process that propels individuals through a multidimensional health space: permutational multivariate analysis of variance (PERMANOVA) and structural equation modeling (SEM). The person-oriented PERMANOVA results revealed that divergence across markers of health increased with age, but did not evidence any cohort differences. Complementary results from variable-oriented SEMs corroborated that the four-dimensional variance-covariance structure did not differ across cohorts and that the age pattern was consistent across cohorts. Results suggest that objective and subjective indicators of health are increasingly discrepant after age 70, but reveal no evidence that age gradient of the divergences has changed across historical time.

History-graded increases in older adults' levels of cognitive performance across a number of different ability domains are well documented. Less is known, however, about whether such historical advances generalize to measures of verbal learning and whether rates of within-person change therein have also historically shifted. To examine these questions, we used up to 17+-years within-person longitudinal change data obtained in the Manchester Longitudinal Study of Cognition (MLSC). We matched cohorts and weighted analyses by age, number of measurement occasions, and education and compared up to four-wave longitudinal data from 1,279 participants born earlier in historical time (1903-1919, 2,668 observations) versus 1,049 participants born later (1920-1935, 2,239 observations). We applied growth models that orthogonalized between-person age and within-person aging effects, controlled for retest effects, and adjusted for sociodemographic characteristics. We found little to no evidence for historical improvements in verbal learning among older adults. It was only at the zero-order level when participants were in their 60s that later-born cohorts outperformed their matched earlier-born peers. We also found no evidence of historical improvements in the rate of within-person aging-related decline in verbal learning. If anything, the later-born cohort appeared to experience somewhat steeper declines, particularly at older ages. After statistically controlling for sex, occupational status, and city of residence, the only remaining cohort difference was the steeper quadratic (accelerated) rate of decline observed in the later-born cohort. We discuss the theoretical and practical implications of these findings. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Normal aging is associated with decline in dopamine function. Factors associated with individual differences in dopamine decline rates remain unclear but are important to map to spare dopamine-related functions, such as cognition. Here we focused on manifestations of cerebral small-vessel disease from magnetic resonance imaging (white-matter lesions, lacunes, and perivascular space dilation) and vascular risk factors (e.g., hypertension, body mass index (BMI), and hyperlipidemia). We assessed striatal dopamine D2-like receptor (DRD2) reductions across five years in healthy, older adults (n = 129, ages: 64-68 years at baseline) using 11C-raclopride/positron emission tomography. Manifestations of confluent lesions and lacunes at baseline had additive effects on DRD2 decline. Individuals with both manifestations showed fastest DRD2 decline rates (∼ -4 %), followed by those with one manifestation (∼ -2 %), whereas individuals spared of confluent lesions and lacunes showed stable DRD2 levels over time (∼ 0 % change). Furthermore, individuals with confluent lesions or lacunes showed more marked decline in perceptual speed performance, as compared to individuals spared of these manifestations (p < 0.05). Higher systolic blood pressure and lower BMI at baseline were associated with faster 5-year DRD2 decline in the putamen (r = -0.17, p < 0.05) and caudate (r = 0.23, p < 0.05), respectively. Together, confluent lesions and lacunes explained up to 8 % of striatal DRD2 change, and up to 10 % when adding hypertension and BMI to the model. These findings suggest that hallmarks of SVD and certain vascular risk factors predispose faster DRD2 decline in aging and may thus serve as factors to consider in future interventions.

In neuroimaging research, tracking individuals over time is key to understanding the interplay between brain changes and genetic, environmental, or cognitive factors across the lifespan. Yet, the extent to which we can estimate the individual trajectories of brain change over time with precision remains uncertain. In this study, we estimated the reliability of structural brain change in cognitively healthy adults from multiple samples and assessed the influence of follow-up time and number of observations. Estimates of cross-sectional measurement error and brain change variance were obtained using the longitudinal FreeSurfer processing stream. Our findings showed, on average, modest longitudinal reliability with 2 years of follow-up. Increasing the follow-up time was associated with a substantial increase in longitudinal reliability, while the impact of increasing the number of observations was comparatively minor. On average, 2-year follow-up studies require ≈2.7 and ≈4.0 times more individuals than designs with follow-ups of 4 and 6 years to achieve comparable statistical power. Subcortical volume exhibited higher longitudinal reliability than cortical area, thickness, and volume. The reliability estimates were comparable with those estimated from empirical data. The reliability estimates were affected by both the cohort's age where younger adults had lower reliability of change and the preprocessing pipeline where the FreeSurfer's longitudinal stream was notably superior than the cross-sectional stream. Suboptimal reliability inflated sample size requirements and compromised the ability to distinguish individual trajectories of brain aging. This study underscores the importance of long-term follow-ups and the need to consider reliability in longitudinal neuroimaging research.

Greater activity diversity—broad and even participation across daily activities—is found to promote psychological well-being, cognitive functioning, and brain health. Sleep is a key lifestyle factor important for health, but it often competes for time with daily activities. Little is known about its relationship with daily activity diversity in older adults. This study examined the bidirectional associations of nightly sleep quantity and quality with daily activity diversity, as well as age-related differences in these associations. Data came from 127 retired older adults (Mage=76.88, Range=67–88) who participated in a 7-day experience sampling study (6 times/day) embedded into the Berlin Aging Study II. Participants reported momentary engagement in 16 activities across 7 domains (i.e., social, physical, mental, productive, household chores, self-care, and leisure), which were used to calculate a daily activity diversity score using Shannon’s entropy. They also reported nightly sleep duration and quality. Multilevel models, adjusted for age, gender, education, morbidity, perceptual speed, negative affect, and daily pain, tested bidirectional associations between nightly sleep and daily activity diversity. Results showed no significant main association between previous night’s sleep duration/quality and the next-day activity diversity, or vice versa. However, age moderated the association between prior night’s sleep quality and the next-day activity diversity: for the oldest-olds (≥80), higher sleep quality predicted greater activity diversity, whereas for the younger-olds (≤73), the association was negative. These findings highlight the nuanced role of nightly sleep quality in shaping daily activity participation across different age groups.

Differences in the volumes of brain structures between individuals are often linked to various conditions, including Alzheimer's disease, schizophrenia, and overall brain health. However, it remains unclear to what extent these differences reflect individual levels present at young adulthood or diverging aging trajectories at later ages. In this study, we analyze the aging dynamics of the volume of six brain structures based on MRI scans from a large cross-cohort longitudinal sample of cognitively healthy adults (n = 8,311 with 18,520 MRIs, ages from 18 to 97 years). From general assumptions about structural brain dynamics and measurement noise, a stochastic dynamical model was fit to the data to estimate both the variability and persistence of structural changes across adulthood. Using this model, we calculated how much of the variance in individual volumetric differences can be attributed to stable levels from young adulthood versus systematic changes at older ages, as well as the theoretical sensitivity of longitudinal studies to detect individual differences in changes. The findings were as follows: 1) Before age 60 years, inter-individual differences in neuroanatomical volumes almost exclusively reflect stable differences between individuals, while the influence from systematic differences in rate-of-change increases thereafter; up to 40 % of the variation being due to differences in change at 80 years. In contrast, ventricular volume reflects differences in change from early adulthood. 2) Current brain-age models are unlikely to be sensitive to detect differences in aging trajectories. 3) Imaging studies have a low reliability to detect inter-individual brain change before age 60. After 60 years, the study reliability increases sharply with longer intervals between scans and more modestly with additional intermediate observations. In conclusion, it is critical to distinguish between stable levels from early adulthood and systematic differences in change when studying adult brain aging.