INTRODUCTION:Environmental organic pollutants impact brain function and cognitive aging, but the effect of real-world complex mixtures of these pollutants is unexplored. METHODS:Using data collected at two time points from 170 cognitively normal adults, we used hierarchical Bayesian kernel machine regression to examine the association between joint exposure to 49 organic pollutants and latent variables derived from neuropsychological tests that capture key aspects of cognitive aging. RESULTS:We observed a non-linear, inverted U-shaped relationship between the pollutant mixture and the global cognitive score. Polychlorinated biphenyls (PCBs) were the most important pollutant group in the mixture followed by industrial-use pollutants. DISCUSSION:Exposure to a mixture of organic pollutants was associated with poor cognitive aging. Even though many of these pollutants, like PCBs, have been banned for decades, they persist in our environment. Strategies to reduce exposure to these organic pollutants are needed to minimize their impact on cognitive aging.

BACKGROUND:Resting-state brain signal variability has been found to vary with age and cognitive function. Neural flexibility has been suggested as a neural mechanism underlying cognitive reserve (CR), a construct that describes better than expected cognition given brain status. We examined the associations between age, resting-state brain signal variability, and CR. METHOD:Analysis was based on resting-state functional neuroimaging data from 470 participants (ages 20-80) from the Reference Ability Neural Network and the CR studies. Brain signal variability was quantified as the log-transformed standard deviation of the time-varying blood-oxygen-level-dependent signal per region. We then derived variability pattern scores related to age, perceptual speed, episodic memory, fluid reasoning, and vocabulary using Scaled Subprofile Modelling principal component analysis. To formally test whether the cognition-related patterns fulfill the requirements for CR, we examined whether each pattern explained additional variance in cognition beyond brain structural measures, age, sex, education. We checked the robustness of our findings using permutation tests. Finally, we determined which brain regions significantly contributed to each pattern. RESULTS:Brain variability patterns emerged across all four cognitive domains and showed robust associations with CR in the domains of episodic memory, fluid reasoning and vocabulary, explaining between 10.5%-18.3% of variance in cognitive performance. The cognition-related variability patterns inversely mirrored age-related variability patterns in some brain regions. CONCLUSIONS:BOLD signal variability plays a role in aging and underlies CR in the domains of episodic memory, fluid reasoning, and vocabulary.

BACKGROUND AND OBJECTIVES:Maintaining cognitive function despite the presence of Alzheimer disease (AD) pathology is the foundation of cognitive reserve. Although the theory of cognitive reserve is strongly supported by empirical research, the field lacks standardized, validated methods for quantifying cognitive and brain reserve. We tested whether associations between AD pathology and cognitive function were modified by proxy measures of cognitive reserve (years of education, socioeconomic status; SES) and brain reserve (brain-predicted age difference, and a volumetric AD signature). We hypothesized that greater structural brain integrity, higher education, and higher SES would attenuate the association between greater AD pathology and poorer cognitive performance. METHODS:This cross-sectional study analyzed baseline data from a multisite randomized clinical trial, which was conducted at 3 US universities and enrolled cognitively unimpaired, physically inactive, community-dwelling adults. AD pathology was measured via plasma assays for phosphorylated tau (p-tau)-217 in the whole cohort, and PET for β-amyloid (Aβ) in a subset of participants as a secondary analysis. The primary outcome of cognitive function was evaluated by a comprehensive cognitive assessment. SES was measured via the MacArthur Socioeconomic Status Index, and magnetic resonance imaging was used to calculate brain-predicted age difference (brain-PAD) and a volumetric AD signature. Data were analyzed using linear regression models with interaction terms for moderation analyses. RESULTS:A total of 621 participants (aged 69.9 ± 3.8, 71% female) had available data for the main analyses and 355 had PET Centiloid data available. Brain-PAD moderated the association between AD pathology (measured by p-tau217) and multiple cognitive domains, including episodic memory (β = -0.09 [-0.16 to -0.02]), processing speed (β = -0.08 [-0.15 to -0.01]), working memory (β = -0.10 [-0.18 to -0.03]), and executive function/attentional control (β = -0.08 [-0.15 to -0.01]). Specifically, the negative association of greater AD pathology with poorer cognition was weakest in individuals with younger appearing brains. A latent SES score also moderated the relationship between p-tau217 and episodic memory (β = 0.08 [0.01-0.16]), but this did not survive correction for multiple comparisons. Neither years of education nor the volumetric AD signature moderated pathology-cognition associations. DISCUSSION:These results support the hypothesis that higher cognitive and brain reserve may help buffer the cognitive consequences of AD pathology. Strategies to increase both cognitive and brain reserve could help to boost resilience against emerging AD pathology; however, longitudinal studies are needed to confirm these conclusions.

As one of the earliest pathological events in Alzheimer's disease, amyloid-beta accumulation has been linked to alterations in large-scale brain networks, especially the default mode network. While most prior work has focused on resting-state functional connectivity or task-evoked activation, how amyloid-beta relates to task-modulated connectivity that directly supports behavior is less explored. Here, we examined the relationship between amyloid-beta burden and task-modulated connectivity during a Letter Sternberg verbal working-memory task in eighty-four cognitively normal older adults from an ongoing longitudinal cohort (age 56-71 years; 41 female). To further understand these connectivity alterations, we tested whether these connections were associated with task performance or with cognitive reserve factors. We found that higher amyloid-beta burden was associated with reduced connectivity specifically during the probe phase of working-memory retrieval. At the regional scale, lower node strength in posterior-medial regions of the default mode network was related to worse task performance and mediated the amyloid-behavior relationship. At the network scale, altered coupling between the default mode and dorsal attention networks was related to task performance but did not mediate the amyloid-behavior relationship. Moreover, physical activity was associated with network-level coupling but not with regional node strength. These findings suggest that amyloid-beta burden disrupts retrieval-related working-memory systems, particularly regions of the default mode network that are vulnerable to amyloid-beta, while coupling between the default mode and dorsal attention networks indexes resilience. Collectively, task-modulated connectivity provides insight into pathways through which early Alzheimer's disease pathology influences working-memory performance and resilience and may inform targeted lifestyle or neuromodulation interventions for older adults. ### Competing Interest Statement The authors have declared no competing interest. NIH/NIA, R01 AG038465, R01 AG026158

Chronic psychological stress has been implicated as a risk factor for Alzheimer’s disease (AD), potentially through cortisol-mediated acceleration of disease progression. However, the molecular pathways underlying this relationship remain poorly understood. Epigenetic regulation of the glucocorticoid and mineralocorticoid receptor genes (NR3C1 and NR3C2), which encode receptors for cortisol, may play an important role, but has not been examined in relation to AD progression. Therefore, this study investigated associations between DNA methylation of NR3C1/NR3C2 and AD-related phenotypes, including cognition, brain amyloid-β (Aβ) burden, and regional brain volumes. These associations were examined in two independent cohorts of cognitively unimpaired individuals with accumulating brain Aβ (n = 89–298 across outcomes) using linear regression and meta-analyses. The study also explored whether DNA methylation within NR3C1 and NR3C2 interacted with depression symptoms to influence relationships with AD-related phenotypes. While only nominal associations were observed in direct analyses, stronger associations emerged in interaction with depressive symptoms. Interaction analyses showed that relationships between DNA methylation and AD-related phenotypes (cognition, hippocampal volume and ventricular expansion) differed depending on the presence of depression symptoms. Consistent patterns across cohorts were observed, with associations primarily evident among individuals with clinically relevant depressive symptoms. One site (NR3C1 cg24052866) was associated with cognitive decline, one (NR3C1 cg08845721) with cross-sectional hippocampal volume, and eight (NR3C1 cg21979215, cg16594263; NR3C2 cg27460943, cg17253842, cg04867484, cg10993059, cg25672354, cg27234800) with ventricular expansion. These exploratory findings suggest epigenetic variation within cortisol receptor genes may influence AD-related neurodegeneration in a depression-dependent manner.

Financial decision making (FDM) and awareness of one's own financial decision making abilities are both clinically and practically important constructs in older adults. Structural and functional neuroimaging may be useful to shed light on the neural mechanisms supporting these constructs and further help identify those at risk for making poor decisions and losing money. We used resting state (rs)-functional connectivity in the present study. We hypothesized that Frontoparietal-Salience (FP-SAL) between-network connectivity would be most strongly associated with FDM, while Default Mode Network-Salience (DMN-SAL) between-network connectivity would be more associated with financial awareness. Community-dwelling older adults (59% women, 67% White, 29% Black) were recruited with mean age=68.43 years (SD=5.14) and mean education=15.88 (SD=2.41). Primary outcomes included standardized measures of financial decision making, financial awareness metrics derived from metacognitive frameworks, and between-network connectivity values. After adjusting for demographic factors and individual within-network connectivity values, FDM was only associated with FP-SAL network connectivity. In contrast to the predictions of our hypothesis, higher between-network connectivity values were associated with lower FDM scores. No significant associations between DMN-SAL between network connectivity and financial awareness was found. Our preliminary findings suggest that the association between FP-SAL between network connectivity and FDM is not straightforward. Future studies using larger samples with alternate analytic approaches will be required to replicate these findings.

Recent advancements in connectome analyses have enabled more precise measurements of brain network integrity. Identifying neural measures that can operate as mechanisms of cognitive reserve is integral for the study of individual variability in age-related cognitive changes. In the present study, we tested the hypothesis that network resilience, or the network's ability to maintain functionality when facing internal or external perturbations that cause damage or error, can function as a cognitive reserve (CR) candidate, modifying the relationship between cognitive and brain changes in a lifespan cohort of cognitively healthy adults. One hundred cognitively healthy older adults from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (50-80 years) underwent resting-state fMRI and neuropsychological testing at baseline and 5-year follow-up. Using undirected weighted adjacency matrices created from the Schaefer et al. (2018) 400-parcellation atlas and 19 additional subcortical regions (419 nodes in total), whole-brain network resilience was assessed through a targeted attack approach, where nodes were sequentially removed by nodal strength and resilience defined as the iteration of the steepest slope in the largest connected component (LCC) decay. We observed that network resilience moderated the effect of cortical thickness (CT) changes on longitudinal changes in Fluid Reasoning performance, even after adjusting for baseline differences, demographic factors, and the initial LCC of the unlesioned matrix, indicating that individuals with greater resilience were less sensitive to the effect of cortical thickness changes on changes in cognition. These findings support the use of targeted attack as a measure of cognitive reserve, suggesting that higher network resilience may allow individuals with reduced brain integrity to better cope with structural loss and maintain cognitive function.

BackgroundPhysical exercise is widely recognized for its cognitive benefits; however, the effect of menopausal status in modulating the cognitive effects of exercise is not definitively established.ObjectiveTo examine the cognitive benefits of two 6-month physical exercise programs in cognitively healthy older women across adulthood, and whether menopause status moderates these benefits.MethodsIn a post hoc analysis of a randomized controlled trial, 93 cognitively healthy women (aged 20-67; 43% at post-menopause) were assigned to either aerobic exercise (AE) or stretching/toning (ST) 4 days a week for six months. Neuropsychological assessment, cardiorespiratory exercise test, and blood draw were performed at baseline, 3-months, and 6-months. Linear mixed-effects regression models assessed whether menopausal status moderated the impact of exercise on executive functions and processing speed.ResultsSeventy-six participants (81.7%) completed the intervention. A time-by-group-by-menopause interaction emerged after 3 months (β = -0.89; p = 0.001) and 6 months (β = -0.67, p = 0.016). Post-menopausal women in the AE group showed greater improvement in executive functions compared to the ST group and pre-menopausal women. Models controlled for age, education, and baseline cognitive performance.ConclusionsOur results provide novel evidence that AE improves cognition with pronounced executive functions benefits in post-menopausal women, a population at higher risk for dementia. Since women are at a higher risk of developing Alzheimer's disease compared to men, these findings support AE as a relevant strategy to promote women's brain health. Although this is a secondary analysis, it may inform future exercise trials targeting women.

White matter microstructural changes play a crucial role in cognitive decline in aging and neurodegenerative disorders including Alzheimer’s disease (AD). However, the processes underlying white matter microstructural changes and the molecular pathways leading to these changes in AD remain largely unknown. AD involves cortical and juxtacortical microstructural changes, with free water fraction (FWF) as a potential imaging marker. We measured FWF using diffusion magnetic resonance imaging in 68 juxtacortical regions of 153 cognitively normal controls and 194 patients with AD as evidenced by elevated amyloid PET. We estimated the expression of 15,633 genes in the same regions using transcriptomic data from the Allen Human Brain Atlas. The biological processes and cell types associated with the linked genes were evaluated. Mediation analysis was used to examine whether FWF mediates the association between APOE ε4 status and cognitive performance. Gene ontological analyses revealed that these genes were enriched for biological processes relating to lipid metabolic process, ensheathment of neurons, and synaptic signaling and were predominantly expressed in oligodendrocytes, GABAergic neurons, and pyramidal neurons from the hippocampus CA region. These ontological enrichment results were replicated in two additional datasets. Furthermore, mediation analyses revealed a domain-specific role of FWF in the association between APOE ε4 status and cognitive performance. Our findings provide mechanistic insights into regional juxtacortical microstructural changes in AD, particularly the processes involving lipid metabolism, offering potential therapeutic targets.

BACKGROUND:Sleep is a complex physiological process, crucial for cognitive functioning, emotional regulation, and overall health. Recent advances in genomics and neuroimaging have illuminated the intricate relationship between genetics, sleep architecture, and brain changes. This study investigated the association between sleep duration genetics, through a Sleep Duration Polygenic Index (Sleep PGI), and brain changes (total cortical thickness, white matter volume, gray matter volume, white matter hyperintensities volume) in cognitively healthy adults aged 20-80 years old. METHODS:Using longitudinal data from the Reference Ability Neural Network (RANN) and Cognitive Reserve (CR) studies, we examined the impact of Sleep PGI on brain measures (total cortical thickness, gray matter volume, white matter volume, WMH volume) over time. Generalized Estimated Equations were used for the statistical analysis. Analysis was performed in the total sample (n = 94) and in three age-groups (young, middle, old). RESULTS:Across age, higher Sleep PGI was associated with higher temporal WMH volumes over time. In models considering an interaction of age between Sleep PGI and time in study, age emerged as a significant moderator for outcomes of hippocampal volume, cortical white matter volume, and WMH volume (total, parietal) as outcomes. CONCLUSIONS:Sleep duration polygenic score was associated with changes in the brain in cognitively healthy adults. Genetic predisposition for longer sleep duration was associated with more favorable longitudinal trajectories against brain decline, a result mostly driven by younger adults. These findings underscore the importance of maintaining optimal sleep duration and the potential for personalized interventions to improve sleep and brain health.

Background: Physical activity (PA) and healthy dietary patterns are associated with reduced risk of cognitive decline and dementia. However, it remains unclear whether these lifestyle factors, assessed in midlife, confer greater resilience to late-life neurodegeneration on cognitive function (i.e., cognitive reserve). Methods: We included 581 Framingham Heart Study Offspring cohort participants who completed food frequency questionnaires (FFQ) and PA questionnaires between ages 40-59 years and at least one neuropsychological (NP) assessment and brain MRI between ages 65-75 years. Dietary Approaches to Stop Hypertension (DASH) scores were based on FFQs. PA was based on the physical activity index (PAI) score. We used previously derived factor scores for memory (MEM) and executive functioning (EF) from the NP exams. MRI measures included hippocampal volume (HCV) and total brain volume (TBV). We used linear regression models to examine the two-way interaction between DASH quartiles or PAI tertiles and each outcome. Post-estimation marginal effects of the associations for each DASH group or PAI group were also obtained. Results: Participants were on average 54 years old at baseline; 53% were female ( Table 1) . Relationships between brain and cognitive measures with diet and PA did not show consistent effects. For example, HCV was positively associated with MEM performance only among those in the second ( β : 0.23; 95% CI: 0.11, 0.34) and fourth ( β : 0.14; 95% CI: 0.02, 0.26) quartiles of DASH scores ( Table 2 ). Greater TBV was significantly associated with better MEM performance only among those in the fourth quartile of DASH diet scores ( β : 0.003; 95% CI: 0.0005, 0.01). Similar positive associations were observed for HCV with EF among individuals in the second quartile of DASH diet scores. HCV was positively associated with MEM performance among those in the first ( β : 0.14; 95% CI: 0.03, 0.25) and third ( β : 0.14; 95% CI: 0.03, 0.26) tertiles of PAI scores ( Table 3 ). TBV was associated with EF only in individuals in the second tertile of PAI. No significant interactions were observed between DASH or PA and brain volume on performance in MEM and EF. Conclusion: DASH diet or PA in midlife does not appear to confer resilience to neurodegeneration in MEM or EF at an average age of 69 years. Continued follow-up of these participants as they enter a period of greater risk for dementia may reveal effects of midlife diet and PA on cognitive resilience in later life.

The variability in cognitive and brain ageing trajectories may be influenced by inter-individual and community-level differences in resilience that result from differential exposures to social and structural determinants of health and be affected by an individual’s sex and gender. However, no clear guidance exists on how to best integrate these diversity-related factors (that is, sex, gender and social and structural determinants of health) into clinical and cognitive neuroscience research on resilience in ageing and dementia. The international Brain Resilience and Diversity in Aging and Dementia (BReDAD) Collaboratory was established in 2024 with the goals of synthesizing knowledge, identifying knowledge gaps and developing recommendations for conducting more inclusive research on resilience in this area. On the basis of a focused review of the literature, and discussions held and recommendations made by the Collaboratory, in this Roadmap article, we present a way forward for integrating diversity in future resilience research. This proposal comprises: (i) developing trust and meaningful long-term relationships with communities historically excluded from research; (ii) diversifying who is engaged in all aspects of the research process; (iii) adopting a life-course perspective; (iv) improving and expanding research designs and measurement tools; and (v) using sensitive computational analytics and mixed methods for testing complex, intersectional models. We conclude by recommending a transdisciplinary approach in resilience research to better reflect the complexities inherent in studying diversity and developing precision medicine outcomes. Guidance is lacking on how to best integrate sex, gender and social and structural determinants of health into neuroscience research on brain resilience in ageing and dementia. In this Roadmap article, Rajah et al. propose a way forward for conducting more inclusive research in this field.

BACKGROUND:Alzheimer's Disease (AD) prognosis is extremely heterogeneous, even with a similar burden of global tau and amyloid (Aβ) deposition in the brain, which makes it challenging to develop targeted therapeutic interventions and to counsel families on disease prognosis. METHOD:The TPI is based on 4 components: 1) Remote interaction (TPIri) between Aβ and tau pathologies in regions that are functionally and/or structurally connected, but spatially distinct; 2) Local interaction (TPIli) between spatially co-localized Aβ and tau pathology; and subject-specific 3) Functional connectivity (TPIfc) and 4) Structural connectivity (TPIsc) between regions that can facilitate the spread of tau in the brain. RESULT:From an ongoing study of 112 participants with early accumulation of Ab and Tau, longitudinal data were available on 27 participants (2-3 years of follow-up). Using these data and all 4 TPI components as independent variables, we built a LASSO model to predict future tau accumulation, controlling for covariates. The obtained coefficients were used to compute our TPI, and its predictability was assessed by its association with actual longitudinal tau accumulation (within-sample validation). Furthermore, we compared our model, using subject-specific connectomes, with a conventional model using group-averaged connectomes. As seen in Figure 1, while both TPIs (obtained by subject-specific; r=0.8, p <10-5; and group-averaged connectomes: r=0.58, p <0.007) predicted longitudinal tau accumulation, the subject-specific TPI significantly outperformed the group-averaged TPI in predicting subsequent tau (DSlope t=2.96, p <0.009) and accounts for 30% more variance in the prediction. CONCLUSION:Despite a small sample size, we demonstrate that an imaging index that incorporates baseline Ab, tau, and subject-specific connectivity can accurately predict future accumulation of tau. Validation in a larger cohort is ongoing.

The Brazilian population in the US is growing and aging but remains underrepresented in US health research. In addition to group-specific genetic and environmental risks, the Brazilian immigrant population in the US likely presents risks for health inequities due to limited healthcare access/utilization and immigration-related stress. Language has been a barrier to recruiting the Brazilian-US population as Portuguese-speaking populations are not typically included in the US-Latinx cohorts. The BRAINY-NJ cohort study is being initiated to investigate the impact of cardiovascular and lifestyle/sociocultural risk factors on cognition and brain function in 120 cognitively healthy Brazilian immigrants (55+) living in/around the New York City area (New Jersey / New York). The study follows a community-based participatory research (CBPR) paradigm to formalize the academic-community partnership through different actions: 1) establishing partnerships with the community organizations and leaderships through in-person events; 2) building a research team culturally sensitive and proficient in Portuguese; 3) collecting data at the events to assess community perceptions, interests, and priorities; 4) identify potential members for the study's Community Advisory Board; 5) identify community resources to recruit participants. Over 10 months, the BRAINY-NJ organized three in-person events: one health fair and two educational 2.5-hour meetings. The events were in Portuguese and on weekends. The educational meetings included: 1) Educational component on brain/mental health, dementia prevention, and the BRAINY-NJ; 2) Social component (e.g., breakfast, Brazilian live music, and dance); and 3) Assessment of interests and suggestions. The educational meetings included 30-40 participants, the main topics of interest were aging, memory, sleep, and emotional health. Most participants (87%) reported never having participated in a study, and 93.3% reported interest in joining the study. More participants rated cognitive/emotional health as a main concern rather than physical health. The BRAINY-NJ study partners with local NGOs, and Brazilian faith-based organizations (e.g., Churches and Afro-Brazilian Umbanda Center). Recruitment of underrepresented populations, including the Brazilian-US population, is urgently needed. In-person events are critical to engage a community with limited research in the US. Research activities that connect topics on brain/cognitive health with emotional health seem particularly of interest to the Brazilian immigrant community.

BACKGROUND AND PURPOSE:Radiomics extracts imaging features that may not be detectable through conventional volumetric analyses. Given their role in multiple sclerosis (MS), we applied radiomics to thalamic nuclei and examined their associations with cognitive performance. METHODS:A total of 601 individuals were included (342 people with MS [PwMS] from two cohorts and 259 healthy controls [HC]). Radiomic features (RF) and volumes were extracted from the whole thalamus, five thalamic nuclei, and the putamen segmented on three-dimensional T1-weighted images. Cognitive performance was assessed using the Symbol Digit Modalities Test (SDMT) and Paced Auditory Serial Addition Test (PASAT) in PwMS and the Digit Symbol Substitution Test (DSST) in HC. In the first MS cohort, multivariate linear regression in a discovery set (N = 103) identified thalamus-derived RF associated with SDMT, which were retested in a replication set (N = 63). Their associations with PASAT in a second MS cohort (N = 176) and DSST in HC were also evaluated. We then tested whether the same RFs, when extracted from the putamen, was associated with SDMT. Least Absolute Shrinkage and Selection Operator (LASSO) models assessed the combined predictive value of RF and volumes. RESULTS:Twenty-eight RF-region of interest (ROI) pairs were associated with SDMT in the replication set (false discovery rate [FDR] < 0.05). Of these, 24 were also associated with PASAT (FDR ≤ 0.03), and 2 with DSST. Only ventral nuclei volume showed replicated associations among volumetrics. Only four putamen-derived pairs were associated with SDMT (FDR = 0.04). LASSO results confirmed RF outperformed volumes. CONCLUSION:RF extracted from the thalamus is strongly associated with cognitive performance in PwMS, outperforming volumetric measures and supporting their potential as sensitive imaging biomarkers.

‘Dependence’ has been proposed as a measurable health outcome reflecting the overall impact of disease progression in Alzheimer’s disease (AD). However, the meaning and relevance of ‘dependence’ in early symptomatic AD (MCI and mild AD dementia) have not been previously investigated. Aims of the current research were to (1) explore the concept of ‘dependence’ from the perspective of caregivers of patients with early symptomatic AD, (2) assess the content validity of the Dependence Scale (DS; Stern, 1994), a 13-item scale evaluating patient dependence based on a caregiver interview, and (3) explore the relevance of newly incorporated DS rater guidance clarifying potential ambiguities in item phrasing. Concept elicitation (CE) and cognitive debriefing (CD) interviews were conducted with caregivers of patients with confirmed diagnoses of Mild Cognitive Impairment (MCI; n = 7) or mild AD dementia (mild AD; n = 18) recruited in the US. The DS was debriefed with 19 caregivers. CE interviews explored the concept of dependence, including its meaning and relevance. Transcribed interviews were analyzed thematically, with structured codes used for CD. During CE most caregivers (n = 21) described ‘dependence’ in AD in terms of ability/lack of ability to complete tasks alone. Several (n = 16) discussed dependence in the context of concerns about leaving patients alone, citing perceived risks to physical safety or need for emotional support. Most concepts covered by the DS were discussed during interviews. During CD, some respondents (up to 42%) felt some item phrasing lacked clarity, particularly when a single response was needed to characterize functioning across multiple activities (e.g. shopping and chores); in all cases, rater guidance provides relevant clarification for use during standardized administration. Regarding content, 1 or more caregivers provided examples of patient difficulties assessed by 10 of the 13 items. Items assessing instrumental daily activities appeared most relevant, with most caregivers reporting clear examples. Findings support patient dependence as a meaningful concept in AD and provide evidence supporting content validity of the DS. Most DS concepts were discussed during the interviews. Rater guidance is relevant and may mitigate the impact of perceived ambiguities in item phrasing.

The Drift-Diffusion Model (DDM) is widely used in neuropsychological studies to understand the decision process by incorporating both reaction times and subjects' responses. Various models have been developed to estimate DDM parameters, with some employing Bayesian inference. However, when examining associations between phenotypes of interest and DDM parameters, most studies adopt a two-step approach: first estimating DDM parameters, then applying a separate statistical model to the estimated values. Despite the potential for bias, this practice remains common, primarily due to researchers' unfamiliarity with Bayesian modeling. To address this issue, this tutorial presents the implementations and advantages of fitting a unified Bayesian hierarchical regression model that integrates trial-level drift-diffusion modeling and subject-level regression between DDM parameters and other variables. The R package RegDDM, developed and demonstrated in this tutorial, facilitates this integrated modeling approach.

We were interested to test whether regional amyloid as measured with Florbetaben could be detected in fMRI activation patterns recorded 5 years prior. Perceptual Speed was probed with 3 cognitive fMRI tasks (Digit Symbol Substitution, Letter Comparison, Pattern Comparison) for 324 participants, aged 20 to 80, at timepoint 1. A subsample of 63 participants (aged 51-80) underwent Florbetaben amyloid scans 5 years later ( = timepoint 2), resulting in SUVR values in 68 regions of the FreeSurfer DK atlas. For these 63 participants, Principal Components analysis was conducted in the activation data that were averaged within participant across all 3 tasks. Correlations of the resulting pattern scores with regional amyloid were observed, adjusting for the covariates of age, sex and years of education. The relationship to task performance in the fMRI tasks ( = sign-reversed RT) was observed too, again with the covariates age, sex, and years of education. All participants were healthy, without cognitive impairment. The pattern score of the first principal component in the fMRI activation data in the subsample with N = 63 correlated with subsequent regional amyloid in 22 regions at p<0.05. In the larger sample of N = 324, the pattern score also correlated negatively with task performance, beyond the covariates of age, sex and years of education. The pattern consisted of positive loadings (Z>1.5) in frontal locations like anterior cingulate, superior and inferior frontal gyrus, bilateral insula, basal ganglia. The pattern showed negative loadings (Z←1.5) in occipital and cerebellar regions (see Figure). Regional amyloid had a robust correlation with an underlying fMRI activation pattern for Perceptual-Speed tasks collected 5 years earlier. While in the subsample of 63 there was no relationship of the pattern with fMRI-task performance, in a whole lifespan sample with N = 324, the amyloid-associated activation pattern was negatively related to task performance. These findings suggest that amyloid can impact activation in a manner that is detrimental to task performance even in healthy older adults.

Mild cognitive impairment (MCI) is a clinical cognitive deficit that is not severe enough to meet the threshold for Alzheimer's Disease (AD); however, MCI patients have an increased risk of developing AD. Therefore, a diagnosis of MCI may represent a critical turning point in the trajectory of developing AD. Establishing neurological signatures of MCI using network control theory (NCT) may allow more informed diagnosis, and an understanding of its underlying mechanisms could pave the way for novel treatments. Functional MRI (fMRI) metrics were collected in MCI patients (n = 57, mean age = 66.68) and healthy controls (HC) (n = 500, mean age = 72.25). The average structural connectivity matrix was obtained using age-matched controls from the Human Connectome Project-Aging dataset. Commonly recurring brain states were identified via k-means clustering of activation matrices over 200 regions using the Schafer atlas. NCT was used to compute the transition energy (TE): the minimum energy required to transition between each pair of brain states. The entropy of each region’s activity was calculated using SampEn, and was then correlated with TE using Pearson’s correlation. Pairwise/global (average of all brain state pairs) TE and global entropy (average of all regions) were compared between MCI and HC using ANCOVA with age and sex as covariates. The brain states identified via k-means clustering were high and low amplitude activity in the visual, somatomotor, and limbic networks (Figure 1). While there were no significant differences in pairwise or global TE between MCI and HC (Figures 2-3), MCI had significantly lower global entropy than HC. ANCOVA revealed that increased age is associated with increased entropy. Pearson correlation showed a significant inverse relationship between global TE and entropy across individuals (r = −0.13) (Figure 3). Entropy of brain activity measured with fMRI is a promising neuroimaging biomarker of MCI, which is often underdiagnosed or diagnosed with delay. Future work will investigate regional entropy reduction patterns between MCI and AD patients to establish the use of these metrics in disease progression, and to get a more detailed picture of brain activity changes in individuals with these diagnoses.