Measuring Resilience and Resistance in Aging and Alzheimer Disease Using Residual Methods: A Systematic Review and Meta-analysis

Age-related behavioral resilience in smartphone touchscreen interaction dynamics

We experience a life that is full of ups and downs. The ability to bounce back after adverse life events such as the loss of a loved one or serious illness declines with age, and such isolated events can even trigger accelerated aging. How humans respond to common day-to-day perturbations is less clear. Here, we infer the aging status from smartphone behavior by using a decision tree regression model trained to accurately estimate the chronological age based on the dynamics of touchscreen interactions. Individuals (N = 280, 21 to 87 y of age) expressed smartphone behavior that appeared younger on certain days and older on other days through the observation period that lasted up to ~4 y. We captured the essence of these fluctuations by leveraging the mathematical concept of critical transitions and tipping points in complex systems. In most individuals, we find one or more alternative stable aging states separated by tipping points. The older the individual, the lower the resilience to forces that push the behavior across the tipping point into an older state. Traditional accounts of aging based on sparse longitudinal data spanning decades suggest a gradual behavioral decline with age. Taken together with our current results, we propose that the gradual age-related changes are interleaved with more complex dynamics at shorter timescales where the same individual may navigate distinct behavioral aging states from one day to the next. Real-world behavioral data modeled as a complex system can transform how we view and study aging.

Influence of cognitive reserve on cognitive and motor function in α-synucleinopathies: A systematic review and multilevel meta-analysis

Cognitive reserve has shown promise as a justification for neuropathologically unexplainable clinical outcomes in Alzheimer's disease. Recent evidence suggests this effect may be replicated in conditions like Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. However, the relationships between cognitive reserve and different cognitive abilities, as well as motor outcomes, are still poorly understood in these conditions. Additionally, it is unclear whether the reported effects are confounded by medication. This review analysed studies investigating the relationship between cognitive reserve and clinical outcomes in these α-synucleinopathy cohorts, identified from MEDLINE, Scopus, psycINFO, CINAHL, and Web of Science. 85 records, containing 176 cognition and 31 motor function effect sizes, were pooled using multilevel meta-analysis. There was a significant, positive association between higher cognitive reserve and both better cognition and motor function. Cognition effect sizes differed by disease subtype, cognitive reserve measure, and outcome type; however, no moderators significantly impacted motor function. Review findings highlight the clinical implications of cognitive reserve and importance of engaging in reserve-building behaviours.

Instruments to Assess Cognitive Reserve Among Older Adults: a Systematic Review of Measurement Properties

Cognitive reserve explains the differences in the susceptibility to cognitive impairment related to brain aging, pathology, or insult. Given that cognitive reserve has important implications for the cognitive health of typically and pathologically aging older adults, research needs to identify valid and reliable instruments for measuring cognitive reserve. However, the measurement properties of current cognitive reserve instruments used in older adults have not been evaluated according to the most up-to-date COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN). This systematic review aimed to critically appraise, compare, and summarize the quality of the measurement properties of all existing cognitive reserve instruments for older adults. A systematic literature search was performed to identify relevant studies published up to December 2021, which was conducted by three of four researchers using 13 electronic databases and snowballing method. The COSMIN was used to assess the methodological quality of the studies and the quality of measurement properties. Out of the 11,338 retrieved studies, only seven studies that concerned five instruments were eventually included. The methodological quality of one-fourth of the included studies was doubtful and three-seventh was very good, while only four measurement properties from two instruments were supported by high-quality evidence. Overall, current studies and evidence for selecting cognitive reserve instruments suitable for older adults were insufficient. All included instruments have the potential to be recommended, while none of the identified cognitive reserve instruments for older adults appears to be generally superior to the others. Therefore, further studies are recommended to validate the measurement properties of existing cognitive reserve instruments for older adults, especially the content validity as guided by COSMIN.Systematic Review Registration numbers: CRD42022309399 (PROSPERO).

Associations of brain morphology with cortical proteins of cognitive resilience

In a recent proteome-wide study, we identified several candidate proteins for drug discovery whose cortical abundance was associated with cognitive resilience to late-life brain pathologies. This study examines the extent to which these proteins are associated with the brain structures of cognitive resilience in decedents from the Religious Orders Study and Memory and Aging Project. Six proteins were associated with brain morphometric characteristics related to higher resilience (i.e., larger anterior and medial temporal lobe volumes), and five were associated with morphometric characteristics related to lower resilience (i.e., enlarged ventricles). Two synaptic proteins, RPH3A and CPLX1, remained inversely associated with the lower resilience signature, after further controlling for 10 neuropathologic indices. These findings suggest preserved brain structure in periventricular regions as a potential mechanism by which RPH3A and CPLX1 are associated with cognitive resilience. Further work is needed to elucidate other mechanisms by which targeting these proteins can circumvent the effects of pathology on individuals at risk for cognitive decline.

Brain structural indicators of β-amyloid neuropathology

Recent efforts demonstrated the efficacy of identifying early-stage neuropathology of Alzheimer's disease (AD) through lumbar puncture cerebrospinal fluid assessment and positron emission tomography (PET) radiotracer imaging. These methods are effective yet are invasive, expensive, and not widely accessible. We extend and improve the multiscale structural mapping (MSSM) procedure to develop structural indicators of β-amyloid neuropathology in preclinical AD, by capturing both macrostructural and microstructural properties throughout the cerebral cortex using a structural MRI. We find that the MSSM signal is regionally altered in clear positive and negative cases of preclinical amyloid pathology (N = 220) when cortical thickness alone or hippocampal volume is not. It exhibits widespread effects of amyloid positivity across the posterior temporal, parietal, and medial prefrontal cortex, surprisingly consistent with the typical pattern of amyloid deposition. The MSSM signal is significantly correlated with amyloid PET in almost half of the cortex, much of which overlaps with regions where beta-amyloid accumulates, suggesting it could provide a regional brain 'map' that is not available from systemic markers such as plasma markers.

Changes in Body Mass Index and Incident Mild Cognitive Impairment Among African American Older Adults

BACKGROUND

Previous research suggests a decline in body mass index (BMI) among older adults is associated with negative health outcomes, including mild cognitive impairment (MCI) and incident dementia. However, no studies have examined the effects of education or developing MCI on BMI trajectories over time. The purpose of this investigation was to characterize trajectories of change in BMI among older adults who develop MCI.

METHODS

Participants were from the Minority Aging Research Study (MARS), a longitudinal cohort study of cognitive decline and Alzheimer's disease in older African Americans living in the greater Chicago, Illinois, area. The study included annual clinical evaluations of cognitive status, as well as measurements of height and weight for BMI calculation. Older African American participants without cognitive impairment at baseline were included in the present analysis (N = 436, 78% women, mean baseline age = 72 [SD = 5.7], mean education = 15 [SD = 3.5]).

RESULTS

In piecewise linear mixed-effects models that included a random intercept and 2 random slopes, BMI declined over time (B = -0.20, SE = 0.02, p < .001), with a faster decline after MCI diagnosis (additional decline, B = -0.15, SE = 0.06, p = .019). Older age was associated with lower baseline BMI (B = -0.19, SE = 0.05, p < .001), as was higher education (B = -0.34, SE = 0.09, p < .001). Further, higher education was associated with a slower decline in BMI before MCI (B = 0.02, SE = 0.006, p = .001), but a faster decline after MCI (B = -0.06, SE = 0.022, p = .003).

CONCLUSIONS

These results suggest an accelerated decline in BMI following an MCI diagnosis, with higher education related to an even faster BMI decline.

Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention

Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.

Characterization of brain resilience in Alzheimer's disease using polygenic risk scores and further improvement by integrating mitochondria-associated loci

INTRODUCTION

Identification of high-risk people for Alzheimer's disease (AD) is critical for prognosis and early management. Longitudinal epidemiologic studies have observed heterogeneity in the brain and cognitive aging. Brain resilience was described as above-expected cognitive function. The "resilience" framework has been shown to correlate with individual characteristics such as genetic factors and age. Besides, accumulative evidence has confirmed the association of mitochondria with the pathogenesis of AD. However, it is challenging to assess resilience through genetic metrics, in particular incorporating mitochondria-associated loci.

OBJECTIVES

In this paper, we first demonstrated that polygenic risk scores (PRS) could characterize individuals' resilience levels. Then, we indicated that mitochondria-associated loci could improve the performance of PRSs, providing more reliable measurements for the prevention and diagnosis of AD.

METHODS

The discovery (N = 1,550) and independent validation samples (N = 2,090) were used to construct nine types of PRSs containing mitochondria-related loci (PRSMT) from both biological and statistical aspects and combined them with known AD risk loci derived from genome-wide association studies (GWAS).Individuals' levels of brain resilience were comprehensively measured by linear regression models using eight pathological characteristics.

RESULTS

It was found that PRSs could characterize brain resilience levels (e.g., Pearson correlation test Pmin = 7.96×10-9). Moreover, the performance of PRS models could be efficiently improved by incorporating a small number of mitochondria-related loci (e.g., Pearson correlation test P improved from 1.41×10-3 to 6.09×10-6). PRSs' ability to characterize brain resilience was validated. More importantly, by incorporating some mitochondria-related loci, the performance of PRSs in measuring brain resilience could be significantly improved.

CONCLUSION

Our findings imply that mitochondria may play an important role in brain resilience, and targeting mitochondria may open a new door to AD prevention and therapy.

Selective Vulnerability to Neurodegenerative Disease: Insights from Cell Type-Specific Translatome Studies

Neurodegenerative diseases (NDs) manifest a wide variety of clinical symptoms depending on the affected brain regions. Gaining insights into why certain regions are resistant while others are susceptible is vital for advancing therapeutic strategies. While gene expression changes offer clues about disease responses across brain regions, the mixture of cell types therein obscures experimental results. In recent years, methods that analyze the transcriptomes of individual cells (e.g., single-cell RNA sequencing or scRNAseq) have been widely used and have provided invaluable insights into specific cell types. Concurrently, transgene-based techniques that dissect cell type-specific translatomes (CSTs) in model systems, like RiboTag and bacTRAP, offer unique advantages but have received less attention. This review juxtaposes the merits and drawbacks of both methodologies, focusing on the use of CSTs in understanding conditions like amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), Alzheimer's disease (AD), and specific prion diseases like fatal familial insomnia (FFI), genetic Creutzfeldt-Jakob disease (gCJD), and acquired prion disease. We conclude by discussing the emerging trends observed across multiple diseases and emerging methods.

From theory to practice: translating the concept of cognitive resilience to novel therapeutic targets that maintain cognition in aging adults

While the concept of cognitive resilience is well-established it has not been defined in a way that can be measured. This has been an impediment to studying its underlying biology and to developing instruments for its clinical assessment. This perspective highlights recent work that has quantified the expression of cortical proteins associated with cognitive resilience, thus facilitating studies of its complex underlying biology and the full range of its clinical effects in aging adults. These initial studies provide empirical support for the conceptualization of resilience as a continuum. Like other conventional risk factors, some individuals manifest higher-than-average cognitive resilience and other individuals manifest lower-than-average cognitive resilience. These novel approaches for advancing studies of cognitive resilience can be generalized to other aging phenotypes and can set the stage for the development of clinical tools that might have the potential to measure other mechanisms of resilience in aging adults. These advances also have the potential to catalyze a complementary therapeutic approach that focuses on augmenting resilience via lifestyle changes or therapies targeting its underlying molecular mechanisms to maintain cognition and brain health even in the presence of untreatable stressors like brain pathologies that accumulate in aging adults.

A deep learning model for brain age prediction using minimally preprocessed T1w images as input

Introduction

In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction.

Methods

We used a multicohort dataset of cognitively healthy individuals (age range = 32.0-95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2-4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset's images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4).

Results

The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1-4, respectively. The model's performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63-2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction.

Discussion

We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.

New directions for Alzheimer's disease research from the Jackson Laboratory Center for Alzheimer's and Dementia Research 2022 workshop

INTRODUCTION

In September 2022, The Jackson Laboratory Center for Alzheimer's and Dementia Research (JAX CADR) hosted a workshop with leading researchers in the Alzheimer's disease and related dementias (ADRD) field.

METHODS

During the workshop, the participants brainstormed new directions to overcome current barriers to providing patients with effective ADRD therapeutics. The participants outlined specific areas of focus. Following the workshop, each group used standard literature search methods to provide background for each topic.

RESULTS

The team of invited experts identified four key areas that can be collectively addressed to make a significant impact in the field: (1) Prioritize the diversification of disease targets, (2) enhance factors promoting resilience, (3) de-risk clinical pipeline, and (4) centralize data management.

DISCUSSION

In this report, we review these four objectives and propose innovations to expedite ADRD therapeutic pipelines.

Quantitative estimate of cognitive resilience and its medical and genetic associations

BACKGROUND

We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer's disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain cognitively normal. However, CR traditionally is considered a binary trait, capturing only the most extreme examples, and is often inconsistently defined.

METHODS

This study addressed existing discrepancies and shortcomings of the current CR definition by proposing a framework for defining CR as a continuous variable for each neuropsychological test. The linear equations clarified CR's relationship to closely related terms, including cognitive function, reserve, compensation, and damage. Primarily, resilience is defined as a function of cognitive performance and damage from neuropathologic damage. As such, the study utilized data from 844 individuals (age = 79 ± 12, 44% female) in the National Alzheimer's Coordinating Center cohort that met our inclusion criteria of comprehensive lesion rankings for 17 neuropathologic features and complete neuropsychological test results. Machine learning models and GWAS then were used to identify medical and genetic factors that are associated with CR.

RESULTS

CR varied across five cognitive assessments and was greater in female participants, associated with longer survival, and weakly associated with educational attainment or APOE ε4 allele. In contrast, damage was strongly associated with APOE ε4 allele (P value < 0.0001). Major predictors of CR were cardiovascular health and social interactions, as well as the absence of behavioral symptoms.

CONCLUSIONS

Our framework explicitly decoupled the effects of CR from neuropathologic damage. Characterizations and genetic association study of these two components suggest that the underlying CR mechanism has minimal overlap with the disease mechanism. Moreover, the identified medical features associated with CR suggest modifiable features to counteract clinical expression of damage and maintain cognitive function in older individuals.

Assessment of Physical Resilience Using Residual Methods and Its Association With Adverse Outcomes in Older Adults

Background and Objectives

Physical resilience (PR) is recognized as the ability to recover from the adverse effects of a stressor. However, there is a lack of consensus on how to optimally measure PR in older adults in general. We aimed to measure PR using residuals from regression analyses and investigated its association with adverse outcomes in older adults.

Research Design and Methods

A total of 6 508 older adults were included from the National Health and Aging Trends Study, which was a population-based prospective cohort study. PR was assessed using residual methods from a linear model regressing the short physical performance battery on clinical diseases, age, sex, race/ethnicity, and health condition. Adverse outcomes included all-cause mortality, falls, and overnight hospitalization.

Results

The mean age was 77.48 (7.84) years. Increased PR was associated with a lower risk of all-cause mortality (hazard ratio [HR] = 0.85, 95% confidence interval [CI]: 0.83-0.87). Compared to participants with reduced PR, those with normal PR had a lower risk for mortality (HR = 0.51, 95% CI: 0.46-0.56). Specifically, restricted cubic spline regression revealed a dose-response relationship between PR and all-cause mortality (p-overall < .0001, p-nonlinear = .011). Additionally, we also found significant associations of increased PR with lower risks of falls (HR = 0.98, 95% CI: 0.96-0.99) and overnight hospitalization (HR = 0.98, 95% CI: 0.97-1.00).

Discussion and Implications

PR, measured by residual methods, was robustly and independently associated with all-cause mortality, falls, and overnight hospitalization. Our findings provide evidence that this approach may be a simple and feasible strategy to assess PR.

Determinants of cognitive and brain resilience to tau pathology: a longitudinal analysis

Mechanisms of resilience against tau pathology in individuals across the Alzheimer's disease spectrum are insufficiently understood. Longitudinal data are necessary to reveal which factors relate to preserved cognition (i.e. cognitive resilience) and brain structure (i.e. brain resilience) despite abundant tau pathology, and to clarify whether these associations are cross-sectional or longitudinal. We used a longitudinal study design to investigate the role of several demographic, biological and brain structural factors in yielding cognitive and brain resilience to tau pathology as measured with PET. In this multicentre study, we included 366 amyloid-β-positive individuals with mild cognitive impairment or Alzheimer's disease dementia with baseline 18F-flortaucipir-PET and longitudinal cognitive assessments. A subset (n = 200) additionally underwent longitudinal structural MRI. We used linear mixed-effects models with global cognition and cortical thickness as dependent variables to investigate determinants of cognitive resilience and brain resilience, respectively. Models assessed whether age, sex, years of education, APOE-ε4 status, intracranial volume (and cortical thickness for cognitive resilience models) modified the association of tau pathology with cognitive decline or cortical thinning. We found that the association between higher baseline tau-PET levels (quantified in a temporal meta-region of interest) and rate of cognitive decline (measured with repeated Mini-Mental State Examination) was adversely modified by older age (Stβinteraction = -0.062, P = 0.032), higher education level (Stβinteraction = -0.072, P = 0.011) and higher intracranial volume (Stβinteraction = -0.07, P = 0.016). Younger age, higher education and greater cortical thickness were associated with better cognitive performance at baseline. Greater cortical thickness was furthermore associated with slower cognitive decline independent of tau burden. Higher education also modified the negative impact of tau-PET on cortical thinning, while older age was associated with higher baseline cortical thickness and slower rate of cortical thinning independent of tau. Our analyses revealed no (cross-sectional or longitudinal) associations for sex and APOE-ε4 status on cognition and cortical thickness. In this longitudinal study of clinically impaired individuals with underlying Alzheimer's disease neuropathological changes, we identified education as the most robust determinant of both cognitive and brain resilience against tau pathology. The observed interaction with tau burden on cognitive decline suggests that education may be protective against cognitive decline and brain atrophy at lower levels of tau pathology, with a potential depletion of resilience resources with advancing pathology. Finally, we did not find major contributions of sex to brain nor cognitive resilience, suggesting that previous links between sex and resilience might be mainly driven by cross-sectional differences.

The association of MIND diet with cognitive resilience to neuropathologies

INTRODUCTION

Cognitive resilience (CR) can be defined as the continuum of better through worse than expected cognition, given the degree of neuropathology. The relation of healthy diet patterns to CR remains to be elucidated.

METHODS

Using longitudinal cognitive data and post mortem neuropathology from 578 deceased older adults, we examined associations between the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet at baseline and two standardized CR measures reflecting higher cognitive levels over time (CRLevel ¯ $_{\overline {{\rm{Level}}}} $ ), and slower decline (CRSlope ), than expected given neuropathology.

RESULTS

Compared to individuals in the lowest tertile of MIND score, those in the top tertile had higher CRLevel ¯ $_{\overline {{\rm{Level}}}} $ (mean difference [MD] = 0.34; 95% confidence interval [CI] = 0.14, 0.55) and CRSlope (MD = 0.27; 95% CI = 0.05, 0.48), after multivariable adjustment. Overall MIND score was more strongly related to CR than the individual food components.

DISCUSSION

The MIND diet is associated with both higher cognition and slower rates of cognitive decline, after controlling for neuropathology, indicating the MIND diet may be important to cognitive resilience.

Individual reserve in aging and neurological disease

BACKGROUND AND OBJECTIVE

Cognitive and physical functions correlate and delineate aging and disease trajectories. Whereas cognitive reserve (CR) is well-established, physical reserve (PR) is poorly understood. We, therefore, developed and evaluated a novel and more comprehensive construct, individual reserve (IR), comprised of residual-derived CR and PR in older adults with and without multiple sclerosis (MS). We hypothesized that: (a) CR and PR would be positively correlated; (b) low CR, PR, and IR would be associated with worse study outcomes; (c) associations of brain atrophy with study outcomes would be stronger in lower compared to higher IR due to compensatory mechanisms conferred by the latter.

METHODS

Older adults with MS (n = 66, mean age = 64.48 ± 3.84 years) and controls (n = 66, mean age = 68.20 ± 6.09 years), underwent brain MRI, cognitive assessment, and motoric testing. We regressed the repeatable battery for the assessment of neuropsychological status and short physical performance battery on brain pathology and socio-demographic confounders to derive independent residual CR and PR measures, respectively. We combined CR and PR to define a 4-level IR variable. The oral symbol digit modalities test (SDMT) and timed-25-foot-walk-test (T25FW) served as outcome measures.

RESULTS

CR and PR were positively correlated. Low CR, PR and IR were associated with worse SDMT and T25FW performances. Reduced left thalamic volume, a marker of brain atrophy, was associated with poor SDMT and T25FW performances only in individuals with low IR. The presence of MS moderated associations between IR and T25FW performance.

CONCLUSION

IR is a novel construct comprised of cognitive and physical dimensions representing collective within-person reserve capacities.

Gender, Personality, and Cognitive Resilience Against Early-Life Disadvantage

OBJECTIVES

Early-life disadvantage (ELD) relates to lower late-life cognition. However, personality factors, including having an internal locus of control (LOC) or a conscientious personality, relate to resilience and effective stress coping. We explore whether personality factors convey resilience against the negative effects of ELD on cognition, by gender, in Mexico.

METHODS

Using the 2015 Mexican Health and Aging Study, we estimated expected cognition using multiple ELD markers to identify a subsample in the lowest quartile of expected cognition given ELD (n = 2,086). In this subsample, we estimated cross-sectional associations between personality and having above-median observed cognitive ability (n = 522) using logistic regression.

RESULTS

Among those in the lowest quartile of expected cognition, a more internal LOC (β = 0.32 [men] and β = 0.44 [women]) and conscientious personality (β = 0.39 [men] and β = 0.17 [women]) were significantly associated with having above-median cognitive ability in models adjusted for demographic confounders. Larger benefits of conscientiousness were observed for men than women. Associations between personality and having above-median cognitive ability remained statistically significant after further adjustment for health, stress, and cognitive stimulation variables, regardless of gender.

DISCUSSION

Personality factors may convey resilience among individuals who experienced ELD, potentially breaking the link between ELD and worse late-life cognition. Structural factors and gender roles may affect how much women benefit from personality factors.

Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing

Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer's disease. However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied similarity network fusion, a method capable of integrating multiple high-dimensional multi-omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive ageing trajectories. We analyzed human frontal cortex brain samples characterized by five omic modalities: bulk RNA sequencing (18 629 genes), DNA methylation (53 932 CpG sites), histone acetylation (26 384 peaks), proteomics (7737 proteins) and metabolomics (654 metabolites). Similarity network fusion followed by spectral clustering was used for subtype detection, and subtype numbers were determined by Eigen-gap and rotation cost statistics. Normalized mutual information determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Fusion of all five data modalities (n = 111) yielded two subtypes (n S1 = 53, n S2 = 58), which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (normalized mutual information = 0.38), DNA methylation (normalized mutual information = 0.18) and RNA abundance (normalized mutual information = 0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n = 513) indicated support for both three- and five-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both three- and five-subtype solutions (p Bonf = 5.9 × 10-3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. We identified highly integrative molecular subtypes of ageing derived from multiple high dimensional, multi-omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and histone acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-omic integration in unsupervised subtyping of post-mortem brain.

Detecting the effect of genetic diversity on brain composition in an Alzheimer's disease mouse model

Alzheimer's disease (AD) is characterized by neurodegeneration, pathology accumulation, and progressive cognitive decline. There is significant variation in age at onset and severity of symptoms highlighting the importance of genetic diversity in the study of AD. To address this, we analyzed cell and pathology composition of 6- and 14-month-old AD-BXD mouse brains using the semi-automated workflow (QUINT); which we expanded to allow for nonlinear refinement of brain atlas-registration, and quality control assessment of atlas-registration and brain section integrity. Near global age-related increases in microglia, astrocyte, and amyloid-beta accumulation were measured, while regional variation in neuron load existed among strains. Furthermore, hippocampal immunohistochemistry analyses were combined with bulk RNA-sequencing results to demonstrate the relationship between cell composition and gene expression. Overall, the additional functionality of the QUINT workflow delivers a highly effective method for registering and quantifying cell and pathology changes in diverse disease models.

A Residual Marker of Cognitive Reserve Is Associated with Resting-State Intrinsic Functional Connectivity Along the Alzheimer's Disease Continuum

BACKGROUND

Cognitive reserve (CR) explains inter-individual differences in the impact of the neurodegenerative burden on cognitive functioning. A residual model was proposed to estimate CR more accurately than previous measures. However, associations between residual CR markers (CRM) and functional connectivity (FC) remain unexplored.

OBJECTIVE

To explore the associations between the CRM and intrinsic network connectivity (INC) in resting-state networks along the neuropathological-continuum of Alzheimer's disease (ADN).

METHODS

Three hundred eighteen participants from the DELCODE cohort were stratified using cerebrospinal fluid biomarkers according to the A(myloid-β)/T(au)/N(eurodegeneration) classification. CRM was calculated utilizing residuals obtained from a multilinear regression model predicting cognition from markers of disease burden. Using an independent component analysis in resting-state fMRI data, we measured INC of resting-state networks, i.e., default mode network (DMN), frontoparietal network (FPN), salience network (SAL), and dorsal attention network. The associations of INC with a composite memory score and CRM and the associations of CRM with the seed-to-voxel functional connectivity of memory-related were tested in general linear models.

RESULTS

CRM was positively associated with INC in the DMN in the entire cohort. The A+T+N+ group revealed an anti-correlation between the SAL and the DMN. Furthermore, CRM was positively associated with anti-correlation between memory-related regions in FPN and DMN in ADN and A+T/N+.

CONCLUSION

Our results provide evidence that INC is associated with CRM in ADN defined as participants with amyloid pathology with or without cognitive symptoms, suggesting that the neural correlates of CR are mirrored in network FC in resting-state.

The development and validation of a new resilience inventory based on inner strength

There are a number of resilience scales with good psychometric properties. However, the various scales differ in their item content in accordance with the model of resilience the developer had in mind. Culture is one of the reasons for the difference. Thailand, one of the Buddhist cultures, has a different view on resilience compared with Western culture. This study aimed to develop and validate a resilience inventory created based on the inner strength concept using a confirmatory factor analysis (CFA) and Rasch measurement model. The resilience inventory (RI) was developed by creating new items representing inner strengths attributed to resilience. The inner strength was adopted to form the resilience construct, including perseverance, wisdom, patience, mindfulness, loving-kindness and equanimity. In addition, face and content validity were examined by experts in both mental health and Buddhism. The final RI comprised nine items with a 5-point Likert-type scale. The RI-9 was completed by 243 medical students who participated in the study, along with other measurements, i.e., Inner Strength-Based Inventory (iSBI), measuring the ten characteristics of perfection or inner strength, and the Core Symptom Index, measuring anxiety, depression and somatization symptoms. CFA, internal consistency and the Polytomous Rasch rating model were used to investigate the RI-9 construct validity. The mean age of the participants was 22.7 years (SD, 0.8); one-half were male (50%). The RI-9 construct demonstrated item hierarchy as follows: perseverance, patience (tolerance), mindfulness and equanimity, wisdom and loving-kindness. CFA showed that the unidimensional model fitted the data well. Rasch analysis showed no misfitting items and local dependence. The reliability of the person and item was good, and no disordered threshold was observed. Two items were found to exhibit differential item functioning due to sex. RI-9 scores were significantly related to all ten strengths from the iSBI, whereas they were negatively related to depression, anxiety, somatization and interpersonal difficulties. The RI-9 demonstrated validity and reliability. It constitutes a promising tool for outcome assessment in nonclinical populations. Further investigation on external validity as well as psychometric validation in other different cultures, should be encouraged.

An empirical measure of resilience explains individual differences in the effect of tau pathology on memory change in aging

Accurately measuring resilience to preclinical Alzheimer's disease (AD) pathology is essential to understanding an important source of variability in cognitive aging. In a cohort of cognitively normal older adults (n = 123, age 76.75 ± 6.15 yr), we built a multifactorial measure of resilience which moderated the effect of AD pathology on longitudinal cognitive change. Linear residuals-based measures of resilience, along with other proxy measures (education and vocabulary), were entered into a hierarchical partial least-squares path model defining a putative consolidated resilience latent factor (model goodness of fit = 0.77). In a set of validation analyses using linear mixed models predicting longitudinal cognitive change, there was a significant three-way interaction among consolidated resilience, tau and time on episodic memory change (P = 0.001) such that higher resilience blunted the effect of tau pathology on episodic memory decline. Interactions between consolidated resilience and amyloid pathology on non-memory cognition decline suggested that resilience moderates pathology-specific effects on different cognitive domains.

Neuropathology of the Common Forms of Dementia

Dementias encompass a range of debilitating neurologic conditions. Here, we summarize the neuropathology of common forms of dementia, focusing on Alzheimer disease (AD) and related dementias. AD is part of a spectrum of neurodegenerative diseases that consists of various protein inclusions (ie, proteinopathies) but other brain abnormalities are also related to dementia. Beta-amyloid and tau aggregates are hallmarks of AD. Other tissue substrates include Lewy bodies, TDP-43 inclusions, vascular brain lesions, and mixed pathologies. This review highlights the complexity of neurodegenerative and other disease substrates and summarizes topography of these lesions and concepts of mixed brain pathologies, resistance, and resilience.

Brain Reserve, Resilience, and Cognitive Stimulation Across the Lifespan: How Do These Factors Influence Risk of Cognitive Impairment and the Dementias?

In the absence of effective treatments for dementia, maintaining cognitive health in old age is one of the major challenges facing aging societies. Interventions for cognitive health that are tailored to the person are more likely to bring the best benefits with a minimum burden. We review the existing literature on this topic and discuss the role of the primary care physician.

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study

Background

Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored.

Methods

We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite.

Results

All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance.

Conclusions

Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences.

Funding

This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).

The aging mind: A complex challenge for research and practice

Cognitive decline as part of mental ageing is typically assessed with standardized tests; below-average performance in such tests is used as an indicator for pathological cognitive aging. In addition, morphological and functional changes in the brain are used as parameters for age-related pathological decline in cognitive abilities. However, there is no simple link between the trajectories of changes in cognition and morphological or functional changes in the brain. Furthermore, below-average test performance does not necessarily mean a significant impairment in everyday activities. It therefore appears crucial to record individual everyday tasks and their cognitive (and other) requirements in functional terms. This would also allow reliable assessment of the ecological validity of existing and insufficient cognitive skills. Understanding and dealing with the phenomena and consequences of mental aging does of course not only depend on cognition. Motivation and emotions as well personal meaning of life and life satisfaction play an equally important role. This means, however, that cognition represents only one, albeit important, aspect of mental aging. Furthermore, creating and development of proper assessment tools for functional cognition is important. In this contribution we would like to discuss some aspects that we consider relevant for a holistic view of the aging mind and promote a strengthening of a multidisciplinary approach with close cooperation between all basic and applied sciences involved in aging research, a quick translation of the research results into practice, and a close cooperation between all disciplines and professions who advise and support older people.

Quantifying longitudinal cognitive resilience to Alzheimer's disease and other neuropathologies

INTRODUCTION

Cognitive resilience (CR) has been defined as the continuum of better (or worse) than expected cognition, given the degree of neuropathology. To quantify this concept, existing approaches focus on either cognitive level at a single time point or slopes of cognitive decline.

METHODS

In a prospective study of 1215 participants, we created a continuous measure of CR defined as the mean of differences between estimated person-specific and marginal cognitive levels over time, after accounting for neuropathologies.

RESULTS

Neuroticism and depressive symptoms were associated with all CR measures (P-values < .012); as expected, cognitive activity and education were only associated with the cognitive-level approaches (P-values < .0002). However, compared with the existing CR measures focusing on a single measure or slopes of cognition, our new measure yielded stronger relations with risk factors.

DISCUSSION

Defining CR based on the longitudinal differences between person-specific and marginal cognitive levels is a novel and complementary way to quantify CR.

Axonal degeneration and amyloid pathology predict cognitive decline beyond cortical atrophy

BACKGROUND

Cortical atrophy is associated with cognitive decline, but the association is not perfect. We aimed to identify factors explaining the discrepancy between the degree of cortical atrophy and cognitive decline in cognitively unimpaired elderly.

METHODS

The discrepancy between atrophy and cognitive decline was measured using the residuals from a linear regression analysis between change in whole brain cortical thickness over time and change in a cognitive composite measure over time in 395 cognitively unimpaired participants from the Swedish BioFINDER study. We tested for bivariate associations of this residual measure with demographic, imaging, and fluid biomarker variables using Pearson correlations and independent-samples t-tests, and for multivariate associations using linear regression models. Mediation analyses were performed to explore possible paths between the included variables.

RESULTS

In bivariate analyses, older age (r = -0.11, p = 0.029), male sex (t = -3.00, p = 0.003), larger intracranial volume (r = -0.17, p < 0.001), carrying an APOEe4 allele (t = -2.71, p = 0.007), larger white matter lesion volume (r = -0.16, p = 0.002), lower cerebrospinal fluid (CSF) β-amyloid (Aβ) 42/40 ratio (t = -4.05, p < 0.001), and higher CSF levels of phosphorylated tau (p-tau) 181 (r = -0.22, p < 0.001), glial fibrillary acidic protein (GFAP; r = -0.15, p = 0.003), and neurofilament light (NfL; r = -0.34, p < 0.001) were negatively associated with the residual measure, i.e., associated with worse than expected cognitive trajectory given the level of atrophy. In a multivariate analysis, only lower CSF Aβ42/40 ratio and higher CSF NfL levels explained cognition beyond brain atrophy. Mediation analyses showed that associations between the residual measure and APOEe4 allele, CSF Aβ42/40 ratio, and CSF GFAP and p-tau181 levels were mediated by levels of CSF NfL, as were the associations with the residual measure for age, sex, and WML volume.

CONCLUSIONS

Our results suggest that axonal degeneration and amyloid pathology independently affect the rate of cognitive decline beyond the degree of cortical atrophy. Furthermore, axonal degeneration mediated the negative effects of old age, male sex, and white matter lesions, and in part also amyloid and tau pathology, on cognition over time when accounting for cortical atrophy.

Residual reserve index modifies the effect of amyloid pathology on fluorodeoxyglucose metabolism: Implications for efficiency and capacity in cognitive reserve

Background

The residual approach to measuring cognitive reserve (using the residual reserve index) aims to capture cognitive resilience conferred by cognitive reserve, but may be confounded by factors representing brain resilience. We sought to distinguish between brain and cognitive resilience by comparing interactions between the residual reserve index and amyloid, tau, and neurodegeneration [“AT(N)”] biomarkers when predicting executive function. We hypothesized that the residual reserve index would moderate at least one path from an AT(N) biomarker to executive function (consistent with cognitive resilience), as opposed to moderating a path between two AT(N) biomarkers (suggestive of brain resilience).

Methods

Participants (N = 332) were from the Alzheimer’s Disease Neuroimaging Initiative. The residual reserve index represented the difference between observed and predicted memory performance (a positive residual reserve index suggests higher cognitive reserve). AT(N) biomarkers were: CSF β-amyloid_1–42/β-amyloid_1–40 (A), plasma phosphorylated tau-181 (T), and FDG metabolism in AD-specific regions ([N]). AT(N) biomarkers (measured at consecutive time points) were entered in a sequential mediation model testing the indirect effects from baseline amyloid to executive function intercept (third annual follow-up) and slope (baseline to seventh follow-up), via tau and/or FDG metabolism. The baseline residual reserve index was entered as a moderator of paths between AT(N) biomarkers (e.g., amyloid-tau), and paths between AT(N) biomarkers and executive function.

Results

The residual reserve index interacted with amyloid pathology when predicting FDG metabolism: the indirect effect of amyloid → FDG metabolism → executive function intercept and slope varied as a function of the residual reserve index. With lower amyloid pathology, executive function performance was comparable at different levels of the residual reserve index, but a higher residual reserve index was associated with lower FDG metabolism. With higher amyloid pathology, a higher residual reserve index predicted better executive function via higher FDG metabolism.

Conclusion

The effect of the residual reserve index on executive function performance via FDG metabolism was consistent with cognitive resilience. This suggests the residual reserve index captures variation in cognitive reserve; specifically, neural efficiency, and neural capacity to upregulate metabolism to enhance cognitive resilience in the face of greater amyloid pathology. Implications for future research include the potential bidirectionality between neural efficiency and amyloid accumulation.

Issues and recommendations for the residual approach to quantifying cognitive resilience and reserve

BACKGROUND

Cognitive reserve and resilience are terms used to explain interindividual variability in maintenance of cognitive health in response to adverse factors, such as brain pathology in the context of aging or neurodegenerative disorders. There is substantial interest in identifying tractable substrates of resilience to potentially leverage this phenomenon into intervention strategies. One way of operationalizing cognitive resilience that has gained popularity is the residual method: regressing cognition on an adverse factor and using the residual as a measure of resilience. This method is attractive because it provides a statistical approach that is an intuitive match to the reserve/resilience conceptual framework. However, due to statistical properties of the regression equation, the residual approach has qualities that complicate its interpretation as an index of resilience and make it statistically inappropriate in certain circumstances.

METHODS AND RESULTS

We describe statistical properties of the regression equation to illustrate why the residual is highly correlated with the cognitive score from which it was derived. Using both simulations and real data, we model common applications of the approach by creating a residual score (global cognition residualized for hippocampal volume) in individuals along the AD spectrum. We demonstrate that in most real-life scenarios, the residual measure of cognitive resilience is highly correlated with cognition, and the degree of this correlation depends on the initial relationship between the adverse factor and cognition. Subsequently, any association between this resilience metric and an external variable may actually be driven by cognition, rather than by an operationalized measure of resilience. We then assess several strategies proposed as potential solutions to this problem, such as including both the residual and original cognitive measure in a model. However, we conclude these solutions may be insufficient, and we instead recommend against "pre-regression" strategies altogether in favor of using statistical moderation (e.g., interactions) to quantify resilience.

CONCLUSIONS

Caution should be taken in the use and interpretation of the residual-based method of cognitive resilience. Rather than identifying resilient individuals, we encourage building more complete models of cognition to better identify the specific adverse and protective factors that influence cognitive decline.

Transfer learning for cognitive reserve quantification

Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the Structural Magnetic Resonance Imaging (sMRI) data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimer's cohorts using transfer learning framework. Structural MRIs were collected from three cohorts: 495 healthy adults (age: 20-80) from RANN, 620 healthy adults (age: 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 adults (age: 55-92) from Alzheimer's Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. CR was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (RANN) to the target domains (HCPA or ADNI). The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets. The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes.

Cognitive Reserve and Related Constructs: A Unified Framework Across Cognitive and Brain Dimensions of Aging

Cognitive reserve and related constructs are valuable for aging-related research, but consistency and clarification of terms is needed as there is still no universally agreed upon nomenclature. We propose a new set of definitions for the concepts of reserve, maintenance, and resilience, and we invoke parallel concepts for each that are applicable to cognition and to brain. Our definitions of reserve and resilience correspond reasonably well to dictionary definitions of these terms. We demonstrate logical/methodological problems that arise from incongruence between commonly used conceptual and operational definitions. In our view, cognitive reserve should be defined conceptually as one's total cognitive resources at a given point in time. IQ and education are examples of common operational definitions (often referred to as proxies) of cognitive reserve. Many researchers define cognitive reserve conceptually as a property that allows for performing better than expected cognitively in the face of aging or pathology. Performing better than expected is demonstrated statistically by interactions in which the moderator is typically IQ or education. The result is an irreconcilable situation in which cognitive reserve is both the moderator and the moderation effect itself. Our proposed nomenclature resolves this logical inconsistency by defining performing better than expected as cognitive resilience. Thus, in our usage, we would test the hypothesis that high cognitive reserve confers greater cognitive resilience. Operational definitions (so-called proxies) should not conflate factors that may influence reserve-such as occupational complexity or engagement in cognitive activities-with cognitive reserve itself. Because resources may be depleted with aging or pathology, one's level of cognitive reserve may change over time and will be dependent on when assessment takes place. Therefore, in addition to cognitive reserve and cognitive resilience, we introduce maintenance of cognitive reserve as a parallel to brain maintenance. If, however, education is the measure of reserve in older adults, it precludes assessing change or maintenance of reserve. Finally, we discuss consideration of resistance as a subcategory of resilience, reverse causation, use of residual scores to assess performing better than expected given some adverse factor, and what constitutes high vs. low cognitive reserve across different studies.

Translational approaches to understanding resilience to Alzheimer's disease

Individuals who maintain cognitive function despite high levels of Alzheimer's disease (AD)-associated pathology are said to be 'resilient' to AD. Identifying mechanisms underlying resilience represents an exciting therapeutic opportunity. Human studies have identified a number of molecular and genetic factors associated with resilience, but the complexity of these cohorts prohibits a complete understanding of which factors are causal or simply correlated with resilience. Genetically and phenotypically diverse mouse models of AD provide new and translationally relevant opportunities to identify and prioritize new resilience mechanisms for further cross-species investigation. This review will discuss insights into resilience gained from both human and animal studies and highlight future approaches that may help translate these insights into therapeutics designed to prevent or delay AD-related dementia.

Pleiotropic predisposition to Alzheimer's disease and educational attainment: insights from the summary statistics analysis

Epidemiological studies report beneficial associations of higher educational attainment (EDU) with Alzheimer's disease (AD). Prior genome-wide association studies (GWAS) also reported variants associated with AD and EDU separately. The analysis of pleiotropic associations with these phenotypes may shed light on EDU-related protection against AD. We performed pleiotropic meta-analyses using Fisher's method and omnibus test applied to summary statistics for single nucleotide polymorphisms (SNPs) associated with AD and EDU in large-scale univariate GWAS at suggestive-effect (5 × 10-8 < p < 0.1) and genome-wide (p ≤ 5 × 10-8) significance levels. We report 53 SNPs that attained p ≤ 5 × 10-8 at least in one of the pleiotropic meta-analyses and were reported in the univariate GWAS at 5 × 10-8 < p < 0.1. Of them, there were 46 pleiotropic SNPs according to Fisher's method. Additionally, Fisher's method identified 25 of 206 SNPs with pleiotropic effects, which attained p ≤ 5 × 10-8 in the univariate GWAS. We showed that a large fraction of the pleiotropic associations was affected by a counterintuitive phenomenon of antagonistic genetic heterogeneity, which explains the increase, rather than decrease, of the significance of the pleiotropic associations in the omnibus test. Functional enrichment analysis showed that apart from cancers, gene set harboring the non-pleiotropic SNPs was characterized by late-onset AD and neurodevelopmental disorders. The pleiotropic gene set was characterized by a broad spectrum of progressive neurological and neuromuscular diseases and immune-mediated conditions, including progressive motor neuropathy, multiple sclerosis, Parkinson's disease, and severe AD. Our results suggest that disentangling genes harboring variants with and without pleiotropic associations with AD and EDU is promising for dissecting heterogeneity in biological mechanisms of AD.

Cognitive Reserve and Related Constructs: A Unified Framework Across Cognitive and Brain Dimensions of Aging

Cognitive reserve and related constructs are valuable for aging-related research, but consistency and clarification of terms is needed as there is still no universally agreed upon nomenclature. We propose a new set of definitions for the concepts of reserve, maintenance, and resilience, and we invoke parallel concepts for each that are applicable to cognition and to brain. Our definitions of reserve and resilience correspond reasonably well to dictionary definitions of these terms. We demonstrate logical/methodological problems that arise from incongruence between commonly used conceptual and operational definitions. In our view, cognitive reserve should be defined conceptually as one's total cognitive resources at a given point in time. IQ and education are examples of common operational definitions (often referred to as proxies) of cognitive reserve. Many researchers define cognitive reserve conceptually as a property that allows for performing better than expected cognitively in the face of aging or pathology. Performing better than expected is demonstrated statistically by interactions in which the moderator is typically IQ or education. The result is an irreconcilable situation in which cognitive reserve is both the moderator and the moderation effect itself. Our proposed nomenclature resolves this logical inconsistency by defining performing better than expected as cognitive resilience. Thus, in our usage, we would test the hypothesis that high cognitive reserve confers greater cognitive resilience. Operational definitions (so-called proxies) should not conflate factors that may influence reserve-such as occupational complexity or engagement in cognitive activities-with cognitive reserve itself. Because resources may be depleted with aging or pathology, one's level of cognitive reserve may change over time and will be dependent on when assessment takes place. Therefore, in addition to cognitive reserve and cognitive resilience, we introduce maintenance of cognitive reserve as a parallel to brain maintenance. If, however, education is the measure of reserve in older adults, it precludes assessing change or maintenance of reserve. Finally, we discuss consideration of resistance as a subcategory of resilience, reverse causation, use of residual scores to assess performing better than expected given some adverse factor, and what constitutes high vs. low cognitive reserve across different studies.

Sex Differences in Resilience and Resistance to Brain Pathology and Dysfunction Moderated by Cerebrovascular Response to Exercise and Genetic Risk for Alzheimer's Disease

Sex as a biological variable appears to contribute to the multifactorial etiology of Alzheimer's disease. We tested sex-based interactions between cerebrovascular function and APOE4 genotype on resistance and resilience to brain pathology and cognitive executive dysfunction in cognitively-normal older adults. Female APOE4 carriers had higher amyloid-β deposition yet achieved similar cognitive performance to males and female noncarriers. Further, female APOE4 carriers with robust cerebrovascular responses to exercise possessed lower amyloid-β. These results suggest a unique cognitive resilience and identify cerebrovascular function as a key mechanism for resistance to age-related brain pathology in females with high genetic vulnerability to Alzheimer's disease.

Exploring Cortical Proteins Underlying the Relation of Neuroticism to Cognitive Resilience

Some individuals maintain cognitive health despite neuropathology. Targets impacting "cognitive resilience" may provide interventions for preventing dementia without decreasing neuropathology. Neuroticism represents the tendency to experience negative emotions, and is related to worse cognitive resilience. Exploring proteins associated with cognitive resilience risk factors, such as neuroticism, could yield new protein targets. We used 355 postmortem prefrontal cortex from two cohorts to measure 8356 proteins. We identified (i) proteins associated with both neuroticism and cognitive resilience, and (ii) proteins statistically mediating relations of neuroticism to cognitive resilience. We found two proteins, 40S ribosomal proteinS3 (RPS3) and branched chain keto acid dehydrogenase E1, subunit beta (BCKDHB), ranked in the top 1% of smallest p-values in parallel linear regression models of neuroticism to protein levels, and protein levels to cognitive decline resilience. In mediation models, RPS3 and BCKDHB accounted for 25% (p=0.005) of the relation of neuroticism to cognitive resilience. Our sample size is modest, thus results may be due to chance (p-values did not meet Bonferroni significance) and will require further confirmation; however, investigating biologic mediators of associations of risk factors to cognitive resilience may help discover targets to promote cognitive resilience and reduce dementia.

Cognitive Trajectories and Dementia Risk: A Comparison of Two Cognitive Reserve Measures

Background and Objectives

Cognitive reserve (CR) is meant to account for the mismatch between brain damage and cognitive decline or dementia. Generally, CR has been operationalized using proxy variables indicating exposure to enriching activities (activity-based CR). An alternative approach defines CR as residual variance in cognition, not explained by the brain status (residual-based CR). The aim of this study is to compare activity-based and residual-based CR measures in their association with cognitive trajectories and dementia. Furthermore, we seek to examine if the two measures modify the impact of brain integrity on cognitive trajectories and if they predict dementia incidence independent of brain status.

Methods

We used data on 430 older adults aged 60+ from the Swedish National Study on Aging and Care in Kungsholmen, followed for 12 years. Residual-based reserve was computed from a regression predicting episodic memory with a brain-integrity index incorporating six structural neuroimaging markers (white-matter hyperintensities volume, whole-brain gray matter volume, hippocampal volume, lateral ventricular volume, lacunes, and perivascular spaces), age, and sex. Activity-based reserve incorporated education, work complexity, social network, and leisure activities. Cognition was assessed with a composite of perceptual speed, semantic memory, letter-, and category fluency. Dementia was clinically diagnosed in accordance with DSM-IV criteria. Linear mixed models were used for cognitive change analyses. Interactions tested if reserve measures modified the association between brain-integrity and cognitive change. Cox proportional hazard models, adjusted for brain-integrity index, assessed dementia risk.

Results

Both reserve measures were associated with cognitive trajectories [β × time (top tertile, ref.: bottom tertile) = 0.013; 95% CI: –0.126, –0.004 (residual-based) and 0.011; 95% CI: –0.001, 0.024, (activity-based)]. Residual-based, but not activity-based reserve mitigated the impact of brain integrity on cognitive decline [β (top tertile × time × brain integrity) = –0.021; 95% CI: –0.043, 0.001] and predicted 12-year dementia incidence, after accounting for the brain-integrity status [HR (top tertile) = 0.23; 95% CI: 0.09, 0.58].

Interpretation

The operationalization of reserve based on residual cognitive performance may represent a more direct measure of CR than an activity-based approach. Ultimately, the two models of CR serve largely different aims. Accounting for brain integrity is essential in any model of reserve.