Model Selection for Exposure-Mediator Interaction

In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators (M) and exposure-by-mediator (X-by-M) interactions. Although several high-dimensional mediation methods can naturally handle X-by-M interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, we develop the XMInt procedure to select M and X-by-M interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments showed promising selection results. Further, we applied our method to ADNI morphological data and examined the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.Comment: 15 pages, 3 figure

Selective Association between Cortical Thickness and Reference Abilities in Normal Aging

A previous study of reference abilities and cortical thickness reported that association between reference abilities and cortical thickness summarized over large ROIs suppressed was suppressed after controlling for mean cortical thickness and global cognition. In this manuscript, we showed that preserving detailed spatial patterns of cortical thickness can identify reference-ability-specific association besides the association explained by global cognition and mean cortical thickness. We identified associations between cortical thickness and 3 cognitive reference abilities after controlling for mean thickness, global cognition, and linear chronological age: (1) memory, (2) perceptual speed, and (3) vocabulary. Global cognition was correlated with mean overall thickness but also was found to have a regionally specific pattern of associations. Nonlinear associations between cortical thickness and cognition were not observed, neither were nonlinear age effects. Age-by-thickness interactions were also absent. This implies that all thickness-cognition relations and age associations are independent of age and that consequently no age range is inherently special, since brain-behavioral findings are invariant across the whole age range

Personality‐cognition associations across the adult life span and potential moderators: Results from two cohorts

Objective: Personality and cognitive abilities have been previously linked. However, there are inconsistencies regarding whether this relationship varies as a function of age, and a lack of evidence on whether gender contributes to this relation, particularly across the adulthood. Therefore, this study investigated the association between personality and cognition across the adult life span, accounting for age and gender. Methods: We examined the association between personality and cognition in two large samples (Sample 1: N = 422; Sample 2: N = 549) including young, middle‐aged and older adults. Participants completed personality scales and several cognitive measures related to reasoning, language, memory and speed of processing. Structural equation modeling was applied in order to investigate associations between personality and cognition, and moderation of age and gender within this relationship. We also conducted a mini‐meta‐analysis procedure in order to examine personality‐cognition associations, combining results from the two samples. Results: Openness was the main trait associated with cognitive performance; however, Extraversion, Conscientiousness, and Neuroticism were also independently associated with cognition. Age and gender did not consistently moderate personality‐cognition in each sample, but the mini‐metanalysis showed that gender moderated Conscientiousness‐cognition associations. Conclusions: We provided robust evidence of personality‐cognition associations across the adult life span, which was not consistently moderated by age, but in part by gender

Sex moderates the effect of aerobic exercise on some aspects of cognition in cognitively intact younger and middle-age adults

We recently reported the results of a randomized, parallel-group, observer-masked, community-based clinical trial of 132 cognitively normal individuals aged 20–67 with below median aerobic capacity who were randomly assigned to one of two 6-month, four-times-weekly conditions: aerobic exercise and stretching/toning. We now assessed potential sex moderation on exercise-related changes in aerobic capacity, BMI and cognitive function. There was no sex moderation of the effect of aerobic exercise on aerobic capacity or BMI. We had previously reported an effect of aerobic exercise on executive function that was moderated by age. We found additional moderation by sex, such that in any age range men improved more than women. Processing speed showed significant sex moderation but not significant age moderation. In men, processing speed significantly improved by week 12 (b = 0.35, p = 0.0051), but the effect was diminished relative to week 12 at week 24 (b = 0.24, p = 0.0578). In women, there was no exercise effect at either time point (week 12: b = −0.06, p = 0.4156; week 24: b = −0.11, p = 0.1841). Men benefited cognitively more than women from aerobic exercise. This study highlights the importance of using sex-disaggregated analyses when assessing the impact of physical exercise intervention, and the need to ascertain the underlying mechanisms for differential cognitive benefit by sex

Diagnosis and Prognosis Using Machine Learning Trained on Brain Morphometry and White Matter Connectomes

Accurate, reliable prediction of risk for Alzheimer’s disease (AD) is essential for early, diseasemodifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of commonly available multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (study 1: Ilsan Dementia Cohort; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 subjective memory complaints [SMC]) to test and validate the diagnostic models; and, secondly, Alzheimer’s Disease Neuroimaging Initiative (ADNI)-2 (study 2) to test the generalizability of the approach and the prognostic models with longitudinal follow up data. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) with iterative nested cross-validation in a single-site study, outperforming the benchmark model (FLAIR-based white matter hyperintensity volumes). In a generalizability study using ADNI-2, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) as CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). We also predicted MCI to AD progression with 69% accuracy, compared with the 70% accuracy using CSF biomarker model. The optimal classification accuracy in a single-site dataset and the reproduced results in multisite dataset show the feasibility of the high-throughput imaging analysis of multimodal MRI and data-driven machine learning for predictive modeling in AD

Dynamic Patterns of Brain Structure-Behavior Correlation across the Lifespan

Although the brain/behavior correlation is one of the premises of cognitive neuroscience, there is still no consensus about the relationship between brain measures and cognitive function, and only little is known about the effect of age on this relationship. We investigated the age-associated variations on the spatial patterns of cortical thickness correlates of four cognitive domains. We showed that the spatial distribution of the cortical thickness correlates of each cognitive domain is distinctive and depicts varying age-association differences across the adult lifespan. Specifically, the present study provides evidence that distinct cognitive domains are associated with unique structural patterns in three adulthood periods: Early, middle, and late adulthood. These findings suggest a dynamic interaction between multiple neural substrates supporting each cognitive domain across the adult lifespan

The relationship between white matter hyperintensities and cognitive reference abilities across the life span

We examined the relationship between white matter hyperintensities (WMH) burden and performance on 4 reference abilities: episodic memory, perceptual speed, fluid reasoning, and vocabulary. Cross-sectional data of 486 healthy adults from 20 to 80 years old enrolled in an ongoing longitudinal study were analyzed. A piecewise regression across age identified an inflection point at 43 years old, where WMH total volume began to increase with age. Subsequent analyses focused on participants above that age (N = 351). WMH total volume had significant inverse correlations with perceptual speed and memory. Regional measures of WMH showed inverse correlations with all reference abilities. We performed principal component analysis of the regional WMH data to create a model of principal components regression. Parietal WMH regional volume burden mediated the relationship between age and perceptual speed in simple and multiple mediation models. The principal components regression pattern associated with perceptual speed also mediated the relationship between age and perceptual speed performance. These results across the extended adult life span help clarify the influence of WMH on cognitive aging

Dependence Clusters in Alzheimer Disease and Medicare Expenditures: A Longitudinal Analysis From the Predictors Study

Introduction: Dependence in Alzheimer disease has been proposed as a holistic, transparent, and meaningful representation of disease severity. Modeling clusters in dependence trajectories can help understand changes in disease course and care cost over time. Methods: Sample consisted of 199 initially community-living patients with probable Alzheimer disease recruited from 3 academic medical centers in the United States followed for up to 10 years and had ≥2 Dependence Scale recorded. Nonparametric K-means cluster analysis for longitudinal data (KmL) was used to identify dependence clusters. Medicare expenditures data (1999-2010) were compared between clusters. Results: KmL identified 2 distinct Dependence Scale clusters: (A) high initial dependence, faster decline, and (B) low initial dependence, slower decline. Adjusting for patient characteristics, 6-month Medicare expenditures increased over time with widening between-cluster differences. Discussion: Dependence captures dementia care costs over time. Better characterization of dependence clusters has significant implications for understanding disease progression, trial design and care planning

Neonatal brain metabolite concentrations: Associations with age, sex, and developmental outcomes

Age and sex differences in brain metabolite concentrations in early life are not well under- stood. We examined the associations of age and sex with brain metabolite levels in healthy neonates, and investigated the associations between neonatal brain metabolite concentrations and developmental outcomes. Forty-one infants (36–42 gestational weeks at birth; 39% female) of predominantly Hispanic/Latina mothers (mean 18 years of age) underwent MRI scanning approximately two weeks after birth. Multiplanar chemical shift imaging was used to obtain voxel-wise maps of N-acetylaspartate (NAA), creatine, and choline concentrations across the brain. The Bayley Scales of Infant and Toddler Development, a measure of cognitive, language, and motor skills, and mobile conjugate reinforcement paradigm, a measure of learning and memory, were administered at 4 months of age. Findings indicated that postmenstrual age correlated positively with NAA concentrations in multiple subcortical and white matter regions. Creatine and choline concentrations showed similar but less pronounced age related increases. Females compared with males had higher metabolite levels in white matter and subcortical gray matter. Neonatal NAA concentrations were positively associated with learning and negatively associated with memory at 4 months. Age-related increases in NAA, creatine, and choline suggest rapid development of neuronal viability, cel- lular energy metabolism, and cell membrane turnover, respectively, during early life. Females may undergo earlier and more rapid regional developmental increases in the density of viable neurons compared to males