White Matter Tract Covariance Patterns Predict Age-Declining Cognitive Abilities

Previous studies investigating the relationship of white matter (WM) integrity to cognitive abilities and aging have either focused on a global measure or a few selected WM tracts. Ideally, contribution from all of the WM tracts should be evaluated at the same time. However, the high collinearity among WM tracts precludes systematic examination of WM tracts simultaneously without sacrificing statistical power due to stringent multiple-comparison corrections. Multivariate covariance techniques enable comprehensive simultaneous examination of all WM tracts without being penalized for high collinearity among observations. METHOD: In this study, Scaled Subprofile Modeling (SSM) was applied to the mean integrity of 18 major WM tracts to extract covariance patterns that optimally predicted four cognitive abilities (perceptual speed, episodic memory, fluid reasoning, and vocabulary) in 346 participants across ages 20 to 79years old. Using expression of the covariance patterns, age-independent effects of white matter integrity on cognition and the indirect effect of WM integrity on age-related differences in cognition were tested separately, but inferences from the indirect analyses were cautiously made given that cross-sectional data set was used in the analysis. RESULTS: A separate covariance pattern was identified that significantly predicted each cognitive ability after controlling for age except for vocabulary, but the age by WM covariance pattern interaction was not significant for any of the three abilities. Furthermore, each of the patterns mediated the effect of age on the respective cognitive ability. A distinct set of WM tracts was most influential in each of the three patterns. The WM covariance pattern accounting for fluid reasoning showed the most number of influential WM tracts whereas the episodic memory pattern showed the least number. CONCLUSION: Specific patterns of WM tracts make significant contributions to the age-related differences in perceptual speed, episodic memory, and fluid reasoning but not vocabulary. Other measures of brain health will need to be explored to reveal the major influences on the vocabulary ability

Investigating White Matter Lesion Load, Intrinsic Functional Connectivity, and Cognitive Abilities in Older Adults

Changes to the while matter of the brain disrupt neural communication between spatially distributed brain regions and are associated with cognitive changes in later life. While approximately 95% of older adults experience these brain changes, not everyone who has significant white matter damage displays cognitive impairment. Few studies have investigated the association between white matter changes and cognition in the context of functional brain network integrity. This study used a data-driven, multivariate analytical model to investigate intrinsic functional connectivity patterns associated with individual variability in white matter lesion load as related to fluid and crystallized intelligence in a sample of healthy older adults (n = 84). Several primary findings were noted. First, a reliable pattern emerged associating whole-brain resting-state functional connectivity with individual variability in measures of white matter lesion load, as indexed by total white matter lesion volume and number of lesions. Secondly, white matter lesion load was associated with increased network disintegration and dedifferentiation. Specifically, lower white matter lesion load was associated with greater within- versus between-network connectivity. Higher white matter lesion load was associated with greater between-network connectivity compared to within. These associations between intrinsic functional connectivity and white matter lesion load were not reliably associated with crystallized and fluid intelligence performance. These results suggest that changes to the white matter of the brain in typically aging older adults are characterized by increased functional brain network dedifferentiation. The findings highlight the role of white matter lesion load in altering the functional network architecture of the brain

Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking.

Ageing is characterized by declines on a variety of cognitive measures. These declines are often attributed to a general, unitary underlying cause, such as a reduction in executive function owing to atrophy of the prefrontal cortex. However, age-related changes are likely multifactorial, and the relationship between neural changes and cognitive measures is not well-understood. Here we address this in a large (N=567), population-based sample drawn from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data. We relate fluid intelligence and multitasking to multiple brain measures, including grey matter in various prefrontal regions and white matter integrity connecting those regions. We show that multitasking and fluid intelligence are separable cognitive abilities, with differential sensitivities to age, which are mediated by distinct neural subsystems that show different prediction in older versus younger individuals. These results suggest that prefrontal ageing is a manifold process demanding multifaceted models of neurocognitive ageing

Understanding preclinical dementia : early detection of dementia through cognitive and biological markers

Dementia is becoming a growing healthcare crisis, therefore identifying individuals at risk or in the earliest stages of dementia is essential if prevention or disease modification is to be achieved. The objective of this thesis was to examine cognitive performance and decline during the preclinical phase and explore the ability of cognitive and biological markers to identify those at risk of future dementia. Data from a population-based longitudinal study, SNAC-K, were used to investigate this aim. Study I examined the ability of neuropsychological tests, genetics, and structural MRI volumes to predict dementia six years later. Models were systematically created to identify the best combinations for prediction. A model containing all three modalities: hippocampal volume, a task of category fluency, presence of an APOE ɛ4 allele, white-matter hyperintensities volume, and a task of general knowledge, displayed the most predictive value (AUC=.924; C.I=.883–.965). However, this model did not significantly improve predictive value over one containing only cognitive and genetic markers, suggesting that minor increases in predictivity should be weighed against the costs of additional tests. Study II investigated the benefit of DTI, alongside neuropsychological tests, genetics, and brain volume markers in predicting future dementia. MD values for tracts CHC, CS, FMAJ, and IFOF (AUC=.837– .862) and the FA IFOF latent factor (AUC=.839) were significantly associated with dementia at six years. A final model consisting of a measure of perceptual speed, hippocampal volume, and MD of the FMAJ tract was created with the highest predictive value (AUC=.911). Assessment of microstructural white matter integrity via DTI was associated with future dementia but the additional benefit when combined with other markers was relatively small. Study III narrowed its focus to the ability of cognitive markers alone and the effect of modifying factors (age, sex, education, the presence of an ɛ4 allele, AD–only dementia, and time to diagnosis) on identifying those at risk of dementia. The most predictive model, consisting of category fluency, word recall, and pattern comparison, achieved good prediction values (AUC=.913) for dementia six years later. Tests in the domains of category fluency, episodic memory, and perceptual speed were, in general, good predictors across all subgroups and up to 6 years before a dementia diagnosis. However, cognitive tests became increasingly unreliable at predicting dementia beyond that time. Study IV explored the trajectories of cognitive decline over a 12-year period during the preclinical stage of dementia, before examining the ability of early cognitive decline in identifying those with increased likelihood of future dementia. Persons in the preclinical phase showed increased rate of decline in all cognitive domains compared to those who did not develop dementia (β:-.07 to -.11), this difference was particularly noticeable closer to diagnosis. Those classified as fast decliners for 3 or more cognitive tests demonstrated the highest risk of dementia (HR: 3.38, CI: 1.91-6.01). Although, changes in early rates of decline were small and rates of decline may be more predictive closer to diagnosis. Collectively, these studies confirm a long preclinical period in dementia development, which allows for the use of a wide range of markers (cognitive, genetic, MRI, and DTI) capable of identifying those at high risk of dementia. The ability of these markers to predict future dementia is increased through combining within and between modalities

An Approach to Studying the Neural Correlates of Reserve

The goal of this paper is to review my current understanding of the concepts of cognitive reserve (CR), brain reserve and brain maintenance, and to describe our group's approach to using imaging to study their neural basis. I present a working model for utilizing data regarding brain integrity, clinical status, cognitive activation and CR proxies to develop analyses that can explore the neural basis of cognitive reserve and brain maintenance. The basic model assumes that the effect of brain changes on cognition is mediated by task-related activation. We treat CR as a moderator to understand how task-related activation might vary as a function of CR, or how CR might operate independently of these differences in task-related activation. My hope is that this presentation will spark discussion across groups that study these concepts, allowing us to come to some common agreement on definitions, methodology and approaches

Longitudinal Changes of Structural and Functional Connectivity and Correlations with Neurocognitive Metrics

Revealing brain functional and micro-structural changes over a relatively short period at individual levels are especially important given that many risks associated with age including vascular and neuroinflammation increases and could confound the baseline fMRI parametric images. Cellular-level axonal injury and/or demyelination as well as dispersed mesoscopic level substance abnormal aggregation and structural/functional abnormality could occur in short subacute/acute phases, while literatures related to longitudinal changes with age are limited with only our previous fMRI findings. Longitudinal data were used to characterize these multi-parameters including random intercept and interval per individual. No significant age by gender interactions have been found to either DTI fractional anisotropy (FA) or diffusivity metrics. The interval effective regions showed longitudinal change of FA and radial diffusivity (RD)/axial diffusivity (AX) values remained similar to the aging results found with cross-sectional data. Significant correlations between DTI and fMRI metrics as well as between imaging and neurocognitive data including speed and memory were found. Our results indicate significant and consistent age, gender and apolipoprotein E (APOE) genotypic effects on structural and functional connectivity at both short-interval and cross-sectional ranges, together with correlational neurocognitive functions

Multimodal MRI Neuroimaging Biomarkers for Cognitive Normal Adults, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease

Multimodal magnetic resonance imaging (MRI) techniques have been developed to noninvasively measure structural, metabolic, hemodynamic and functional changes of the brain. These advantages have made MRI an important tool to investigate neurodegenerative disorders, including diagnosis, disease progression monitoring, and treatment efficacy evaluation. This paper discusses recent findings of the multimodal MRI in the context of surrogate biomarkers for identifying the risk for AD in normal cognitive (NC) adults, brain anatomical and functional alterations in amnestic mild cognitive impairment (aMCI), and Alzheimer's disease (AD) patients. Further developments of these techniques and the establishment of promising neuroimaging biomarkers will enhance our ability to diagnose aMCI and AD in their early stages and improve the assessment of therapeutic efficacy in these diseases in future clinical trials

Age Differences of Multivariate Network Expressions During Task-Switching and Their Associations with Behavior

The effect of aging on functional network activation associated with task-switching was examined in 24 young (age=25.2+/-2.73 years) and 23 older adults (age=65.2+/-2.65 years) using functional magnetic resonance imaging (fMRI). The study goals were to (1) identify a network shared by both young and older adults, (2) identify additional networks in each age group, and (3) examine the relationship between the networks identified and behavioral performance in task-switching. Ordinal trend covariance analysis was used to identify the networks, which takes advantage of increasing activation with greater task demand to isolate the network of regions recruited by task-switching. Two task-related networks were found: a shared network that was strongly expressed by both young and older adults and a second network identified in the young data that was residualized from the shared network. Both networks consisted of regions associated with task-switching in previous studies including the middle frontal gyrus, the precentral gyrus, the anterior cingulate, and the superior parietal lobule. Not only was pattern expression of the shared network associated with reaction time in both age groups, the difference in the pattern expression across task conditions (task-switch minus single-task) was also correlated with the difference in RT across task conditions. On the contrary, expression of the young-residual network showed a large age effect such that older adults do not increase expression of the network with greater task demand as young adults do and correlation between expression and accuracy was significant only for young adults. Thus, while a network related to RT is preserved in older adults, a different network related to accuracy is disrupted

Maternal depression during early childhood, persistent aggression into emerging adulthood: neurodevelopmental pathways of risk?

Despite an accumulation of evidence documenting prospective links between maternal depression and aggression in offspring, the mechanisms underlying this association remain somewhat mysterious. Mothers' depressive symptoms could undermine offspring's learning of stage-adaptive emotion regulation (ER) skills during early childhood (e.g., Seifer, Schiller, Sameroff, Resnick, & Riordan, 1996; Silk, Shaw, Skuban, Oland, & Kovacs, 2006). Some longitudinal studies link maternal depression to disruptions in young children's ER, which has been found to predict elevated aggressive behavior in later childhood and emerging adolescence (e.g., Gilliom et al., 2002; Trentacosta & Shaw, 2009). Neurodevelopmental mechanisms such as altered organization or refinement in cortico-limbic pathways could also play a role in prospective associations between mothers' depression during early childhood and dysregulated aggression in offspring (Callaghan & Tottenham, 2016; Sheikh et al., 2014). To further inform future inquiries into these mechanisms of risk, the present study tested whether maternal depression in early childhood was prospectively linked to persistent patterns of aggression at school entry and in emerging adulthood via disruptions in early ER processes and related patterns of neuroanatomical connectivity. Participants were drawn from a sample of 310 males at elevated risk for disruptive behavior problems based on their gender and low socioeconomic status. Direct paths from maternal depression and preschool-age ER in early childhood to offspring aggression at school-age were supported. Unexpectedly, aggressive behavior was not found to be stable from the early school-age period into young adulthood across informant and context. Children's aggressive behavior was inversely associated with uncinate fasciculus structural integrity in emerging adulthood, such that higher aggression at school-age predicted lower fractional anisotropy at age 20. Another index of uncinate structural integrity (i.e., mean diffusivity) was positively associated with general antisocial behavior and depressive symptoms in young adulthood. The present findings add new, longitudinal evidence to inform nascent theories for neurodevelopmental mechanisms underlying antisocial behavior and clarify directions for future research endeavors to illuminate other potential neurodevelopmental mechanisms of risk related to mothers' depression