Search

Cognitive reserve (CR) explains differential susceptibility of cognitive performance to neuropathology. However, as brain pathologies progress, cognitive decline occurs even in individuals with initially high CR. The interplay between the structural brain health (= level of brain reserve) and CR-related brain networks therefore requires further research. Our sample included 142 individuals aged 60-70 years. National Adult Reading Test intelligence quotient (NART-IQ) was our CR proxy. On an in-scanner Letter Sternberg task, we used ordinal trend (OrT) analysis to extract a task-related brain activation pattern (OrT slope) for each participant that captures increased expression with task load (one, three, and six letters). We assessed whether OrT slope represents a neural mechanism underlying CR by associating it with task performance and NART-IQ. Additionally, we investigated how the following brain reserve measures affect the association between NART-IQ and OrT slope: mean cortical thickness, total gray matter volume, and brain volumes proximal to the areas contained in the OrT patterns. We found that higher OrT slope was associated with better task performance and higher NART-IQ. Further, the brain reserve measures were not directly associated with OrT slope, but they affected the relationship between NART-IQ and OrT slope: NART-IQ was associated with OrT slope only in individuals with high brain reserve. The degree of brain reserve has an impact on how (and perhaps whether) CR can be implemented in brain networks in older individuals., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Brain-segregation attributes in resting-state functional networks have been widely investigated to understand cognition and cognitive aging using various approaches [e.g., average connectivity within/between networks and brain system segregation (BSS)]. While these approaches have assumed that resting-state functional networks operate in a modular structure, a complementary perspective assumes that a core-periphery or rich club structure accounts for brain functions where the hubs are tightly interconnected to each other to allow for integrated processing. In this article, we apply a novel method, persistent homology (PH), to develop an alternative to standard functional connectivity by quantifying the pattern of information during the integrated processing. We also investigate whether PH-based functional connectivity explains cognitive performance and compare the amount of variability in explaining cognitive performance for three sets of independent variables: (1) PH-based functional connectivity, (2) graph theory-based measures, and (3) BSS. Resting-state functional connectivity data were extracted from 279 healthy participants, and cognitive ability scores were generated in four domains (fluid reasoning, episodic memory, vocabulary, and processing speed). The results first highlight the pattern of brain-information flow over whole brain regions (i.e., integrated processing) accounts for more variance of cognitive abilities than other methods. The results also show that fluid reasoning and vocabulary performance significantly decrease as the strength of the additional information flow on functional connectivity with the shortest path increases. While PH has been applied to functional connectivity analysis in recent studies, our results demonstrate potential utility of PH-based functional connectivity in understanding cognitive function., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Previous studies have demonstrated that four latent variables, or reference abilities (RAs), can account for the majority of age-related changes in cognition: these being episodic memory, fluid reasoning, speed of processing, and vocabulary. In the current study, we focused on RA-selective functional connectivity patterns that vary with both age and behavior. We analyzed fMRI data from 287 community-dwelling adults (20-80 years) on a battery of tests relating to the four RAs (three tests per RA = 12 tests). Functional connectivity values were calculated between a pre-defined set of 264 ROIs (nodes). Across all participants, we (a) identified connections (edges) that correlated with an RA-specific indicator variable and, indexing only these edges; (b) performed linear regression analysis per edge, regressing indicator correlations (Model 1) and connectivity values (Model 2) on Age, Behavioral Performance, and the Interaction term; and (c) took the conjunction of significant edges between models. Results revealed a different subset of edges for each RA whose connectivity strength and domain-selectivity varied with age and behavior. Strikingly, the fluid reasoning RA was particularly vulnerable to the effects of age and displayed the most extensive connectivity and selectivity "footprint" for behavior. These findings indicate that different functional networks are recruited across RA, with fluid reasoning displaying a special status among them., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Understanding the associations between brain biomarkers (BMs) and cognition across age is of paramount importance. Five hundred and sixty-two participants (19-80 years old, 16 mean years of education) were studied. Data from structural T1, diffusion tensor imaging, fluid-attenuated inversion recovery, and resting-state functional magnetic resonance imaging scans combined with a neuropsychological evaluation were used. More specifically, the measures of cortical, entorhinal, and parahippocampal thickness, hippocampal and striatal volume, default-mode network and fronto-parietal control network, fractional anisotropy (FA), and white matter hyperintensity (WMH) were assessed. z-Scores for three cognitive domains measuring episodic memory, executive function, and speed of processing were computed. Multiple linear regressions and interaction effects between each of the BMs and age on cognition were examined. Adjustments were made for age, sex, education, intracranial volume, and then, further, for general cognition and motion. BMs were significantly associated with cognition. Across the adult lifespan, slow speed was associated with low striatal volume, low FA, and high WMH burden. Poor executive function was associated with low FA, while poor memory was associated with high WMH burden. After adjustments, results were significant for the associations: speed-FA and WMH, memory-entorhinal thickness. There was also a significant interaction between hippocampal volume and age in memory. In age-stratified analyses, the most significant associations for the young group occurred between FA and executive function, WMH, and memory, while for the old group, between entorhinal thickness and speed, and WMH and speed, executive function. Unique sets of BMs can explain variation in specific cognitive domains across adulthood. Such results provide essential information about the neurobiology of aging., (© 2019 Wiley Periodicals, Inc.)

β-Amyloid (Aβ), a feature of Alzheimer's disease (AD) pathology, may precede reduced glucose metabolism and gray matter (GM) volume and cognitive decline in patients with AD. Accumulation of Aβ, however, has been also reported in cognitively intact older people, although it remains unresolved whether and how Aβ deposition, glucose metabolism, and GM volume relate to one another in cognitively normal elderly. Fifty-two cognitively normal older adults underwent Pittsburgh Compound B-positron emission tomography (PIB-PET), [(18)F]fluorodeoxyglucose-PET, and structural magnetic resonance imaging to measure whole-brain amyloid deposition, glucose metabolism, and GM volume, respectively. Covariance patterns of these measures in association with global amyloid burden measured by PIB index were extracted using principal component analysis-based multivariate methods. Higher global amyloid burden was associated with relative increases of amyloid deposition and glucose metabolism and relative decreases of GM volume in brain regions collectively known as the default mode network including the posterior cingulate/precuneus, lateral parietal cortices, and medial frontal cortex. Relative increases of amyloid deposition and glucose metabolism were also noted in the lateral prefrontal cortices, and relative decreases of GM volume were pronounced in hippocampus. The degree of expression of the topographical patterns of the PIB data was further associated with visual memory performance when controlling for age, sex, and education. The present findings suggest that cognitively normal older adults with greater amyloid deposition are relatively hypermetabolic in frontal and parietal brain regions while undergoing GM volume loss in overlapping brain regions., (Copyright © 2012 Wiley Periodicals, Inc.)

The accuracy of cerebral blood flow (CBF) imaging in humans has been impeded by the partial volume effects (PVE), which are a consequence of the limited spatial resolution. Because of brain atrophy, PVE can be particularly problematic in imaging the elderly and can considerably overestimate the CBF difference with the young. The primary goal of this study was to separate the structural decline from the true CBF reduction in elderly. To this end, a PVE-correction algorithm was applied on the CBF images acquired with spin-echo EPI continuous arterial spin labeling MRI (voxel size = 3.4 x 3.4 x 8 mm(3)). Tissue-specific CBF images that were independent of voxels' tissue fractional volume were obtained in elderly (N = 30) and young (N = 26); mean age difference was 43 years. Globally, PVE-corrected gray matter CBF was 88.2 +/- 16.1 and 107.3 +/- 17.5 mL/100 g min(-1) in elderly and young, respectively. The largest PVE contribution was found in the frontal lobe and accounted for an additional 10% and 12% increase in the age-related CBF difference between men and women, respectively. The GM-to-WM CBF ratios were found to be on average 3.5 in elderly and 3.9 in young. Whole brain voxelwise comparisons showed marked CBF decrease in anterior cingulate (bilateral), caudate (bilateral), cingulate gyrus (bilateral), cuneus (left), inferior frontal gyrus (left), insula (left), middle frontal gyrus (left), precuneus (bilateral), prefrontal cortex (bilateral), and superior frontal gyrus (bilateral) in men and amygdala (bilateral), hypothalamus (left), hippocampus (bilateral), and middle frontal gyrus (right) in women., (2009 Wiley-Liss, Inc.)

To capture patterns of normal age-associated atrophy, we previously used a multivariate statistical approach applied to voxel based morphometry that identified age-associated gray and white matter covariance networks (Brickman et al. [2007]: Neurobiol Aging 28:284-295). The current study sought to examine the stability of these patterns by forward applying the identified networks to an independent sample of neurologically healthy younger and older adults. Forty-two younger and 35 older adults were imaged with standard high-resolution structural magnetic resonance imaging. Individual images were spatially normalized and segmented into gray and white matter. Covariance patterns that were previously identified with scaled subprofile model analyses were prospectively applied to the current sample to identify to what degree the age-associated patterns were manifested. Older individuals were also assessed with a modified version of the Mini Mental State Examination (mMMSE). Gray matter covariance pattern expression discriminated between younger and older participants with high optimal sensitivity (100%) and specificity (90.5%). While the two groups differed in the degree of white matter pattern expression (t (75) = 5.26, P < 0.001), classification based on white matter expression was relatively low (sensitivity = 80% and specificity = 61.9%). Among older adults, chronological age was significantly associated with increased gray matter pattern expression (r (32) = 0.591, P < 0.001) but not with performance on the mMMSE (r (31) = -0.314, P = 0.085). However, gray matter pattern expression was significantly associated with performance on the mMMSE (r (31) = -0.405, P = 0.024). The findings suggest that the previously derived age-associated covariance pattern for gray matter is reliable and may provide information that is more functionally meaningful than chronological age.