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Age-related cognitive gains are mediated by the effects of white matter development on brain network integration.

ABSTRACT: A fundamental, yet rarely tested premise of developmental cognitive neuroscience is that changes in brain activity and improvements in behavioral control across adolescent development are related to brain maturational factors that shape a more efficient, highly-interconnected brain in adulthood. We present the first multimodal neuroimaging study to empirically demonstrate that maturation of executive cognitive ability is directly associated with the relationship of white matter development and age-related changes in neural network functional integration. In this study, we identified specific white matter regions whose maturation across adolescence appears to reduce reliance on local processing in brain regions recruited for conscious, deliberate cognitive control in favor of a more widely distributed profile of functionally-integrated brain activity. Greater white matter coherence with age was associated with both increases and decreases in functional connectivity within task-engaged functional circuits. Importantly, these associations between white matter development and brain system functional integration were related to behavioral performance on tests of response inhibition, demonstrating their importance in the maturation of optimal cognitive control.

SUBMITTER: Stevens MC

PROVIDER: S-EPMC2753497 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Age-related cognitive gains are mediated by the effects of white matter development on brain network integration.

Stevens Michael C MC Skudlarski Pawel P Pearlson Godfrey D GD Calhoun Vince D VD

NeuroImage 20090703 4

A fundamental, yet rarely tested premise of developmental cognitive neuroscience is that changes in brain activity and improvements in behavioral control across adolescent development are related to brain maturational factors that shape a more efficient, highly-interconnected brain in adulthood. We present the first multimodal neuroimaging study to empirically demonstrate that maturation of executive cognitive ability is directly associated with the relationship of white matter development and a ...[more]

PMID: 19577651

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Project description:BackgroundResearch suggests that cerebral small vessel disease (CSVD), amyloid, and pTau contribute to age-related cognitive decline. It remains unknown how these factors relate to one another and how they jointly contribute to cognitive decline in normal aging. This project examines the association between these factors and their relationship to cognitive decline in cognitively unimpaired older adults without subjective cognitive decline.MethodsA total of 230 subjects with cerebrospinal fluid (CSF) Aß42, CSF pTau181, white matter lesions (WMLs) used as a proxy of CSVD, and cognitive scores from the Alzheimer's Disease Neuroimaging Initiative were included. Associations between each factor and cognitive score were investigated using regression models. Furthermore, relationships between the three pathologies were also examined using regression models.ResultsAt baseline, there was an inverse association between WML load and Aß42 (t = -4.20, p <.001). There was no association between WML load and pTau (t = 0.32, p = 0.75), nor with Aß42 and pTau (t = 0.51, p =.61). Correcting for age, sex and education, baseline WML load was associated with baseline ADAS-13 scores (t = 2.59, p =.01) and lower follow-up executive functioning (t = -2.84, p =.005). Baseline Aß42 was associated with executive function at baseline (t = 3.58, p<.004) but not at follow-up (t = 1.05, p = 0.30), nor with ADAS-13 at baseline (t = -0.24, p = 0.81) or follow-up (t = 0.09, p = 0.93). Finally, baseline pTau was not associated with any cognitive measure at baseline or follow-up.ConclusionBoth baseline Aß42 and WML load are associated with some baseline cognition scores, but only baseline WML load is associated with follow-up executive functioning. This finding suggests that WMLs may be one of the earliest clinical manifestations that contributes to future cognitive decline in cognitively healthy older adults. Given that healthy older adults with WMLs exhibit declines in cognitive functioning, they may be less resilient to future pathology increasing their risk for cognitive impairment due to dementia than those without WMLs.

Project description:BackgroundDetection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer's disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance.MethodsOur study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier.Results(1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI.ConclusionFeature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.

Project description:White matter disruption is an important determinant of cognitive impairment after brain injury, but conventional neuroimaging underestimates its extent. In contrast, diffusion tensor imaging provides a validated and sensitive way of identifying the impact of axonal injury. The relationship between cognitive impairment after traumatic brain injury and white matter damage is likely to be complex. We applied a flexible technique-tract-based spatial statistics-to explore whether damage to specific white matter tracts is associated with particular patterns of cognitive impairment. The commonly affected domains of memory, executive function and information processing speed were investigated in 28 patients in the post-acute/chronic phase following traumatic brain injury and in 26 age-matched controls. Analysis of fractional anisotropy and diffusivity maps revealed widespread differences in white matter integrity between the groups. Patients showed large areas of reduced fractional anisotropy, as well as increased mean and axial diffusivities, compared with controls, despite the small amounts of cortical and white matter damage visible on standard imaging. A stratified analysis based on the presence or absence of microbleeds (a marker of diffuse axonal injury) revealed diffusion tensor imaging to be more sensitive than gradient-echo imaging to white matter damage. The location of white matter abnormality predicted cognitive function to some extent. The structure of the fornices was correlated with associative learning and memory across both patient and control groups, whilst the structure of frontal lobe connections showed relationships with executive function that differed in the two groups. These results highlight the complexity of the relationships between white matter structure and cognition. Although widespread and, sometimes, chronic abnormalities of white matter are identifiable following traumatic brain injury, the impact of these changes on cognitive function is likely to depend on damage to key pathways that link nodes in the distributed brain networks supporting high-level cognitive functions.

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Age-related cognitive gains are mediated by the effects of white matter development on brain network integration.

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Age-related cognitive gains are mediated by the effects of white matter development on brain network integration.

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