Results 21 to 30 of about 80,806 (260)
Age-related changes in processing speed: unique contributions of cerebellar and prefrontal cortex
Age-related declines in processing speed are hypothesized to underlie the widespread changes in cognition experienced by older adults. We used a structural covariance approach to identify putative neural networks that underlie age-related structural ...
Mark A Eckert +4 more
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Structural covariance networks across the life span, from 6 to 94 years of age [PDF]
Structural covariance examines covariation of gray matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete life span ...
Elizabeth DuPre, R. Nathan Spreng
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Alzheimer’s disease (AD) has a long preclinical stage that can last for decades prior to progressing toward amnestic mild cognitive impairment (aMCI) and/or dementia.
Zhenrong Fu +14 more
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Covariant spin structure [PDF]
Dirac fermion fields associated with different tetrad gravitational fields and under general covariant transformations are described by sections of the composite bundle S→Σ→X4, which is both the Dirac spinor bundle over the tetrad bundle Σ and the natural one over X4. As a natural bundle, S→X4 admits general covariant transformations which are those of
openaire +2 more sources
Musical training can induce the functional and structural changes of the hippocampus. The hippocampus is not a homogeneous structure which can be divided into anterior and posterior parts along its longitudinal axis, and the whole-brain structural ...
Panfei Guo +19 more
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Connectivity Alterations in Vascular Parkinsonism: A Structural Covariance Study
This study aimed to investigate the structural covariance between the striatum and large-scale brain regions in patients with vascular parkinsonism (VP) compared to Parkinson’s disease (PD) and control subjects, and then explore the relationship between ...
Fabiana Novellino +8 more
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Covariance structure estimation with Laplace approximation
Gaussian covariance graph model is a popular model in revealing underlying dependency structures among random variables. A Bayesian approach to the estimation of covariance structures uses priors that force zeros on some off-diagonal entries of covariance matrices and put a positive definite constraint on matrices.
Bongjung Sung, Jaeyong Lee
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Structural Covariance Network Disruption and Functional Compensation in Parkinson’s Disease
Purpose: To investigate the structural covariance network disruption in Parkinson’s disease (PD), and explore the functional alterations of disrupted structural covariance network.Methods: A cohort of 100 PD patients and 70 healthy participants underwent
Cheng Zhou +13 more
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Brain network changes in adult victims of violence
IntroductionStressful experiences such as violence can affect mental health severely. The effects are associated with changes in structural and functional brain networks.
Aliaksandra Shymanskaya +6 more
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Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls
Dancers and musicians differ in brain structure from untrained individuals. Structural covariance (SC) analysis can provide further insight into training-associated brain plasticity by evaluating interregional relationships in gray matter (GM) structure.
Falisha J. Karpati +10 more
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