期刊
NEUROIMAGE-CLINICAL
卷 12, 期 -, 页码 806-814出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.nicl.2016.10.009
关键词
Huntington's disease; Structural MRI; Grey matter; Structural covariance networks; Voxel-based morphometry
类别
资金
- CHDI Foundation, Inc.
Background: Progressive subcortical changes are known to occur in Huntington's disease (HD), a hereditary neurodegenerative disorder. Less is known about the occurrence and cohesion of whole brain grey matter changes in HD. Objectives: We aimed to detect network integrity changes in grey matter structural covariance networks and examined relationships with clinical assessments. Methods: Structural magnetic resonance imaging data of premanifest HD (n = 30), HD patients (n = 30) and controls (n = 30) was used to identify ten structural covariance networks based on a novel technique using the co-variation of grey matter with independent component analysis in FSL. Group differences were studied controlling for age and gender. To explore whether our approach is effective in examining grey matter changes, regional voxel-based analysis was additionally performed. Results: Premanifest HD and HD patients showed decreased network integrity in two networks compared to controls. One network included the caudate nucleus, precuneous and anterior cingulate cortex (in HD p < 0.001, in pre-HDp = 0.003). One other network contained the hippocampus, premotor, sensorimotor, and insular cortices (in HD p < 0.001, in pre-HD p = 0.023). Additionally, in HD patients only, decreased network integrity was observed in a network including the lingual gyrus, intracalcarine, cuneal, and lateral occipital cortices (p = 0.032). Changes in network integrity were significantly associated with scores of motor and neuropsychological assessments. In premanifest HD, voxel-based analyses showed pronounced volume loss in the basal ganglia, but less prominent in cortical regions. Conclusion: Our results suggest that structural covariance might be a sensitive approach to reveal early grey matter changes, especially for premanifest HD. (C) 2016 The Authors. Published by Elsevier Inc.
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