Dataset Information

Structural covariance networks of striatum subdivision in patients with Parkinson's disease.

ABSTRACT: Parkinson's disease (PD) is a neurodegenerative disorder associated with the striatum. Previous studies indicated that subdivisions of the striatum with distinct functional connectivity profiles contribute to different pathogeneses in PD. Segregated structural covariance (SC) pattern between the striatum and neocortex observed in healthy subjects, however, remain unknown in PD. The purpose of this study is to map and compare the subregional striatal SC network organization between 30 healthy controls and 48 PD patients and to investigate their association with the disease severity. The striatal SC network was statistically inferred by correlating the mean gray matter (GM) volume of six striatal subdivisions (including the bilateral dorsal caudate, superior ventral striatum, inferior ventral striatum, dorsal caudal putamen, dorsal rostral putamen, and ventral rostral putamen) with the entire neocortical GM volume in voxel-wise manner. The PD patients revealed marked atrophy in the striatum, cerebellum, and extra-striatum neocortices. As predicted, segregated striatal SC network patterns were observed in both groups. This suggests that in PD, pathological processes occurring in the striatum affect the same striato-cortical networks that covary with the striatum in healthy brains. The PD patients further demonstrated atypical striatal SC patterns between the caudate, parahippocampus temporal cortices, and cerebellum, which corresponded to dopaminergic associated network. The areas with significant group differences in SC were further associated with disease severity. Our findings support previous studies indicating that PD is associated with altered striato-cortical networks, and suggest that structural changes in the striatum may result in a cascade of alterations in multiple neocortices.

SUBMITTER: Chou KH

PROVIDER: S-EPMC6869748 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Structural covariance networks of striatum subdivision in patients with Parkinson's disease.

Chou Kun-Hsien KH Lin Wei-Che WC Lee Pei-Lin PL Tsai Nai-Wen NW Huang Yung-Cheng YC Chen Hsiu-Ling HL Cheng Kuei-Yueh KY Chen Pei-Chin PC Wang Hung-Chen HC Lin Tsu-Kung TK Li Shau-Hsuan SH Lin Wei-Ming WM Lu Cheng-Hsien CH Lin Ching-Po CP

Human brain mapping 20141231 4

Parkinson's disease (PD) is a neurodegenerative disorder associated with the striatum. Previous studies indicated that subdivisions of the striatum with distinct functional connectivity profiles contribute to different pathogeneses in PD. Segregated structural covariance (SC) pattern between the striatum and neocortex observed in healthy subjects, however, remain unknown in PD. The purpose of this study is to map and compare the subregional striatal SC network organization between 30 healthy con ...[more]

PMID: 25594281

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Project description:In neurodegenerative disorders, a clearer understanding of the underlying aberrant networks facilitates the search for effective therapeutic targets and potential cures. [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging data of brain metabolism reflects the distribution of glucose consumption known to be directly related to neural activity. In FDG PET resting-state metabolic data, characteristic disease-related patterns have been identified in group analysis of various neurodegenerative conditions using principal component analysis of multivariate spatial covariance. Notably, among several parkinsonian syndromes, the identified Parkinson's disease-related pattern (PDRP) has been repeatedly validated as an imaging biomarker of PD in independent groups worldwide. Although the primary nodal associations of this network are known, its connectivity is not fully understood. Here, we describe a novel approach to elucidate functional principal component (PC) network connections by performing graph theoretical sparse network derivation directly within the disease relevant PC partition layer of the whole brain data rather than by searching for associations retrospectively in whole brain sparse representations. Using sparse inverse covariance estimation of each overlapping PC partition layer separately, a single coherent network is detected for each layer in contrast to more spatially modular segmentation in whole brain data analysis. Using this approach, the major nodal hubs of the PD disease network are identified and their characteristic functional pathways are clearly distinguished within the basal ganglia, midbrain and parietal areas. Network associations are further clarified using Laplacian spectral analysis of the adjacency matrices. In addition, the innate discriminative capacity of the eigenvector centrality of the graph derived networks in differentiating PD versus healthy external data provides evidence of their validity.

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Dataset Information

Structural covariance networks of striatum subdivision in patients with Parkinson's disease.

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Structural covariance networks of striatum subdivision in patients with Parkinson's disease.

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