Characteristics of disrupted topological organization in white matter functional connectome in schizophrenia.


Journal

Psychological medicine
ISSN: 1469-8978
Titre abrégé: Psychol Med
Pays: England
ID NLM: 1254142

Informations de publication

Date de publication:
05 2022
Historique:
pubmed: 4 9 2020
medline: 3 6 2022
entrez: 4 9 2020
Statut: ppublish

Résumé

Neuroimaging characteristics have demonstrated disrupted functional organization in schizophrenia (SZ), involving large-scale networks within grey matter (GM). However, previous studies have ignored the role of white matter (WM) in supporting brain function. Using resting-state functional MRI and graph theoretical approaches, we investigated global topological disruptions of large-scale WM and GM networks in 93 SZ patients and 122 controls. Six global properties [clustering coefficient (Cp), shortest path length (Lp), local efficiency (Eloc), small-worldness (σ), hierarchy (β) and synchronization (S) and three nodal metrics [nodal degree (Knodal), nodal efficiency (Enodal) and nodal betweenness (Bnodal)] were utilized to quantify the topological organization in both WM and GM networks. At the network level, both WM and GM networks exhibited reductions in Eloc, Cp and S in SZ. The SZ group showed reduced σ and β only for the WM network. Furthermore, the Cp, Eloc and S of the WM network were negatively correlated with negative symptoms in SZ. At the nodal level, the SZ showed nodal disturbances in the corpus callosum, optic radiation, posterior corona radiata and tempo-occipital WM tracts. For GM, the SZ manifested increased nodal centralities in frontoparietal regions and decreased nodal centralities in temporal regions. These findings provide the first evidence for abnormal global topological properties in SZ from the perspective of a substantial whole brain, including GM and WM. Nodal centralities enhance GM areas, along with a reduction in adjacent WM, suggest that WM functional alterations may be compensated for adjacent GM impairments in SZ.

Sections du résumé

BACKGROUND
Neuroimaging characteristics have demonstrated disrupted functional organization in schizophrenia (SZ), involving large-scale networks within grey matter (GM). However, previous studies have ignored the role of white matter (WM) in supporting brain function.
METHODS
Using resting-state functional MRI and graph theoretical approaches, we investigated global topological disruptions of large-scale WM and GM networks in 93 SZ patients and 122 controls. Six global properties [clustering coefficient (Cp), shortest path length (Lp), local efficiency (Eloc), small-worldness (σ), hierarchy (β) and synchronization (S) and three nodal metrics [nodal degree (Knodal), nodal efficiency (Enodal) and nodal betweenness (Bnodal)] were utilized to quantify the topological organization in both WM and GM networks.
RESULTS
At the network level, both WM and GM networks exhibited reductions in Eloc, Cp and S in SZ. The SZ group showed reduced σ and β only for the WM network. Furthermore, the Cp, Eloc and S of the WM network were negatively correlated with negative symptoms in SZ. At the nodal level, the SZ showed nodal disturbances in the corpus callosum, optic radiation, posterior corona radiata and tempo-occipital WM tracts. For GM, the SZ manifested increased nodal centralities in frontoparietal regions and decreased nodal centralities in temporal regions.
CONCLUSIONS
These findings provide the first evidence for abnormal global topological properties in SZ from the perspective of a substantial whole brain, including GM and WM. Nodal centralities enhance GM areas, along with a reduction in adjacent WM, suggest that WM functional alterations may be compensated for adjacent GM impairments in SZ.

Identifiants

pubmed: 32880241
doi: 10.1017/S0033291720003141
pii: S0033291720003141
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1333-1343

Auteurs

Yuchao Jiang (Y)

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China.
High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, P. R. China.

Dezhong Yao (D)

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China.
Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China.
School of Electrical Engineering, Zhengzhou University, Zhengzhou, P. R. China.

Jingyu Zhou (J)

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China.
High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, P. R. China.

Yue Tan (Y)

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China.

Huan Huang (H)

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China.

MeiLin Wang (M)

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China.

Xin Chang (X)

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China.
High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, P. R. China.

Mingjun Duan (M)

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China.
Department of Psychiatry, Chengdu Mental Health Center, Chengdu, P. R. China.

Cheng Luo (C)

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China.
High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, P. R. China.
Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China.

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