Structural covariance networks in schizophrenia: A systematic review Part II.
Connectome
Graph theory
Schizophrenia
Structural covariance network
Journal
Schizophrenia research
ISSN: 1573-2509
Titre abrégé: Schizophr Res
Pays: Netherlands
ID NLM: 8804207
Informations de publication
Date de publication:
01 2022
01 2022
Historique:
received:
15
03
2021
revised:
02
09
2021
accepted:
23
11
2021
pubmed:
14
12
2021
medline:
26
3
2022
entrez:
13
12
2021
Statut:
ppublish
Résumé
Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia. A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria. A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness. Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.
Sections du résumé
BACKGROUND
Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia.
METHODS
A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria.
RESULTS
A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness.
CONCLUSIONS
Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.
Identifiants
pubmed: 34902650
pii: S0920-9964(21)00466-7
doi: 10.1016/j.schres.2021.11.036
pmc: PMC8785680
mid: NIHMS1763453
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
176-191Subventions
Organisme : NIMH NIH HHS
ID : R01 MH112584
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH115026
Pays : United States
Informations de copyright
Copyright © 2021 Elsevier B.V. All rights reserved.
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