Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network.
default mode network
hidden Markov model
neural dynamics
resting state fMRI
resting state networks
schizophrenia
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
05 2019
05 2019
Historique:
received:
31
10
2018
revised:
06
12
2018
accepted:
26
12
2018
pubmed:
22
1
2019
medline:
14
4
2020
entrez:
22
1
2019
Statut:
ppublish
Résumé
Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (n = 41) and healthy comparison individuals (n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting-state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8-min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4-5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76-85%.
Identifiants
pubmed: 30664285
doi: 10.1002/hbm.24519
pmc: PMC6917018
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2212-2228Subventions
Organisme : NIMH NIH HHS
ID : U54 MH091657
Pays : United States
Organisme : National Health and Medical Research Council
ID : APP1136649
Pays : International
Organisme : NHMRC Senior Principal Research Fellowship
ID : 628386 and 1105825
Pays : International
Informations de copyright
© 2019 Wiley Periodicals, Inc.
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