Predicting individual variability in task-evoked brain activity in schizophrenia.
Connectome
cognitive function
fMRI
machine learning
resting-state
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:
15 08 2021
15 08 2021
Historique:
revised:
25
04
2021
received:
10
01
2021
accepted:
02
05
2021
pubmed:
23
5
2021
medline:
17
3
2022
entrez:
22
5
2021
Statut:
ppublish
Résumé
What goes wrong in a schizophrenia patient's brain that makes it so different from a healthy brain? In this study, we tested the hypothesis that the abnormal brain activity in schizophrenia is tightly related to alterations in brain connectivity. Using functional magnetic resonance imaging (fMRI), we demonstrated that both resting-state functional connectivity and brain activity during the well-validated N-back task differed significantly between schizophrenia patients and healthy controls. Nevertheless, using a machine-learning approach we were able to use resting-state functional connectivity measures extracted from healthy controls to accurately predict individual variability in the task-evoked brain activation in the schizophrenia patients. The predictions were highly accurate, sensitive, and specific, offering novel insights regarding the strong coupling between brain connectivity and activity in schizophrenia. On a practical perspective, these findings may allow to generate task activity maps for clinical populations without the need to actually perform any tasks, thereby reducing patients inconvenience while saving time and money.
Identifiants
pubmed: 34021674
doi: 10.1002/hbm.25534
pmc: PMC8288090
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3983-3992Informations de copyright
© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
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