Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography.
cortical surface
fMRI
schizophrenia
subcortical-cortical
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 10 2023
15 10 2023
Historique:
revised:
05
07
2023
received:
10
01
2023
accepted:
01
08
2023
medline:
18
9
2023
pubmed:
22
8
2023
entrez:
22
8
2023
Statut:
ppublish
Résumé
Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles. We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT). The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10 Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.
Sections du résumé
BACKGROUND
Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles.
METHODS
We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT).
RESULTS
The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10
CONCLUSION
Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.
Identifiants
pubmed: 37605827
doi: 10.1002/hbm.26453
pmc: PMC10502662
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5153-5166Subventions
Organisme : NIMH NIH HHS
ID : 1/3 R01 MH102324
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH102313
Pays : United States
Organisme : NCRR NIH HHS
ID : UL1 RR024911
Pays : United States
Organisme : NIH HHS
ID : DP5 OD012109
Pays : United States
Organisme : NIMH NIH HHS
ID : P50MH080173
Pays : United States
Organisme : NINDS NIH HHS
ID : PL1 NS062410
Pays : United States
Organisme : NIH HHS
ID : 2P50AA012870-11
Pays : United States
Organisme : NIH HHS
ID : 1U01MH121766
Pays : United States
Organisme : NIH HHS
ID : DP5OD012109
Pays : United States
Organisme : NCRR NIH HHS
ID : P20 RR021938
Pays : United States
Organisme : NIDA NIH HHS
ID : RL1 DA024853
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH102324
Pays : United States
Organisme : NIH HHS
ID : 5R01MH112189
Pays : United States
Organisme : NIH HHS
ID : 5R01MH108590
Pays : United States
Organisme : NIMH NIH HHS
ID : R01MH102313
Pays : United States
Organisme : NIMH NIH HHS
ID : PL1 MH083271
Pays : United States
Organisme : NIMH NIH HHS
ID : RL1 MH083270
Pays : United States
Organisme : NIMH NIH HHS
ID : RL1 MH083269
Pays : United States
Organisme : NIDCR NIH HHS
ID : UL1 DE019580
Pays : United States
Organisme : NIMH NIH HHS
ID : P50 MH080173
Pays : United States
Organisme : NLM NIH HHS
ID : RL1 LM009833
Pays : United States
Organisme : NIMH NIH HHS
ID : RL1 MH083268
Pays : United States
Organisme : NIAAA NIH HHS
ID : P50 AA012870
Pays : United States
Informations de copyright
© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
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