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Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach

OBJECTIVE: It is well known that altered functional connectivity is a robust neuroimaging marker of schizophrenia. However, there is inconsistency in the direction of alterations, i.e., increased or decreased connectivity. In this study, we aimed to determine the direction of the connectivity altera...

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Autores principales: Kim, Minhoe, Seo, Ji Won, Yun, Seokho, Kim, Minchul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512460/
https://www.ncbi.nlm.nih.gov/pubmed/37743998
http://dx.doi.org/10.3389/fpsyt.2023.1232015
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author Kim, Minhoe
Seo, Ji Won
Yun, Seokho
Kim, Minchul
author_facet Kim, Minhoe
Seo, Ji Won
Yun, Seokho
Kim, Minchul
author_sort Kim, Minhoe
collection PubMed
description OBJECTIVE: It is well known that altered functional connectivity is a robust neuroimaging marker of schizophrenia. However, there is inconsistency in the direction of alterations, i.e., increased or decreased connectivity. In this study, we aimed to determine the direction of the connectivity alteration associated with schizophrenia using a multivariate, data-driven approach. METHODS: Resting-state functional magnetic resonance imaging data were acquired from 109 individuals with schizophrenia and 120 controls across two openly available datasets. A whole-brain resting-state functional connectivity (rsFC) matrix was computed for each individual. A modified connectome-based predictive model (CPM) with a support vector machine (SVM) was used to classify patients and controls. We conducted a series of multivariate classification analyses using three different feature sets, increased, decreased, and both increased and decreased rsFC. RESULTS: For both datasets, combining information from both increased and decreased rsFC substantially improved prediction accuracy (Dataset 1: accuracy = 70.2%, permutation p = 0.001; Dataset 2: accuracy = 64.4%, permutation p = 0.003). When tested across datasets, the prediction model using decreased rsFC performed best. The identified predictive features of decreased rsFC were distributed mostly in the motor network for both datasets. CONCLUSION: These findings suggest that bidirectional alterations in rsFC are distributed in schizophrenia patients, with the pattern of decreased rsFC being more similar across different populations.
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spelling pubmed-105124602023-09-22 Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach Kim, Minhoe Seo, Ji Won Yun, Seokho Kim, Minchul Front Psychiatry Psychiatry OBJECTIVE: It is well known that altered functional connectivity is a robust neuroimaging marker of schizophrenia. However, there is inconsistency in the direction of alterations, i.e., increased or decreased connectivity. In this study, we aimed to determine the direction of the connectivity alteration associated with schizophrenia using a multivariate, data-driven approach. METHODS: Resting-state functional magnetic resonance imaging data were acquired from 109 individuals with schizophrenia and 120 controls across two openly available datasets. A whole-brain resting-state functional connectivity (rsFC) matrix was computed for each individual. A modified connectome-based predictive model (CPM) with a support vector machine (SVM) was used to classify patients and controls. We conducted a series of multivariate classification analyses using three different feature sets, increased, decreased, and both increased and decreased rsFC. RESULTS: For both datasets, combining information from both increased and decreased rsFC substantially improved prediction accuracy (Dataset 1: accuracy = 70.2%, permutation p = 0.001; Dataset 2: accuracy = 64.4%, permutation p = 0.003). When tested across datasets, the prediction model using decreased rsFC performed best. The identified predictive features of decreased rsFC were distributed mostly in the motor network for both datasets. CONCLUSION: These findings suggest that bidirectional alterations in rsFC are distributed in schizophrenia patients, with the pattern of decreased rsFC being more similar across different populations. Frontiers Media S.A. 2023-09-07 /pmc/articles/PMC10512460/ /pubmed/37743998 http://dx.doi.org/10.3389/fpsyt.2023.1232015 Text en Copyright © 2023 Kim, Seo, Yun and Kim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Kim, Minhoe
Seo, Ji Won
Yun, Seokho
Kim, Minchul
Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach
title Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach
title_full Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach
title_fullStr Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach
title_full_unstemmed Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach
title_short Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach
title_sort bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512460/
https://www.ncbi.nlm.nih.gov/pubmed/37743998
http://dx.doi.org/10.3389/fpsyt.2023.1232015
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