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Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach
OBJECTIVE: To explore the interhemispheric information synergy ability of the brain in major depressive disorder (MDD) patients by applying the voxel-mirrored homotopic connectivity (VMHC) method and further explore the potential clinical diagnostic value of VMHC metric by a machine learning approac...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10520785/ https://www.ncbi.nlm.nih.gov/pubmed/37766927 http://dx.doi.org/10.3389/fpsyt.2023.1241670 |
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author | Chen, Qing Bi, Yanmeng Yan, Weixin Wu, Shuhui Xia, Ting Wang, Yuhua Huang, Sha Zhou, Chuying Xie, Shuwen Kuang, Shanshan Kong, Wen Lv, Zhiping |
author_facet | Chen, Qing Bi, Yanmeng Yan, Weixin Wu, Shuhui Xia, Ting Wang, Yuhua Huang, Sha Zhou, Chuying Xie, Shuwen Kuang, Shanshan Kong, Wen Lv, Zhiping |
author_sort | Chen, Qing |
collection | PubMed |
description | OBJECTIVE: To explore the interhemispheric information synergy ability of the brain in major depressive disorder (MDD) patients by applying the voxel-mirrored homotopic connectivity (VMHC) method and further explore the potential clinical diagnostic value of VMHC metric by a machine learning approach. METHODS: 52 healthy controls and 48 first-episode MDD patients were recruited in the study. We performed neuropsychological tests and resting-state fMRI scanning on all subjects. The VMHC values of the symmetrical interhemispheric voxels in the whole brain were calculated. The VMHC alterations were compared between two groups, and the relationship between VMHC values and clinical variables was analyzed. Then, abnormal brain regions were selected as features to conduct the classification model by using the support vector machine (SVM) approach. RESULTS: Compared to the healthy controls, MDD patients exhibited decreased VMHC values in the bilateral middle frontal gyrus, fusiform gyrus, medial superior frontal gyrus and precentral gyrus. Furthermore, the VMHC value of the bilateral fusiform gyrus was positively correlated with the total Hamilton Depression Scale (HAMD). Moreover, SVM analysis displayed that a combination of all clusters demonstrated the highest area under the curve (AUC) of 0.87 with accuracy, sensitivity, and specificity values of 86.17%, 76.74%, and 94.12%, respectively. CONCLUSION: MDD patients had reduced functional connectivity in the bilateral middle frontal gyrus, fusiform gyrus, medial superior frontal gyrus and precentral gyrus, which may be related to depressive symptoms. The abnormality in these brain regions could represent potential imaging markers to distinguish MDD patients from healthy controls. |
format | Online Article Text |
id | pubmed-10520785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105207852023-09-27 Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach Chen, Qing Bi, Yanmeng Yan, Weixin Wu, Shuhui Xia, Ting Wang, Yuhua Huang, Sha Zhou, Chuying Xie, Shuwen Kuang, Shanshan Kong, Wen Lv, Zhiping Front Psychiatry Psychiatry OBJECTIVE: To explore the interhemispheric information synergy ability of the brain in major depressive disorder (MDD) patients by applying the voxel-mirrored homotopic connectivity (VMHC) method and further explore the potential clinical diagnostic value of VMHC metric by a machine learning approach. METHODS: 52 healthy controls and 48 first-episode MDD patients were recruited in the study. We performed neuropsychological tests and resting-state fMRI scanning on all subjects. The VMHC values of the symmetrical interhemispheric voxels in the whole brain were calculated. The VMHC alterations were compared between two groups, and the relationship between VMHC values and clinical variables was analyzed. Then, abnormal brain regions were selected as features to conduct the classification model by using the support vector machine (SVM) approach. RESULTS: Compared to the healthy controls, MDD patients exhibited decreased VMHC values in the bilateral middle frontal gyrus, fusiform gyrus, medial superior frontal gyrus and precentral gyrus. Furthermore, the VMHC value of the bilateral fusiform gyrus was positively correlated with the total Hamilton Depression Scale (HAMD). Moreover, SVM analysis displayed that a combination of all clusters demonstrated the highest area under the curve (AUC) of 0.87 with accuracy, sensitivity, and specificity values of 86.17%, 76.74%, and 94.12%, respectively. CONCLUSION: MDD patients had reduced functional connectivity in the bilateral middle frontal gyrus, fusiform gyrus, medial superior frontal gyrus and precentral gyrus, which may be related to depressive symptoms. The abnormality in these brain regions could represent potential imaging markers to distinguish MDD patients from healthy controls. Frontiers Media S.A. 2023-09-12 /pmc/articles/PMC10520785/ /pubmed/37766927 http://dx.doi.org/10.3389/fpsyt.2023.1241670 Text en Copyright © 2023 Chen, Bi, Yan, Wu, Xia, Wang, Huang, Zhou, Xie, Kuang, Kong and Lv. 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 Chen, Qing Bi, Yanmeng Yan, Weixin Wu, Shuhui Xia, Ting Wang, Yuhua Huang, Sha Zhou, Chuying Xie, Shuwen Kuang, Shanshan Kong, Wen Lv, Zhiping Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach |
title | Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach |
title_full | Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach |
title_fullStr | Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach |
title_full_unstemmed | Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach |
title_short | Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach |
title_sort | abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fmri: a machine learning approach |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10520785/ https://www.ncbi.nlm.nih.gov/pubmed/37766927 http://dx.doi.org/10.3389/fpsyt.2023.1241670 |
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