Cargando…
Abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: A resting-state functional magnetic resonance imaging study and support vector machine analysis
OBJECTIVE: Previous studies have revealed abnormal degree centrality (DC) in the structural and functional networks in the brains of patients with major depressive disorder (MDD). There are no existing reports on the DC analysis method combined with the support vector machine (SVM) to distinguish pa...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9486164/ https://www.ncbi.nlm.nih.gov/pubmed/36147977 http://dx.doi.org/10.3389/fpsyt.2022.960294 |
_version_ | 1784792219235909632 |
---|---|
author | Lin, Hang Xiang, Xi Huang, Junli Xiong, Shihong Ren, Hongwei Gao, Yujun |
author_facet | Lin, Hang Xiang, Xi Huang, Junli Xiong, Shihong Ren, Hongwei Gao, Yujun |
author_sort | Lin, Hang |
collection | PubMed |
description | OBJECTIVE: Previous studies have revealed abnormal degree centrality (DC) in the structural and functional networks in the brains of patients with major depressive disorder (MDD). There are no existing reports on the DC analysis method combined with the support vector machine (SVM) to distinguish patients with MDD from healthy controls (HCs). Here, the researchers elucidated the variations in DC values in brain regions of MDD patients and provided imaging bases for clinical diagnosis. METHODS: Patients with MDD (N = 198) and HCs (n = 234) were scanned using resting-state functional magnetic resonance imaging (rs-fMRI). DC and SVM were applied to analyze imaging data. RESULTS: Compared with HCs, MDD patients displayed elevated DC values in the vermis, left anterior cerebellar lobe, hippocampus, and caudate, and depreciated DC values in the left posterior cerebellar lobe, left insula, and right caudate. As per the results of the SVM analysis, DC values in the left anterior cerebellar lobe and right caudate could distinguish MDD from HCs with accuracy, sensitivity, and specificity of 87.71% (353/432), 84.85% (168/198), and 79.06% (185/234), respectively. Our analysis did not reveal any significant correlation among the DC value and the disease duration or symptom severity in patients with MDD. CONCLUSION: Our study demonstrated abnormal DC patterns in patients with MDD. Aberrant DC values in the left anterior cerebellar lobe and right caudate could be presented as potential imaging biomarkers for the diagnosis of MDD. |
format | Online Article Text |
id | pubmed-9486164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94861642022-09-21 Abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: A resting-state functional magnetic resonance imaging study and support vector machine analysis Lin, Hang Xiang, Xi Huang, Junli Xiong, Shihong Ren, Hongwei Gao, Yujun Front Psychiatry Psychiatry OBJECTIVE: Previous studies have revealed abnormal degree centrality (DC) in the structural and functional networks in the brains of patients with major depressive disorder (MDD). There are no existing reports on the DC analysis method combined with the support vector machine (SVM) to distinguish patients with MDD from healthy controls (HCs). Here, the researchers elucidated the variations in DC values in brain regions of MDD patients and provided imaging bases for clinical diagnosis. METHODS: Patients with MDD (N = 198) and HCs (n = 234) were scanned using resting-state functional magnetic resonance imaging (rs-fMRI). DC and SVM were applied to analyze imaging data. RESULTS: Compared with HCs, MDD patients displayed elevated DC values in the vermis, left anterior cerebellar lobe, hippocampus, and caudate, and depreciated DC values in the left posterior cerebellar lobe, left insula, and right caudate. As per the results of the SVM analysis, DC values in the left anterior cerebellar lobe and right caudate could distinguish MDD from HCs with accuracy, sensitivity, and specificity of 87.71% (353/432), 84.85% (168/198), and 79.06% (185/234), respectively. Our analysis did not reveal any significant correlation among the DC value and the disease duration or symptom severity in patients with MDD. CONCLUSION: Our study demonstrated abnormal DC patterns in patients with MDD. Aberrant DC values in the left anterior cerebellar lobe and right caudate could be presented as potential imaging biomarkers for the diagnosis of MDD. Frontiers Media S.A. 2022-09-06 /pmc/articles/PMC9486164/ /pubmed/36147977 http://dx.doi.org/10.3389/fpsyt.2022.960294 Text en Copyright © 2022 Lin, Xiang, Huang, Xiong, Ren and Gao. 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 Lin, Hang Xiang, Xi Huang, Junli Xiong, Shihong Ren, Hongwei Gao, Yujun Abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: A resting-state functional magnetic resonance imaging study and support vector machine analysis |
title | Abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: A resting-state functional magnetic resonance imaging study and support vector machine analysis |
title_full | Abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: A resting-state functional magnetic resonance imaging study and support vector machine analysis |
title_fullStr | Abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: A resting-state functional magnetic resonance imaging study and support vector machine analysis |
title_full_unstemmed | Abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: A resting-state functional magnetic resonance imaging study and support vector machine analysis |
title_short | Abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: A resting-state functional magnetic resonance imaging study and support vector machine analysis |
title_sort | abnormal degree centrality values as a potential imaging biomarker for major depressive disorder: a resting-state functional magnetic resonance imaging study and support vector machine analysis |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9486164/ https://www.ncbi.nlm.nih.gov/pubmed/36147977 http://dx.doi.org/10.3389/fpsyt.2022.960294 |
work_keys_str_mv | AT linhang abnormaldegreecentralityvaluesasapotentialimagingbiomarkerformajordepressivedisorderarestingstatefunctionalmagneticresonanceimagingstudyandsupportvectormachineanalysis AT xiangxi abnormaldegreecentralityvaluesasapotentialimagingbiomarkerformajordepressivedisorderarestingstatefunctionalmagneticresonanceimagingstudyandsupportvectormachineanalysis AT huangjunli abnormaldegreecentralityvaluesasapotentialimagingbiomarkerformajordepressivedisorderarestingstatefunctionalmagneticresonanceimagingstudyandsupportvectormachineanalysis AT xiongshihong abnormaldegreecentralityvaluesasapotentialimagingbiomarkerformajordepressivedisorderarestingstatefunctionalmagneticresonanceimagingstudyandsupportvectormachineanalysis AT renhongwei abnormaldegreecentralityvaluesasapotentialimagingbiomarkerformajordepressivedisorderarestingstatefunctionalmagneticresonanceimagingstudyandsupportvectormachineanalysis AT gaoyujun abnormaldegreecentralityvaluesasapotentialimagingbiomarkerformajordepressivedisorderarestingstatefunctionalmagneticresonanceimagingstudyandsupportvectormachineanalysis |