Biomarkers of Suicidal Ideation in Depression: Insights From VMHC Analysis and Machine Learning
Xinlin Wang,1,* Fei Liu,1,* Xinyi Hu,1 Qiong Zhang,1 Xiaofeng Guan,1 Jiaxin Wu,1 Xiangyun Long,2 Zheng Lu1 1Department of Psychiatry, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China; 2Department of Psychiatry, the Affiliated Brain...
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Dove Medical Press
2025-04-01
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| author | Wang X Liu F Hu X Zhang Q Guan X Wu J Long X Lu Z |
| author_facet | Wang X Liu F Hu X Zhang Q Guan X Wu J Long X Lu Z |
| author_sort | Wang X |
| collection | DOAJ |
| description | Xinlin Wang,1,* Fei Liu,1,* Xinyi Hu,1 Qiong Zhang,1 Xiaofeng Guan,1 Jiaxin Wu,1 Xiangyun Long,2 Zheng Lu1 1Department of Psychiatry, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China; 2Department of Psychiatry, the Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, 510370, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zheng Lu, Department of Psychiatry, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Road, Shanghai, 200092, People’s Republic of China, Email luzheng@tongji.edu.cn Xiangyun Long, Department of Psychiatry, the Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, 510370, People’s Republic of China, Email lxylyl_0503@163.comBackground: Suicidal ideation (SI) is a major cause of death in patients with major depressive disorder (MDD). Although current clinical tools can assess suicide risk, objective neurobiological markers based on research remain lacking. Clinical evidence suggests that resting-state functional magnetic resonance imaging (rs-fMRI) studies utilizing voxel-mirrored homotopic connectivity (VMHC) analysis can uncover the neural mechanisms underlying mental disorders. This study explores differences in interhemispheric connectivity between MDD patients with and without SI, aiming to identify imaging biomarkers for suicide risk.Methods: This study included 48 SI patients and 44 non-SI patients. VMHC values were calculated to assess interhemispheric functional connectivity. Brain regions with significant differences between the groups were identified. A support vector machine (SVM) model was applied to evaluate the utility of VMHC values in distinguishing SI patients from non-SI patients with MDD.Results: Patients with suicidal ideation exhibited significantly increased VMHC values in the superior frontal gyrus, putamen, inferior temporal gyrus, and cerebellum compared to those without suicidal ideation. The SVM model achieved an accuracy of 77.2%, sensitivity of 83.3%, specificity of 70.5%, and an area under the curve (AUC) of 0.81. When combining VMHC values from multiple brain regions, classification accuracy improved to 86.8%.Conclusion: MDD patients with SI exhibit abnormal interhemispheric connectivity, with VMHC abnormalities in specific brain regions serving as potential biomarkers for suicide risk. The integration of machine learning and neuroimaging highlights the clinical relevance of VMHC as a tool for early detection and targeted intervention in suicide prevention.Keywords: suicidal ideation, major depressive disorder, support vector machine, fMRI, voxel-mirrored homotopic connectivity, biomarkers, neuroimaging |
| format | Article |
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| institution | OA Journals |
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| language | English |
| publishDate | 2025-04-01 |
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| series | Neuropsychiatric Disease and Treatment |
| spelling | doaj-art-37394be664c744e39b53de2acee8ba692025-08-20T02:12:10ZengDove Medical PressNeuropsychiatric Disease and Treatment1178-20212025-04-01Volume 21855865102048Biomarkers of Suicidal Ideation in Depression: Insights From VMHC Analysis and Machine LearningWang XLiu FHu XZhang QGuan XWu JLong XLu ZXinlin Wang,1,* Fei Liu,1,* Xinyi Hu,1 Qiong Zhang,1 Xiaofeng Guan,1 Jiaxin Wu,1 Xiangyun Long,2 Zheng Lu1 1Department of Psychiatry, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China; 2Department of Psychiatry, the Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, 510370, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zheng Lu, Department of Psychiatry, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Road, Shanghai, 200092, People’s Republic of China, Email luzheng@tongji.edu.cn Xiangyun Long, Department of Psychiatry, the Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, 510370, People’s Republic of China, Email lxylyl_0503@163.comBackground: Suicidal ideation (SI) is a major cause of death in patients with major depressive disorder (MDD). Although current clinical tools can assess suicide risk, objective neurobiological markers based on research remain lacking. Clinical evidence suggests that resting-state functional magnetic resonance imaging (rs-fMRI) studies utilizing voxel-mirrored homotopic connectivity (VMHC) analysis can uncover the neural mechanisms underlying mental disorders. This study explores differences in interhemispheric connectivity between MDD patients with and without SI, aiming to identify imaging biomarkers for suicide risk.Methods: This study included 48 SI patients and 44 non-SI patients. VMHC values were calculated to assess interhemispheric functional connectivity. Brain regions with significant differences between the groups were identified. A support vector machine (SVM) model was applied to evaluate the utility of VMHC values in distinguishing SI patients from non-SI patients with MDD.Results: Patients with suicidal ideation exhibited significantly increased VMHC values in the superior frontal gyrus, putamen, inferior temporal gyrus, and cerebellum compared to those without suicidal ideation. The SVM model achieved an accuracy of 77.2%, sensitivity of 83.3%, specificity of 70.5%, and an area under the curve (AUC) of 0.81. When combining VMHC values from multiple brain regions, classification accuracy improved to 86.8%.Conclusion: MDD patients with SI exhibit abnormal interhemispheric connectivity, with VMHC abnormalities in specific brain regions serving as potential biomarkers for suicide risk. The integration of machine learning and neuroimaging highlights the clinical relevance of VMHC as a tool for early detection and targeted intervention in suicide prevention.Keywords: suicidal ideation, major depressive disorder, support vector machine, fMRI, voxel-mirrored homotopic connectivity, biomarkers, neuroimaginghttps://www.dovepress.com/biomarkers-of-suicidal-ideation-in-depression-insights-from-vmhc-analy-peer-reviewed-fulltext-article-NDTsuicidal ideation;major depressive disordersupport vector machinefmrivoxel-mirrored homotopic connectivitybiomarkersneuroimaging |
| spellingShingle | Wang X Liu F Hu X Zhang Q Guan X Wu J Long X Lu Z Biomarkers of Suicidal Ideation in Depression: Insights From VMHC Analysis and Machine Learning Neuropsychiatric Disease and Treatment suicidal ideation;major depressive disorder support vector machine fmri voxel-mirrored homotopic connectivity biomarkers neuroimaging |
| title | Biomarkers of Suicidal Ideation in Depression: Insights From VMHC Analysis and Machine Learning |
| title_full | Biomarkers of Suicidal Ideation in Depression: Insights From VMHC Analysis and Machine Learning |
| title_fullStr | Biomarkers of Suicidal Ideation in Depression: Insights From VMHC Analysis and Machine Learning |
| title_full_unstemmed | Biomarkers of Suicidal Ideation in Depression: Insights From VMHC Analysis and Machine Learning |
| title_short | Biomarkers of Suicidal Ideation in Depression: Insights From VMHC Analysis and Machine Learning |
| title_sort | biomarkers of suicidal ideation in depression insights from vmhc analysis and machine learning |
| topic | suicidal ideation;major depressive disorder support vector machine fmri voxel-mirrored homotopic connectivity biomarkers neuroimaging |
| url | https://www.dovepress.com/biomarkers-of-suicidal-ideation-in-depression-insights-from-vmhc-analy-peer-reviewed-fulltext-article-NDT |
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