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Mapping Intersubject Variability in Functional Connectivity in Gray and White Matter to Predict Suicide Risk Among Patients with Major Depressive Disorder

Academic RadiologyResearch Authors: Wanyang Jing, MD; Lei Yang, MD; Yuting Guo, MD; Danni Chen, MD; Kaili Che, MD; Fanghui Dong, MD; Chao Li, Ph.D; Yinghong Shi, Ph.D; Haizhu Xie, Ph.D; Feng Zhao, Ph.D; Xicheng Song, Ph.D; Heng Ma, Ph.D; Tongpeng Chu, Ph.D; Ning Mao, Ph.DAIIM Authors: Raymond Cheng, Layna ParaboschiApproved by President Reda RiffiPublication Date: 8/8/2025

Comprehensive Summary

In this study by Jing and Yang et al., intersubject variability in functional connectivity (IVFC) in grey matter (GM) and white matter (WM) was examined as a potential biomarker for predicting suicideal ideation (SI) and suicide attempts (SA) among major depressive disorder (MDD) patients. This study was conducted with a homogeneous Chinese cohort of 178 MDD patients and 173 healthy controls; after the exclusion of certain subjects for health-related reasons, MDD subjects were then split into three categories based on suicide status - non-suicidal (NS), SI, and SA. IVFC information was determined using fMRI scans of the subjects and, after statistical filtering, was used to train Support Vector Machine (SVM) and eXtreme Gradient Boosting Tree (XGBoost) models for suicide status and diagnosis prediction. From the results, utilizing a combination of WM and GM IVFC biomarkers consistently improved model accuracy for both SVM and XGBoost in SI and SA prediction. Furthermore, These models detected significant IVFC differences among NS, SI, and SA patients in the limbic system and other brain regions, such as the thalamus and the superior frontal gyrus. While certain factors, such as the differences of IVFC among MDD patients across different demographics and time-related variability of IVFC biomarkers, were not considered for this study, the usage of WM and GM IVFC biomarkers potentially offers a highly accurate means of assessing suicide risk among MDD patients.

Outcomes and Implications

This study elucidates on the usage of machine learning models and biomarkers to aid in the psychiatric assessment of major depressive disorder. Clinically, this may help reduce the incidence of suicide among MDD patients, which has the highest suicide incident rates across all psychiatric disorders. The authors of this study proposed that, in the future, these experimental method should be used in conjunction to conventional methods for assessing suicide among MDD patients, and the diagnostic model should be simultaneously modified to improve over time.

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