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Functional connectome-based predictive modeling of suicidal ideation.

Lynnette A Averill¹, Amanda J F Tamman², Samar Fouda³

¹Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, 1977 Butler Boulevard, Houston, TX 77030, USA; Michael E. DeBakey VA Medical Center, 2002 Holcombe Boulevard, Houston, TX 77030, USA; Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA.

Journal of Affective Disorders

|May 29, 2025

Summary

Researchers identified brain network differences linked to suicidal ideation using machine learning. These findings may reveal new therapeutic targets for suicide prevention by understanding brain connectivity.

Keywords:

Brain imaging Functional connectivity Intrinsic connectivity networks Machine learning Suicidal ideation Suicide

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Area of Science:

Neuroscience
Psychiatry
Computational Biology

Background:

Suicide remains a significant societal threat, with limited understanding of its underlying biological mechanisms.
Advancements in medicine have not fully elucidated the neural basis of suicidal behavior, hindering the development of targeted therapies.

Purpose of the Study:

To identify a reproducible brain network biomarker associated with suicidal ideation.
To explore potential targets for novel anti-suicidal therapeutics based on brain connectivity patterns.

Main Methods:

Utilized a connectome predictive modeling (CPM) machine learning approach.
Analyzed resting-state functional magnetic resonance imaging (fMRI) data from 261 patients with major depressive disorder.
Compared brain network connectivity in individuals with and without suicidal ideation.

Main Results:

Found a robust biomarker for suicidal ideation characterized by increased internal connectivity and decreased external connectivity in key brain networks (central executive, default mode, dorsal salience).
Observed higher external connectivity between ventral salience and sensorimotor/visual networks correlated with increased suicidal ideation.
These connectivity alterations suggest reduced network integration and increased segregation in individuals at higher suicide risk.

Conclusions:

The identified brain network patterns serve as a potential biomarker for suicidal ideation.
Findings offer novel avenues for developing therapeutics targeting specific neural alterations to increase network integration and reduce suicide risk.