aceMachine learning-based source-level EEG functional network biomarkers of antidepressant treatment response in major depressive disorder
Major depressive disorder (MDD) is highly prevalent, yet antidepressant selection remains largely trial and error because of limited predictive biomarkers. Resting-state electroencephalography (EEG) combined with graph theory-based functional networks offers a noninvasive approach to probing brain dysconnectivity. This study aimed to delineate source-level functional network differences between antidepressant responders and non-responders, and to evaluate whether machine learning models using nodal network featu…
A support vector machine using source-level EEG nodal network features classified 47 patients with depression as 12-week antidepressant responders or non-responders with about 83% accuracy, suggesting a route beyond trial-and-error prescribing.
The sample is small (20 responders and 27 non-responders) and the confidence intervals are wide, and the authors say only that the approach 'may identify' pretreatment features associated with response.
Evidence
- Peer-reviewedProgress in Neuro-Psychopharmacology and Biological Psychiatry2026-10-02
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Truvace Impact Record TRV-2026-1359, v1: “Machine learning-based source-level EEG functional network biomarkers of antidepressant treatment response in major depressive disorder.” Truvace, 2026-10-14. /record/TRV-2026-1359 (accessed at citation time). sha256 11f4687c6c74f10d…
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