Reinforcement learning for repetitive transcranial magnetic stimulation protocol selection in treatment-resistant depression

Repetitive transcranial magnetic stimulation (rTMS), including intermittent theta-burst stimulation (iTBS), is an established treatment for treatment-resistant depression, but clinicians lack evidence-based guidance for session-level choices among stimulation type, intensity, and laterality. In this study, we investigated whether a reinforcement learning (RL) algorithm trained on routine clinical data could recommend session-level rTMS protocols associated with greater symptom improvement using electronic health record data from 1609 adults treated with rTMS between January 2023 and June 2025 across 19 clinic groups. Among 370 patients from held-out clinics, treatment decisions matching algorithm recommendations were associated with 0.788 points greater next-session Patient Health Questionnaire-9 improvement than treatments that differed (95% CI: 0.634–0.940).

Following every recommendation was projected to produce 3.22 points of additional improvement (95% CI: 2.72–3.75). These findings suggest RL can find treatment patterns associated with greater next-session improvement and potentially meaningful improvement accumulated across a treatment episode. This work was supported by the National Institute on Drug Abuse under award 1K01DA061975 and the National Institute of Mental Health under award 1UG3MH141096-01.

The funders had no role in study design, analysis, or the decision to submit. These authors jointly supervised this work: Stanley Lyndon, Philip S. Division of Pharmacoepidemiology & Pharmacoeconomics, Mass General Brigham, Harvard Medical School, Boston, MA, USA Jason B.

Gibbons, Loreen Straub, Jie Yang, Meekang Sung, Richard Wyss & Philip S. Wang Department of Statistics, Harvard University, Cambridge, MA, USA Department of Psychiatry, Mass General Brigham, Harvard Medical School, Boston, MA, USA Shan H. Hathaway & Stanley Lyndon Department of Computer Science, Harvard University, Cambridge, MA, USA Department of Epidemiology, Harvard T.H.

Chan School of Public Health, Boston, MA, USA Department of Psychiatry, Cambridge Health Alliance, Cambridge, MA, USA Department of Health Policy, Harvard Medical School, Boston, MA, USA Discovery Behavioral Health, Irvine, CA, USA Rachel Wood, Matthew Ruble, Cason Elliott & Denise H. Gibbons has a financial interest in Adaptive Testing Technologies, a company that develops online mental and behavioral health assessment tools. Gibbons’ interests were reviewed and are managed by BWH and Mass General Brigham in accordance with their conflict-of-interest policies.

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