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Predicting dimensional antidepressant response to repetitive transcranial magnetic stimulation using pretreatment resting-state functional connectivity

Nature Mental Health volume 3, pages 1046–1056 (2025)Cite this article

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Abstract

Repetitive transcranial magnetic stimulation is an effective treatment for depression that modulates resting-state functional connectivity (RSFC) of depression-relevant neural circuits. So far, however, few studies have investigated whether individual treatment-related symptom changes are predictable from pretreatment RSFC. Here we use machine learning to predict dimensional changes in depressive symptoms using pretreatment RSFC. We hypothesized that changes in dimensional depressive symptoms would be predicted more accurately than scale total scores. Patients with depression (n = 26) underwent pretreatment RSFC magnetic resonance imaging. Depressive symptoms were assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Random forest regression models were trained to predict treatment-related symptom changes captured by the HDRS-17, HDRS-6 and three previously identified HDRS subscales: core mood and anhedonia (CMA), somatic disturbances and insomnia. Changes along the CMA, HDRS-17 and HDRS-6 were predicted significantly above chance, with 9%, 2% and 2% of out-of-sample outcome variance explained, respectively (all P values <0.001). CMA changes were predicted more accurately than the HDRS-17 (P < 0.05). Higher baseline global connectivity (GC) of default mode network subregions and the somatomotor network predicted poorer outcomes, while higher GC of the right dorsal attention frontoparietal control and visual networks predicted reduced CMA symptoms. HDRS-17 and HDRS-6 changes were predicted with similar GC patterns. These results suggest that RSFC spanning the default mode, somatomotor, dorsal attention, frontoparietal control and visual network subregions predict dimensional changes with significantly greater accuracy than syndromal changes after repetitive transcranial magnetic stimulation. These findings highlight the need to assess more granular clinical dimensions in therapeutic studies and echo earlier studies supporting that dimensional outcomes improve model accuracy.

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Fig. 1: Model performance by outcome.
Fig. 2: Predicted symptom changes given pretreatment connectivity.
Fig. 3: Predictor importance.

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Data availability

The dataset used for this study is not publicly available due to patient privacy concerns but may be made available from the corresponding author on request.

Code availability

Code used for this study is available via GitHub at https://github.com/bscwade/tms_moa_dimensional_predictions.

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Acknowledgements

This work was supported by a K99/R00 Pathway to Independence Award (MH119314 to B.S.C.W.) and NIH grants R01MH112737 and R61MH132869 to J.A.C.

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  1. Division of Neuropsychiatry and Neuromodulation, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Benjamin S. C. Wade, Tracy A. Barbour, Kristen K. Ellard & Joan A. Camprodon

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  2. Tracy A. Barbour
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Contributions

B.S.C.W. designed the study’s machine learning and statistical methods, interpreted the findings and drafted the paper. J.A.C. oversaw and funded data acquisition efforts. T.A.B., K.K.E. and J.A.C. assisted in interpreting the findings and drafting the paper.

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Correspondence to Benjamin S. C. Wade.

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Competing interests

J.A.C. is listed as an inventor on patents and patent applications on neuromodulation targeting methods held by Massachusetts General Hospital. He is a member of the scientific advisory board of Hyka and Flow Neuroscience, and has been a paid consultant for Mifu Technologies, Neuroelectrics, and LivaNova. The other authors report no conflicts of interest.

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Supplementary Tables

Supplementary Table 1. Tabulation of atlas-based parcellations used in the analysis. Supplementary Table 2. Tabulation of Hamilton Depression Rating Scale items present in each subscale.

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Wade, B.S.C., Barbour, T.A., Ellard, K.K. et al. Predicting dimensional antidepressant response to repetitive transcranial magnetic stimulation using pretreatment resting-state functional connectivity. Nat. Mental Health 3, 1046–1056 (2025). https://doi.org/10.1038/s44220-025-00469-5

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