TMS-Evoked Cortical Reactivity Signatures as a Multidimensional Biomarker of Depression: A TMS-EEG Meta-Analysis from Diagnosis to Prognosis.

Authors

Ji-Yu Xie, Tao Zhao, Liang Guo, Dong Li, Yanjie Zhao, Zhimin Wang, Pengfei Wang, Linghui Meng, Gang Wang, Zhi Yang

Published in

Biological psychiatry. Oct 03, 2026. Epub Oct 03, 2026.

Abstract

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) enables perturbation-based assessment of cortical reactivity and has emerged as a promising tool for identifying neurophysiological biomarkers in major depressive disorder (MDD). However, the literature remains fragmented, and TMS-EEG metrics have yet to be systematically integrated within a unified framework spanning diagnosis, treatment, and prognosis.
PubMed, Scopus, Ovid, and Web of Science were systematically searched for studies reporting single-pulse TMS-EEG measures in MDD. Random-effects meta-analyses were performed to synthesize evidence across three hierarchical biomarker validation domains: characterization (diagnostic discrimination and state-related symptom tracking), treatment response (intervention monitoring and clinical coupling with remission), and baseline prediction.
Across 21 studies comprising 1,025 patients with MDD and 517 healthy controls, negative-polarity components were significantly enhanced in MDD (Hedges' g = 0.36), driven primarily by the N100 (g = 0.35), which was also associated with greater symptom severity (g = 0.67). Treatment attenuated N100 (g = -0.21) while elevating P180 (g = 0.52). Critically, treatment-induced reduction in negative-polarity responses was strongly coupled with clinical remission (g = 0.91), with the N100 showing the most robust component-level effect (g = 0.85). Preliminary evidence further indicated that baseline negative-polarity components predicted treatment response (g = 1.35).
TMS-EEG cortical reactivity signatures, particularly negative-polarity components and their N100 subcomponent, demonstrated convergent effects across disease characterization, treatment monitoring, and outcome prediction, supporting their candidacy as multidimensional neurophysiological biomarkers for MDD with potential clinical utility in biomarker-guided precision psychiatry.

PMID:
42829057
Bibliographic data and abstract were imported from PubMed on 04 Oct 2026.

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