NEUROPHYSIOLOGICAL AND NEUROIMAGING MARKERS OF REPETITIVE TRANSCRANIAL STIMULATION TREATMENT RESPONSE IN MAJOR DEPRESSIVE DISORDER: A SYSTEMATIC REVIEW AND META-ANALYSIS OF PREDICTIVE MODELING STUDIES
Authors: Jin *Minxia , Qin Penny Ping , Xia Adam Wei Li , Kan Rebecca Lai Di , Zhang Bella Bing Bing , Tang Alvin Hong Pui , Li Ami Sin Man , Lin Tim Tian Ze , Pei Jun Jie , Kranz Georg S 
Published: February 2025

Abstract

Background
Predicting repetitive transcranial magnetic stimulation (rTMS) treatment outcomes in major depressive disorder (MDD) could reduce the financial and psychological risks of treatment failure [1, 2].Neurophysiological and neuroimaging techniques are being increasingly used for this purpose [3, 4].

Aims & Objectives
We sought to systematically review and meta-analyze predictive modeling studies that leveraged neurophysiological and neuroimaging techniques to predict rTMS response in MDD and to identify the methodological limitations of the current evidence.

Method
PubMed, Medline, EMBASE, CENTRAL, and PsycINFO from inception to May 25, 2023, were searched for eligible articles. The primary meta-analysis outcome was predictive accuracy pooled from classification models. As a secondary analysis, study design (prospective vs. retrospective), sample size, rTMS protocols (rTMS vs. rTMS + intermittent theta burst stimulation, iTBS) and treatment duration were treated as predictor variables in bivariate meta-regression. Regression models were summarized qualitatively. A promising marker was identified if it showed a sensitivity and specificity of 80% or higher in at least two independent studies. We evaluated articles using the Quality Assessment of Diagnostic Accuracy Studies-2 and indicators of good prediction-based research practice [5].

Results
Searching yielded 36 eligible studies. Twenty-two classification modeling studies produced an estimated area under the summary receiver operator curve of 0.87 (95% CI = 0.83 to 0.92), with 86.8% sensitivity (95% CI = 80.6 to 91.2%) and 81.9% specificity (95% CI = 76.1 to 86.4%). All regression models except one demonstrated significant predictive accuracy, with the coefficient of determination ranging from 0.16 to 0.78. Age significantly moderated pooled estimates of classification accuracy in the bivariate meta-regression. None of the other covariates were statistically significant. Replications for each specific marker are rare and sometimes from the same research group. No specific marker was considered clinically promising. Frontal theta cordance measured by electroencephalography is closest to proof of concept. Most studies did not meet all the quality metrics.

Discussion & Conclusion
Predicting rTMS response using neurophysiological and neuroimaging markers has yet to be ready for clinical decision-making due to the heterogeneous models and markers used, the small sample size, and the lack of model validation. Future marker research based on hundreds of samples that demonstrate rigorous generalizability across independent datasets will be helpful to be incorporated into clinical practice [6].

References
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