NEUROPHYSIOLOGICAL AND NEUROIMAGING MARKERS OF REPETITIVE TRANSCRANIAL STIMULATION TREATMENT RESPONSE IN MAJOR DEPRESSIVE DISORDER: A SYSTEMATIC REVIEW AND META-ANALYSIS OF PREDICTIVE MODELING STUDIES
Bibliographic record
Abstract
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 1.MCINTYRE, R. S. & O'DONOVAN, C. 2004. The human cost of not achieving full remission in depression. Can J Psychiatry, 49, 10-16. 2.MILEV, R. V., GIACOBBE, P., KENNEDY, S. H., BLUMBERGER, D. M., DASKALAKIS, Z. J., DOWNAR, J., MODIRROUSTA, M., PATRY, S., VILA-RODRIGUEZ, F., LAM, R. W., MACQUEEN, G. M., PARIKH, S. V. &RAVINDRAN, A. V. 2016. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder: Section 4. Neurostimulation Treatments. Can J Psychiatry, 61, 561-75. 3.WIDGE, A. S., BILGE, M. T., MONTANA, R., CHANG, W., RODRIGUEZ, C. I., DECKERSBACH, T., CARPENTER, L. L., KALIN, N. H. &NEMEROFF, C. B. 2019. Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis. Am J Psychiatry, 176, 44- 56. 4.COHEN, S. E., ZANTVOORD, J. B., WEZENBERG, B. N., BOCKTING, C. L. H. &VAN WINGEN, G. A. 2021. Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis. Transl Psychiatry, 11, 168. 5.WHITING, P. F., RUTJES, A. W., WESTWOOD, M. E., MALLETT, S., DEEKS, J. J., REITSMA, J. B., LEEFLANG, M. M., STERNE, J. A. &BOSSUYT, P. M. 2011. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 155, 529-36. 6.WOO, C. W., CHANG, L. J., LINDQUIST, M. A. &WAGER, T. D. 2017. Building better biomarkers: brain models in translational neuroimaging. Nat Neurosci, 20, 365-377.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".