NEUROPHYSIOLOGICAL AND NEUROIMAGING MARKERS OF REPETITIVE TRANSCRANIAL STIMULATION TREATMENT RESPONSE IN MAJOR DEPRESSIVE DISORDER: A SYSTEMATIC REVIEW AND META-ANALYSIS OF PREDICTIVE MODELING STUDIES
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,032 | 0,074 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,020 | 0,041 |
| Bibliométrie | 0,012 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».