Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis

Abstract

Background: The variability of responses to plasticity-inducing repetitive transcranial magnetic stimulation (rTMS) challenges its successful application in psychiatric care. No objective means currently exists to individually predict the patients' response to rTMS.

Methods: We used machine learning to develop and validate such tools using the pre-treatment structural Magnetic Resonance Images (sMRI) of 92 patients with schizophrenia enrolled in the multisite RESIS trial (https://ichgcp.net/clinical-trials-registry/NCT00783120" title="See in ClinicalTrials.gov">NCT00783120): patients were randomized to either active (N = 45) or sham (N = 47) 10-Hz rTMS applied to the left dorsolateral prefrontal cortex 5 days per week for 21 days. The prediction target was nonresponse vs response defined by a ≥20% pre-post Positive and Negative Syndrome Scale (PANSS) negative score reduction.

Results: Our models predicted this endpoint with a cross-validated balanced accuracy (BAC) of 85% (nonresponse/response: 79%/90%) in patients receiving active rTMS, but only with 51% (48%/55%) in the sham-treated sample. Leave-site-out cross-validation demonstrated cross-site generalizability of the active rTMS predictor despite smaller training samples (BAC: 71%). The predictive pre-treatment pattern involved gray matter density reductions in prefrontal, insular, medio-temporal, and cerebellar cortices, and increments in parietal and thalamic structures. The low BAC of 58% produced by the active rTMS predictor in sham-treated patients, as well as its poor performance in predicting positive symptom courses supported the therapeutic specificity of this brain pattern.

Conclusions: Individual responses to active rTMS in patients with predominant negative schizophrenia may be accurately predicted using structural neuromarkers. Further multisite studies are needed to externally validate the proposed treatment stratifier and develop more personalized and biologically informed rTMS interventions.

Figures

Fig. 1.

Reliability of the baseline gray matter density pattern predicting subsequent response vs nonresponse to active repetitive transcranial magnetic stimulation (rTMS). The reliability of the gray matter density (GMD) pattern elements was measured in terms of a Cross-Validation Ratio (CVR) map [CVR = mean(w) / standard error(w)], where w are the weight vectors of the 5111 Support Vector Machine (SVM) models generated in the study’s repeated nested cross-validation setup]. The CVR map was thresholded at a CVR of ±3, corresponding to an alpha level of .01 reliable areas of GMD reduction in non-responders (NON-RESP) vs responders (RESP) are shaded in red colours, whereas areas of GMD increments are painted in green. The open-source 3D rendering software MRIcroGL (C. Rohrden) available at https://www.nitrc.org/projects/mricrogl/ was used to overlay the CVR map on the MNI single-subject template.

Fig. 2.

Sorted neuroanatomical nonresponse likelihoods of patients treated with active repetitive transcranial magnetic stimulation (rTMS) (A1). For each patient in study population the active rTMS outcome predictor generated ensemble-based out-of-training probabilities of belonging to the nonresponse group (y axis). The figure shows these likelihoods sorted in ascending order, with red-colored subjects indicating misclassifications. Using these outcome predictions, a Receiver-Operating Characteristic (ROC) analysis was conducted (A2). Based on the patients identified as support vectors by the Support Vector Machine (SVM) algorithm, voxel-level mean and standard deviation maps were computed and used to standardize patients in the active rTMS group. The standardized maps were separately averaged for the 2 prediction groups to illustrate the quantitative baseline gray matter density (GMD) differences between patients with predicted nonresponses (top) vs responses (bottom) to active rTMS. The bottom panel of the figure shows the sorted outcome probabilities and misclassification (circles) for each of the 3 RESIS sites (B1) as well as the respective ROCs and Areas-under-the-Curve (AUC; B2).

Fig. 3.

(A) Correlation analyses between patients’ neuroanatomical likelihood of nonresponse to active (top) and sham (bottom) TMS and PANSS-NS absolute score changes measured between T0 and T1. (B) Descriptive absolute PANSS-NS trajectory graphs of patients with predicted nonresponse (red) vs response (green) to active (top) vs sham (bottom) repetitive transcranial magnetic stimulation (rTMS) spanning from baseline to day 105 follow-up examinations. Here, the T1 timepoint (day 21) was included in the figure for visualization purposes.