Use of Machine Learning in Predicting Clinical Response to TMS in Comorbid Posttraumatic Stress Disorder and Major Depression

Use of Machine Learning in Predicting Clinical Response to TMS in Comorbid Posttraumatic Stress Disorder and Major Depression: A Resting State Electroencephalography Study

Journal of Affective Disorders. 252:47-54, 2019 06 01.

Zandvakili A; Philip NS; Jones SR; Tyrka AR; Greenberg BD; Carpenter LL.

Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for other conditions including posttraumatic stress disorder (PTSD). Understanding the mechanisms of TMS action and developing biomarkers predicting response remain important goals. We applied a combination of machine learning and electroencephalography (EEG), testing whether machine learning analysis of EEG coherence would (1) predict clinical outcomes in individuals with comorbid MDD and PTSD, and (2)
determine whether an individual had received a TMS course.

We collected resting-state 8-channel EEG before and after TMS (5Hz to the left dorsolateral prefrontal cortex). We used Lasso regression and Support Vector Machine (SVM) to test the hypothesis that baseline EEG coherence predicted the outcome and to assess if EEG coherence changed after TMS.

In our sample, clinical response to TMS were predictable based on pretreatment EEG coherence (n=29). After treatment, 13/29 had more than 50% reduction in MDD self-report score 12/29 had more than 50% reduction in PTSD self-report score. For MDD, area under roc curve was for MDD was 0.83 (95% confidence interval 0.69-0.94) and for PTSD was 0.71 (95% confidence interval 0.54-0.87). SVM classifier was able to accurately assign EEG recordings to pre- and post-TMS treatment. The accuracy for Alpha, Beta, Theta and Delta bands was 75.4+/-1.5%, 77.4+/-1.4%, 73.8+/-1.5%, and 78.6+/-1.4%, respectively, all significantly better than chance (50%, p<0.001).

Limitations of this work include lack of sham condition, modest sample size, and a sparse electrode array. Despite these methodological limitations, we found validated and clinically meaningful results.

Machine learning successfully predicted non-response to TMS with high specificity, and identified pre- and post-TMS status using EEG coherence. This approach may provide mechanistic insights and may also become a clinically useful screening tool for TMS candidates.