Uncovering Neural Signatures of Convulsive Therapy in Depression Using Massive EEG Time-Series Feature Extraction

Abstract BACKGROUND Convulsive therapies, including electroconvulsive therapy (ECT) and magnetic seizure therapy (MST), are highly effective for treatment-resistant depression, however, their neural mechanisms remain incompletely understood. We tested whether a data-driven framework applying a massive time-series feature library to electroencephalography (EEG) could reveal novel insights into changes in functional brain dynamics following convulsive therapy and provide preliminary markers of response. METHODS Resting-state EEG was analysed before and after a course of ECT or MST in 42 patients. Data were pooled and reduced to three principal components (PCs) capturing 78.1% of total variance, then >7,000 time-series features per PC were extracted from each patient using the highly comparative time-series analysis ( hctsa ) framework. Linear support vector machines (SVMs) classified pre-versus post-treatment EEG for each PC. A separate linear SVM, was also trained on 18 representative baseline features to predict clinical response. RESULTS hctsa- based classifiers distinguished pre-from post-treatment EEG for each PC (accuracy 73.8-76.2%, p FDR