Prediction of Clinical Response of Transcranial Magnetic Stimulation Treatment for Major Depressive Disorder Using Hyperdimensional Computing

• Published in: IEEE journal of biomedical and health informatics Vol. 29; no. 5; pp. 3678 - 3686
• Main Authors: Ge, Lulu, McInnes, Aaron N., Widge, Alik S., Parhi, Keshab K.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2025
• Subjects: Adult; Biomedical measurement; Classification; Depression; Depressive Disorder, Major - physiopathology; Depressive Disorder, Major - therapy; Encoding; Female; Humans; hyperdimensional computing (HDC); major depressive disorder (MDD); Male; Middle Aged; Reviews; Signal Processing, Computer-Assisted; Support Vector Machine; support vector machine (SVM); Support vector machines; Training; Trajectory; Transcranial magnetic stimulation; transcranial magnetic stimulation (TMS); Transcranial Magnetic Stimulation - methods; Treatment Outcome; vector symbolic architecture (VSA); Vectors; Young Adult
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2025.3537757

Cognitive control dysregulation is nearly universal across disorders, including major depressive disorder (MDD). Achieving comparable response rates to medication, the transcranial magnetic stimulation (TMS) mechanism and its effect on cognitive control have not been well understood yet. This paper investigates the predictive capability of the clinical response to TMS treatment using 34 cognitive variables measured from TMS treatment of 22 MDD subjects over an eight-week period. We employ a novel brain-inspired computing paradigm, hyperdimensional computing (HDC), to classify the effectiveness of TMS using leave-one-subject-out cross-validation (LOSOCV). Four performance metrics-accuracy, sensitivity, specificity and AUC-are used, with AUC being the primary metric. Experimental results reveal that: i) . Although SVM outperforms HDC in terms of accuracy, HDC achieves an AUC of 0.82, surpassing SVM by 0.07. ii) . The optimal performance for both classifiers is obtained with feature selection using SelectKBest. iii) Among the top features selected by SelectKBest for the two classifiers, ws_MedRT (median rate for the Websurf task) shows a more distinguishable distribution between clinical responses ("1") and no clinical responses ("0"). In conclusion, these results highlight the potential of HDC for predicting clinical responses to TMS and underscore the importance of feature selection in improving classification performance.