Predictors of ECT Response: Chapter in Korean Book on Treatment of MDD

Out on PubMed, from authors in Korea, is this book chapter:

Electroconvulsive Therapy (ECT) in Major Depression: Oldies but Goodies.

Yun JY, Kim YK.Adv Exp Med Biol. 2024;1456:187-196. doi: 10.1007/978-981-97-4402-2_10.PMID: 39261430 Review.

The abstract is copied below:

Electroconvulsive therapy is one of the useful treatment methods for symptom improvement and remission in patients with treatment-resistant depression. Considering the various clinical characteristics of patients experiencing depression, key indicators are extracted from structural brain magnetic resonance imaging, functional brain magnetic resonance imaging, and electroencephalography (EEG) data taken before treatment, and applied as explanatory variables in machine learning and network analysis. Studies that attempt to make reliable predictions about the degree of response to electroconvulsive treatment and the possibility of remission in patients with treatment-resistant depression are continuously being published. In addition, studies are being conducted to identify the correlation with clinical improvement by taking structural-functional brain magnetic resonance imaging after electroconvulsive therapy in depressed patients. By reviewing and integrating the results of the latest studies on the above matters, we aim to present the usefulness of electroconvulsive therapy for improving the personalized prognosis of patients with treatment-resistant depression.

Keywords: Biomarker; Electroconvulsive therapy; Electroencephalography; Machine learning; Magnetic resonance imaging; Major depressive disorder; Personalized medicine.

The ECT chapter: 

Here is a book chapter about ECT response prediction, mostly from neuroimaging and using machine learning techniques. The title is goofy, but there may be something of interest to those scholars of the ECT response prediction literature.