Using routine MRI data of depressed patients to predict individual responses to electroconvulsive therapy.

Using routine MRI data of depressed patients to predict individual responses to electroconvulsive therapy. Exp Neurol. 2020 Oct 14;:113505 Authors: Gärtner M, Ghisu E, Herrera-Melendez AL, Koslowski M, Aust S, Asbach P, Otte C, Regen F, Heuser I, Borgwardt K, Grimm S, Bajbouj M Abstract Electroconvulsive therapy (ECT) is one of the most effective treatments in cases of severe and treatment resistant major depression. 60-80% of patients respond to ECT, but the procedure is demanding and robust prediction of ECT responses would be of great clinical value. Predictions based on neuroimaging data have recently come into focus, but still face methodological and practical limitations that are hampering the translation into clinical practice. In this retrospective study, we investigated the feasibility of ECT response prediction using structural magnetic resonance imaging (sMRI) data that was collected during ECT routine examinations. We applied machine learning techniques to predict individual treatment outcomes in a cohort of N = 71 ECT patients, N = 39 of which responded to the treatment. SMRI-based classification of ECT responders and non-responders reached an accuracy of 69% (sensitivity: 67%; specificity: 72%). Classification on additionally investigated clinical variables had no predictive power. Since dichotomisation of patients into ECT responders and non-responders is debatable due to many patients only showing a partial r...
Source: Experimental Neurology - Category: Neurology Authors: Tags: Exp Neurol Source Type: research