Resting-state Functional Connectivity Predicts Individual Treatment Outcomes of Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder
Year: 2014
Type: dissertation
Abstract: Repetitive transcranial magnetic stimulation (rTMS) is an emerging treatment for treatment-resistant major depressive disorder (MDD). We recently found that treatment response to rTMS of the dorsomedial prefrontal cortex (DMPFC) was bimodal, with individual patients showing either minimal or marked improvement (Downar et al., 2013). Here, we tested whether treatment response could be predicted before initiating treatment. To this end, we trained support vector machine (SVM) classifiers on whole-brain resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) data to learn the treatment outcomes of 41 patients who received rTMS of the DMPFC. The SVMs classified patients with 90% accuracy (p = 0.035, corrected). Classification was based on 9 functional connections, with a pattern of positive and negative functional connectivity in non-responders and responders, respectively. This study represents the first step towards developing a screening tool that can direct treatment decisions.
Source: TSpace (University of Toronto)
Author: N. Bakker
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2754929433