Static and dynamic network properties of the repetitive transcranial magnetic stimulation target predict changes in emotion regulation in obsessive-compulsive disorder

Background: Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation technique to treat psychiatric disorders, such as obsessive-compulsive disorder (OCD). However, the rTMS response varies across subjects. Objective/hypothesis: We hypothesize that baseline network properties of the rTMS target may help understand this variation and predict response. Methods: Excitatory rTMS to the dorsolateral prefrontal cortex (dlPFC) was applied in 19 unmedicated OCD patients, while inhibitory dlPFC-rTMS was applied in 17 healthy controls. The vertex was used as an active control target (19 patients, 18 controls). The rTMS response was operationalized as the individual change in state distress rating during an emotion regulation task. At baseline, subjects underwent resting-state functional MRI. The brain network was constructed by calculating wavelet coherence between regional activity of regions in the Brainnetome atlas. Local and integrative static connectivity and the dynamic network role of the target were calculated. Baseline target region network features were non-parametrically correlated to rTMS response. Results: In the dIPFC-stimulated patients, greater local connectivity (Kendall's Tau = -0.415, p = 0.013) and less promiscuous role of the target (Kendall's Tau = 0.389, p = 0.025) at baseline were related to greater distress reduction after excitatory rTMS. There were no significant associations in healthy subjects nor in the active control stimulated patients. Conclusions: Pre-treatment network topological indices predict rTMS-induced emotional response changes in OCD, such that greater baseline resting-state local connectivity and less temporal integration of the target region imply greater stimulation effects. These results may lead the way towards personalized neuromodulation in OCD. (C) 2019 The Author(s). Published by Elsevier Inc.