Structural-functional multilayer brain network properties of the stimulation location predict outcome of repetitive transcranial magnetic stimulation for obsessive-compulsive disorder

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Abstract

Background

Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for obsessive-compulsive disorder (OCD), but response rates are variable. Pre-treatment characteristics of the stimulated region can influence rTMS treatment outcome.

Objective/hypothesis

We investigated the relationship between network features of the rTMS stimulation location and treatment outcome in a randomised trial of rTMS for OCD, using graph analysis of single-layer functional and structural brain networks, and of structural-functional multilayer networks.

Methods

We analysed data from 58 treatment-refractory adult OCD patients. Participants received either: high frequency (HF) rTMS to the left dorsolateral prefrontal cortex (DLPFC) (n=19); HF rTMS to the left pre-supplementary motor area (preSMA) (n=21); or control rTMS to the vertex (n=18). We used pre-treatment resting state functional MRI (rs-fMRI) and diffusion MRI (dMRI) scans to construct single-layer and structural-functional multilayer networks for each participant. We computed various centrality measures of the stimulated location and connected subnetwork, and examined their relationship with treatment outcome.

Results

We found no associations between functional or structural single-layer network characteristics and treatment outcome. However, higher average multilayer betweenness centrality of the stimulated subnetwork in the DLPFC rTMS group (but not in the preSMA or vertex groups) was associated with greater symptom reduction (p=0.013).

Conclusions

Participants with greater integration of the stimulated subnetwork with the rest of the structural-functional network showed greater improvement following DLPFC rTMS. Our results give a preliminary indication that the outcome of rTMS treatment for OCD is dependent on the interplay between structural and functional networks.

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