Data Driven Exploration of Network Connectivity in task-fMRI of Focal Hand Dystonia

Multiple neuroimaging studies suggest that dystonia is a network-level brain disorder, but the key networks to target for brain therapy in dystonia remain poorly understood. This study identified impaired functional networks (FNs) in writers cramp (WC) dystonia while participants performed hand motor tasks in the scanner. Task-fMRI images were acquired from twelve WC and twelve healthy volunteers (HV) while they performed writing, tapping, and flexion-extension motor tasks. Group independent component analysis was used to derive FNs, and functional network connectivity (FNC) between the FNs was computed to evaluate their associations with dystonic behavior. Our approach revealed three novel findings: First, we found that the basal ganglia, orbitofrontal, and superior parietal FNs were aberrant during the on-block of the task and, interestingly, also during the off-block period reminiscent of the loss of network inhibitory response during writing in WC. Second, we found multiple impaired FNC patterns in WC primarily in these three FNs these results were further validated through non-parametric testing via Monte Carlo simulations. Third, using FNC deficits and FNC-writing legibility correlations, we characterized these FNC patterns as primary or secondary associations with the motor program for writing. Findings from our data-driven whole-brain analysis approach reveal new FNs disrupted in dystonia and they may serve as key targets for the treatment of this disorder. Impact StatementDystonia is a rare and disabling brain disorder affecting motor function. In this study, novel functional network differences in dystonia were identified. Associations between functional network connectivity and dystonic behavior were found. Through a data-driven approach, this study reveals new targets for dystonia treatment.