Neuroimaging-based analysis of DBS outcome in pediatric dystonia: Insights from the GEPESTIM registry

Introduction Deep brain stimulation (DBS) is an established treatment in patients with pharmaco-resistant neurological disorders of different ages. Surgical targeting and postoperative programming of DBS depend on the spatial location of the stimulating electrodes in relation to the surrounding anatomical structures and on electrode connectivity to a specific distributed pattern of brain networks. Such information is usually collected using group-level analysis which relies on the availability normative imaging-resources (atlases and connectomes). To this end, analyzing DBS data of children with debilitating neurological disorders like dystonia would make benefit from such resources, especially given the developmental differences between adults and children neuroimaging data. We assembled pediatric, normative neuroimaging-resources from open-access neuroimaging datasets and illustrated their utility on a cohort of children with dystonia treated with pallidal DBS. We aimed to derive a local pallidal sweetspot and explore a connectivity fingerprint associated with pallidal stimulation to exemplify the utility of the assembled imaging resources. Methods A pediatric average brain template was implemented and used to localize DBS electrodes of twenty patients of the GEPESTIM registry cohort. Next, a pediatric subcortical atlas was also employed to highlight anatomical structures of interest. Local pallidal sweetspot was modeled and its degree of overlap with stimulation volumes was calculated as a correlate of individual clinical outcome. Additionally, a pediatric functional connectome of neurotypical subjects was built to allow network-based analyses and decipher a connectivity fingerprint responsible for clinical improvement in our cohort. Results We successfully implemented a pediatric neuroimaging dataset that will be made available to public use as a tool for DBS-analyses. Overlap of stimulation volumes with the identified DBS-sweetspot model correlated significantly with improvement on a local spatial level (R = 0.46, permuted p = 0.019). Functional connectivity fingerprint of DBS-outcome was determined as a network correlate of therapeutic pallidal stimulation in children with dystonia (R = 0.30, permuted p = 0.003). Conclusions Local sweetspot and distributed network models provide neuroanatomical substrates for DBS-associated clinical outcome in dystonia using pediatric neuroimaging surrogate data. The current implementation of pediatric neuroimaging dataset might help improving the practice of DBS-neuroimaging analyses in pediatric patients.