Optimized DBS in pediatric dystonia restores balance in transmission of signals within pallidothalamic network by modulating neural oscillations in deep brain regions

Deep brain stimulation (DBS) is a neuromodulation technique commonly used for treatment of movement disorders, including dystonia. Stimulation of the globus pallidus internus (GPi) of basal ganglia or the subthalamic nucleus (STN) typically confers clinical benefit, although the specific mechanism of action remains unclear. Previous studies in dystonic patients show abnormalities in low-frequency activity in GPi and other motor sensory regions such as STN, ventralis oralis anterior/posterior (VoaVop), and ventral anterior (VA) nuclei of thalamus. We hypothesize that DBS works in part by modulating transmission of abnormal signals in low frequency bands between different brain regions, both at the stimulation site (e.g. GPi) and distant deep brain regions.

To test this hypothesis, we used a novel transfer function analysis that has not previously been utilized to study neural signal transmission. We recorded intracranial signals from 13 pediatric and young adult patients with dystonia, with and without stimulation. We performed transfer function analysis to compare the mean transfer function gain—representing signal amplification from input to output—across deep brain pathways in low-frequency bands, under both DBS-on and DBS-off conditions. Our results show that DBS modulates signal transmission between different brain regions. In particular, GPi stimulation increased transfer function gains from pallidum to thalamic motor subnuclei, especially in the beta and gamma frequency bands. These findings support the hypothesis that DBS decreases inhibitory output from GPi to thalamus through enhanced high-frequency transmission, offering insight into its mechanism of action. This, in turn, may provide fundamental knowledge for the development of closed-loop DBS, particularly in controlling the intensity and pattern of stimulation. A better understanding of neuromodulation could also help to further the design of brain-computer interfaces and neurorehabilitation systems.