Complex network measures reveal optimal targets for deep brain stimulation and identify clusters of collective brain dynamics

An important question in computational neuroscience is how to improve the efficacy of deep brain stimulation by extracting information from underlying connectivity structure. Recent studies also highlight relation structural and functional disorders such as Parkinson’s disease. Exploiting properties network, we identify nodes strong influence, which are potential targets for Deep Brain Stimulation (DBS). Simulating volume tissue activated, confirm that proposed reported optimal (sweet spots) be beneficial improvement motor symptoms. Furthermore, based on a modularity algorithm, network communities detected set with high-interconnectivity. This allows localise neural activity, directly topology. For this purpose, build large scale model consists following elements basal ganglia network: subthalamic nucleus (STN), globus pallidus (external internal parts) (GPe-GPi), extended striatum, thalamus cortex (MC) areas, integrating multimodal imaging data. We analyse dynamics under Healthy, Parkinsonian DBS conditions aim treatment. The define new partition (or segregation) brain, characterising treatment conditions.