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

Abstract

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