Characterization of pallidocortical motor network in Parkinson's disease through complex network analysis.

Deep brain stimulation (DBS) has been demonstrated by numerous clinical trials to be an advanced therapy for selected patients with Parkinson's disease (PD), while its maximal therapeutic effect is capped by the inadequate understanding of the precise neuronal mechanisms underlying PD. Recordings from multichannel electrodes placed in subcortical and cortical regions of the basal ganglia-thalamocortical (BGTC) motor network during DBS surgical procedures can provide rich physiologic information from accessible network nodes. However, most investigations focus on presumed spatio-spectral points of interest, neither fully utilizing the richness of spatial, spectral and temporal aspects of the multivariate signals nor making discoveries in the context of all possible candidates. In addition, aggregated network-level information has been missed out. We use complex network analysis to characterize functional network characteristics of the pallidocortical subcircuit of the BGTC motor network in PD at rest and with movement. The network matrix was constructed using distinct frequency bands at each anatomic recording site as virtual nodes and spectral connectivity (through phase-amplitude coupling and coherence) as network edges. We confirm the critical roles of beta bands and provide additional evidence on their differential functional roles in the pallidocortical motor network. Moreover, significant changes (p   <  0.05) in network functional segregation and integration between rest and movement conditions are revealed for the first time. More importantly, movement-dependent modulation of these network metrics are significantly correlated with hemibody unified PD rating scales (UPDRS), providing network-level perspectives of the pallidocortical motor network pertaining to PD symptoms (p   <  0.05). Findings in the present study provide network-level understanding of neuronal mechanisms in the pallidocortical motor network underlying PD. It is also highly plausible that the demonstrated approach can be applied in other important subcircuits towards a comprehensive understanding of the BGTC motor network.