Patients with Parkinson's disease have difficulties with self-initiating a task and maintaining a steady task performance. We hypothesized that these difficulties relate to reorganization in the sensorimotor execution, cingulo-opercular task-set maintenance, and frontoparietal adaptive control networks. We tested this hypothesis using graph theory-based network analysis of a composite network including a total of 86 nodes, derived from the three networks of interest. Resting-state functional magnetic resonance images were collected from 30 patients with Parkinson's disease (age 42-75 years, 11 females; Hoehn and Yahr score 2-3, average 2.4 ± 0.4) in their off-medication state and 30 matched control subjects (age 44-75 years, 10 females). For each node, we calculated strength as a general measure of connectivity, global efficiency and betweenness centrality as measures of functional integration, and clustering coefficient and local efficiency as measures of functional segregation. We found reduced node strength, clustering, and local efficiency in sensorimotor and posterior temporal nodes. There was also reduced node strength and betweenness centrality in the dorsal anterior insula and temporoparietal junction nodes of the cingulo-opercular network. These nodes are involved in integrating multimodal information, specifically related to self-awareness, sense of agency, and ultimately to intact perception of self-in-action. Moreover, we observed significant correlations between global disease severity and averaged graph metrics of the whole network. In addition to the well-known task-related frontostriatal mechanisms, we propose that the resting-state reorganization in the composite network can contribute to problems with self-initiation and task-set maintenance in Parkinson's disease.
Keywords: Anterior insula; Functional magnetic resonance imaging; Graph theory; Neural network; Resting-state; Temporoparietal junction.