Individualized functional connectivity markers for motor and mood symptoms of Parkinson's disease

Parkinson's disease (PD) is a complex neurological disorder characterized by many motor and non-motor symptoms. While most studies focus on the motor symptoms of the disease, it is important to identify pathophysiological markers that underlie different facets of the disease. In this case-control study, we sought to discover reliable, individualized functional connectivity markers associated with both motor and mood symptoms of PD. In order to obtain precise functional connectivity measurements, we extensively sampled 166 patients with PD and 51 healthy control participants using functional MRI and characterized functional connectomes at the level of each individual participant. Multiple linear regressions were performed to model the relationship between functional connectivity markers and PD symptoms. We found that a model consisting of 44 functional connections predicted both motor (r = 0.21, p = 0.006) and mood symptoms (depression: r = 0.23, p = 0.006; anxiety: r = 0.21, p = 0.006). Two sets of connections contributed differentially to these predictions. Between-network connections, mainly connecting the sensorimotor and visual large-scale functional networks, substantially contributed to the prediction of motor measures, while within-network connections in the insula and sensorimotor network contributed more so to mood prediction. The middle to posterior insula region played a particularly important role in predicting depression and anxiety scores. We successfully replicated and generalized our findings in three independent PD datasets. Taken together, our findings indicate that sensorimotor and visual network markers are indicative of PD brain pathology, and that distinct subsets of markers are associated with motor and mood symptoms of PD.