Changes in sensorimotor network dynamics in resting-state recordings in Parkinson’s Disease

Abstract

AbstractNon-invasive recordings of magnetoencephalography (MEG) have been used for developing biomarkers for neural changes associated with Parkinson’s Disease (PD) but have yielded inconsistent findings. Here, we investigated whether analysing motor cortical activity within the context of large-scale brain network activity provides a more sensitive marker of changes in PD using MEG.We extracted motor cortical beta power and beta bursts from resting-state MEG scans of individuals with PD (N=28) and well-matched healthy controls (N=36). To situate beta bursts in their brain network contexts, we used a time-delay embedded Hidden Markov Model (TDE-HMM) to extract brain network activity and investigated co-occurrence patterns between brain networks and beta bursts.PD was associated with decreased beta power in motor cortex and decreased occurrences of the sensorimotor network, while motor cortical beta-burst dynamics were not changed. By comparing conventional burst and large-scale network occurrences, we observed that motor beta bursts occurred during both sensorimotor network and non-sensorimotor network activations. When using the large-scale network information provided by the TDE-HMM to focus on bursts that were active during sensorimotor network activations, significant decreases in burst dynamics could be observed in individuals with PD.In conclusion, our findings suggest that decreased motor cortical beta power in PD is prominently associated with changes in sensorimotor network dynamics using MEG. Thus, investigating large-scale networks or considering the large-scale network context of motor cortical activations may be crucial for identifying alterations in the sensorimotor network that are prevalent in PD, and might help resolve contradicting findings in the literature.HighlightsSensorimotor network occurrences are decreased in Parkinson’s Disease.Motor cortical beta bursts occur during both sensorimotor network and non-sensorimotor network activations.Focusing on motor beta bursts occurring during sensorimotor network activations enables for better discrimination between controls and individuals with PD.The spatiotemporal details provided by large-scale network analysis may help to overcome discrepancies found in the PD literature.