Varying patterns of association between cortical large-scale networks and subthalamic nucleus activity in Parkinson’s Disease

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Abstract The rising prevalence of Parkinson’s disease has created an urgent need for brain activity markers guiding diagnosis and treatment strategies. While abnormal basal ganglia activity is known to synchronise with specific cortical regions, the temporal dynamics and cortical network architecture of this coupling remain unclear. To address this, we analysed simultaneous magnetoencephalography and subthalamic nucleus (STN) local field potential recordings from 27 individuals with Parkinson’s disease, both on and off dopaminergic medication. Using a time-delay embedded Hidden Markov Model, we identified dynamic large-scale cortical networks with distinct STN-cortical coupling. STN-supplementary motor area (SMA) synchrony increased during visits to the sensorimotor network and the posterior default mode network (DMN). The former was further associated with 9.5-23 Hz power and beta bursts in the STN, and the latter with 5-16.5 Hz power in the STN. Dopaminergic medication preferentially reduced STN beta power in networks lacking enhanced STN-SMA synchrony. These findings suggest that large-scale cortical networks show varying patterns of association with STN activity, and that the sensorimotor network and posterior DMN may provide temporal windows into subcortical processing. Such network signatures in non-invasive recordings offer promising candidates for markers of subcortical-cortical activity in Parkinson’s disease and may provide targets for treatment strategies, including closed-loop stimulation.

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