Pathological oscillations including elevated beta activity in the subthalamic nucleus (STN) and between STN and cortical areas are a hallmark of neural activity in Parkinson’s disease (PD). Oscillations also play an important role in normal physiological processes and serve distinct functional roles at different points in time. We characterised the effect of dopaminergic medication on oscillatory whole-brain networks in PD in a time-resolved manner by employing a hidden Markov model on combined STN local field potentials and magnetoencephalography (MEG) recordings from 17 PD patients. Dopaminergic medication led to coherence within the medial and orbitofrontal cortex in the delta/theta frequency range. This is in line with known side effects of dopamine treatment such as deteriorated executive functions in PD. In addition, dopamine caused the beta band activity to switch from an STN-mediated motor network to a frontoparietal-mediated one. In contrast, dopamine did not modify local STN–STN coherence in PD. STN–STN synchrony emerged both on and off medication. By providing electrophysiological evidence for the differential effects of dopaminergic medication on the discovered networks, our findings open further avenues for electrical and pharmacological interventions in PD.
The electrophysiological basis of resting state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography (EEG) are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this paper, we compare the two main existing data-driven analysis strategies for extracting resting state networks from MEG data. The first approach extracts RSN through an independent component analysis (ICA) of the Hilbert envelope in different frequency bands. The second approach uses phase-amplitude coupling to determine the RSN. To evaluate the performance of these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract the canonical fMRI RSN with MEG. The approach based on phase-amplitude coupling yielded the best correspondence to the fMRI-RSN. The Hilbert envelope-ICA produced different dominant frequency-bands underlying RSN for different ICA runs, suggesting the absence of a single dominant frequency underlying the RSN. Our results also suggest that individual RSN are not characterized by one single dominant frequency. Instead, the resting state networks seem to be based on a combination of the delta/theta phase and gamma amplitude.
Pathological oscillations are a hallmark of neural activity in Parkinson’s disease (PD). Time-averaged analyses are usually employed to study changes in spectral connectivity with and without dopaminergic intervention in PD. This prevents differentiating the pathological vs physiological nature of dynamically evolving oscillatory activity serving multiple functional roles. Using a Hidden Markov Model on combined STN-LFP and whole-brain MEG data from 17 PD patients we discovered three distinct network activity patterns. One network was related to adverse effects of increased dopamine, a second one maintained ON-medication spatio-spectrally selective cortico-STN connectivity and finally, a local STN-STN network emerged which indicated the inability of L-DOPA to modify local basal ganglia activity. Temporally we found that, ON medication, the cortico-STN and the STN-STN network increased in duration whereas the cortico-cortical network occurred less frequently. Our results provide a spectrally diverse and spatially specific understanding of transient network connectivity in PD on a whole brain level, disambiguating temporal and spatial changes of the underlying networks. By providing electrophysiological evidence for the differential effects of L-DOPA intervention in PD, our findings open further avenues for electrical and pharmacological intervention in PD.
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