Abstract
We are interested in characterizing how brain networks interact and communicate with each other during voluntary movements. We recorded electrical activities from the globus pallidus pars interna (GPi), subthalamic nucleus (STN) and the motor cortex during voluntary wrist movements. Seven patients with dystonia and six patients with Parkinson’s disease underwent bilateral deep brain stimulation (DBS) electrode placement. Local field potentials from the DBS electrodes and scalp EEG from the electrodes placed over the motor cortices were recorded while the patients performed externally triggered and self-initiated movements. The coherence calculated between the motor cortex and STN or GPi was found to be coupled to its power in both the beta and the gamma bands. The association of coherence with power suggests that a coupling in neural activity between the basal ganglia and the motor cortex is required for the execution of voluntary movements. Finally, we propose a mathematical model involving coupled neural oscillators which provides a possible explanation for how inter-regional coupling takes place.
Highlights
A number of studies examined at time-dependent changes[9,21,22,25] but none have looked at the time evolution of coherence in finer detail
The event-related desynchronization (ERD) in alpha-beta activity is observed bilaterally in both the motor cortex as well as in subthalamic nucleus (STN) and globus pallidus interna (GPi), lasting from two seconds before the movement onset to approximately two seconds after movement termination
While the results for coherence in the alpha band were derived from the same data used in the analysis of beta activity, we found no clear relationship in coherence between cortex-STN or cortex-GPi
Summary
A number of studies examined at time-dependent changes[9,21,22,25] but none have looked at the time evolution of coherence in finer detail. To address this gap, we present a study here investigating the time-dependent coherence between basal ganglia and cortex, tracking coherence relative to both movement phase and LFP powers. Our results suggest a complex inter-play between different brain structures. A neural oscillator model is proposed to help understand the mechanism of communication between different networks
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