Abstract
Deep brain stimulation (DBS) can alleviate the movement disorders like Parkinson’s disease (PD). Indeed, it is known that aberrant beta (13-30 Hz) oscillations and the loss of dopaminergic neurons in the basal ganglia-thalamus (BGTH) and cortex characterize the akinesia symptoms of PD. However, the relevant biophysical mechanism behind this process still remains unclear. Based on the prior striatal inhibitory model, we propose an extended BGTH model incorporating medium spine neurons (MSNs) and fast-spiking interneurons (FSIs) along with the effect of DBS. We are focusing in this paper on an open-loop DBS mode, where the stimulation parameters stay constant independent of variations in the disease state, and modifications of parameters rely mainly on trial and error of medical experts. Additionally, we propose a novel combined model of the cerebellar-basal-ganglia thalamocortical network, MSNs, and FSIs and show new results that indicate that Parkinsonian oscillations in the beta-band frequency range emerge from the dynamics of such a network. Our model predicts that DBS can be used to suppress beta oscillations in globus pallidus pars interna (GPi) neurons. This research will help our better understanding of the changes in the brain activity caused by DBS, providing new insight for studying PD in the future.
Highlights
Deep brain stimulation (DBS) is an effective symptomatic treatment for a range of neurodegenerative disorders such as Parkinson’s disease (PD)
We investigate an improved computational model of the basal ganglia-thalamus (BGTH) network based on the model originally proposed by Rubin and Terman [3] for the effects of DBS on the evolution of PD, incorporating four brain nuclei
In our attempts to address this issue, we have created a striatum network model using medium spine neurons (MSNs) and fast-spiking interneurons (FSIs), which we subsequently integrated into the BGTH model
Summary
Deep brain stimulation (DBS) is an effective symptomatic treatment for a range of neurodegenerative disorders such as Parkinson’s disease (PD). We investigate an improved computational model of the basal ganglia-thalamus (BGTH) network based on the model originally proposed by Rubin and Terman [3] for the effects of DBS on the evolution of PD, incorporating four brain nuclei. The Hodgkin-Huxley (HH) neurons are employed in this model to replicate the four basic nuclei in BGTH containing MSNs and FSIs and to further explore possible scenarios of PD progression and treatment In this approach, four major nuclei (STN, GPe, GPi, and TH) are linked together by excitatory and inhibitory synaptic connections to create the BG network, which reacts to SMC input. The four brain areas targeted by deep brain stimulation (DBS) are shown by solid red arrows
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