A Computational Neural Network Model Depicting Bradykinesia in Parkinson’s Disease

Abstract

Parkinson's disease (PD) is caused by a deficiency of dopamine (DA) as a result of cell death in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc). Though most computational studies of Parkinson's disease (PD) have concentrated on the effects of dopamine depletion in the basal ganglia, it's crucial to remember that the spinal cord, frontal and parietal cortex, and other areas have considerable dopamine innervation. A network model must be created to investigate how patterns of dopamine depletion across important cellular sites in the spinal cord, cortex, and basal ganglia affect the disruption of spinal cord and neuronal activity in addition to other PD symptoms in order to fully comprehend PD symptoms such as bradykinesia. We integrate dopaminergic innervation of cells in the cortical and spinal components of the basal ganglia-cortico-spinal circuit that governs voluntary arm motions. Many of the main impacts of DA depletion on neuronal, electromyographic (EMG), and movement parameters linked to Parkinson's disease (PD) bradykinesia are effectively replicated in the resultant model.