SIMULATION OF SPONTANEOUS RESTING TREMOR AND BRADYKINESIA IN PARKINSON’S DISEASE USING A BOOLEAN NEURAL NETWORK MODEL OF THE MOTOR SYSTEM

Parkinson’s disease (PD) is a progressive neurodegenerative disorder resulting in the development of bradykinesia and resting tremor due to dopamine depletion. This study aimed to model and mechanistically explain the symptomatology of PD and to identify new potential treatments. This was achieved through a comprehensive neural network model of the motor system using Boolean neurons and a circuital reductionist approach. The network included the basal ganglia, cerebellum, thalamus, motor cortex, spinal circuits and an idealised joint. Execution of motor programs was successfully performed by the idealised joint with the network being able to prioritise parallel motor programs depending on the activation of the motor cortex. Moreover, the symptomatology of the two major subtypes of PD and the therapeutic and adverse effects of pharmacological and surgical treatments were replicated. A major finding of this study was the identification of the network responsible for the spontaneous generation of resting tremor and the combined simulation of resting tremor and bradykinesia under a single circuit (Figure). The key prerequisites required to reproduce this phenomenon are a reduced release of dopamine during voluntary movement and the hyperactivation of the subthalamic nucleus. The latter results in the abnormal activation of the centromedian nucleus, inducing a progressive reduction in acetylcholine and globus pallidus internus activity, resulting in movement disinhibition. During voluntary movement, acetylcholine is increased due to the activation of the motor cortex, causing bradykinesia and superimposition of the resting tremor. This modelling framework allows to explore PD pathophysiology and guiding personalised interventions.