Abstract
One of the main disabling features of Parkinson's disease (PD) is bradykinesia. Usually, bradykinesia is clinically assessed through the performance of an extremely simple motor task: the finger tapping task. This motor test correlates with the extent of loss of dopaminergic neurons in the substantia nigra, which is responsible for the triggering of Parkinson's disease (PD) and for the subsequent lack of dopamine (DA) typical of the disease. Therefore, this simple task provides useful information on the state and on the progression of PD. The present study aims at quantifying the connection between DA levels and finger tapping performances by means of a biologically inspired neurocomputational model, which investigates in detail the neural circuitry altered in PD and links the observed output (the finger tapping performance) to the different DA levels included in the model.
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