Neural dynamics of short and medium-term motor control effects of levodopa therapy in Parkinson's disease

A neural network model of movement control in normal and Parkinson's disease (PD) conditions is proposed to simulate the time-varying dose- response relationship underlying the effects of levodopa on movement amplitude and movement duration in PD patients. Short and long-term dynamics of cell activations and neurotransmitter mechanisms underlying the differential expression of neuropeptide messenger RNA within the basal ganglia striatum are modeled to provide a mechanistic account for the effects of levodopa medication on motor performance (e.g. the pharmacodynamics). Experimental and neural network simulation data suggest that levodopa therapy in Parkinson's disease has differential effects on cell activities, striatal neuropeptides, and motor behavior. In particular, it is shown how dopamine depletion in the striatum may modulate differentially the level of substance P and enkephalin messenger RNA in the direct and indirect basal ganglia pathways. This dissociation in the magnitude and timing of peptide expression causes an imbalance in the opponently organized basal ganglia pathways which results in Parkinsonian motor deficits. The model is validated with experimental data obtained from handwriting movements performed by PD subjects before and after medication intake. The results suggest that fine motor control analysis and network modeling of the effects of dopamine in motor control are useful tools in drug development and in the optimization of pharmacological therapy in PD patients.