A model of aberrant learning in Parkinson disease

What is it about?

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by a progressive decline in motor functions, such as bradykinesia, caused by the pathological denervation of nigrostriatal dopaminergic neurons within the basal ganglia (BG). It is acknowledged that dopamine (DA) directly affects the modulatory role of BG towards the cortex. However, a growing body of literature is suggesting that DA induced aberrant synaptic plasticity could play a role in the core symptoms of PD, thus recalling for a “reconceptualization” of the pathophysiology. The aim of this work is to investigate DA driven aberrant learning as a concurrent cause of bradykinesia, using a comprehensive, biologically inspired neurocomputational model of action selection in the BG. The model includes the three main pathways operating in the BG circuitry, i.e., the direct, indirect and hyperdirect pathways, and use a two-term Hebb rule to train synapses in the striatum, based on previous history of rewards and punishments. Levodopa pharmacodynamics is also incorporated. Through model simulations of the Alternate Finger Tapping motor task, we assessed the role of aberrant learning on bradykinesia. The results show that training under drug medication (levodopa), provides not only immediate but also delayed benefit lasting in time. Conversely, if performed in conditions of vanishing levodopa efficacy, training may result in dysfunctional corticostriatal synaptic plasticity, further worsening motor performances in PD subjects. This suggests that bradykinesia may result from the concurrent effects of low DA levels and dysfunctional plasticity, and that training can be exploited in medicated subjects to improve levodopa treatment.

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Why is it important?

Training can significantly affect the behavior of Parkinsonian subjects, depending on whether is performed during ON or OFF Levodopa treatment. A model can significantly improve the calibration of treatment.