Abstract
Background: Parkinson's disease affects many motor processes including speech. Besides drug treatment, deep brain stimulation (DBS) in the subthalamic nucleus (STN) and globus pallidus internus (GPi) has developed as an effective therapy. Goal: We present a neural model that simulates a syllable repetition task and evaluate its performance when varying the level of dopamine in the striatum, and the level of activity reduction in the STN or GPi. Method: The Neural Engineering Framework (NEF) is used to build a model of syllable sequencing through a cortico-basal ganglia-thalamus-cortex circuit. The model is able to simulate a failing substantia nigra pars compacta (SNc), as occurs in Parkinson's patients. We simulate syllable sequencing parameterized by (i) the tonic dopamine level in the striatum and (ii) average neural activity in STN or GPi. Results: With decreased dopamine levels, the model produces syllable sequencing errors in the form of skipping and swapping syllables, repeating the same syllable, breaking and restarting in the middle of a sequence, and cessation ("freezing") of sequences. We also find that reducing (inhibiting) activity in either STN or GPi reduces the occurrence of syllable sequencing errors. Conclusion: The model predicts that inhibiting activity in STN or GPi can reduce syllable sequencing errors in Parkinson's patients. Since DBS also reduces syllable sequencing errors in Parkinson's patients, we therefore suggest that STN or GPi inhibition is one mechanism through which DBS reduces syllable sequencing errors in Parkinson's patients.