We developed a computational model of the parkinsonian motor network to investigate multivariable closed-loop control strategies of deep brain stimulation (DBS) for Parkinson’s disease (PD). The motor network model includes a model of the cortical basal ganglia coupled to a model of the motoneuron pool. The cortical basal ganglia model incorporates (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode, while (iv) the motoneuron pool model includes a model of electromyography and (v) force generated due to the activation of motoneurons in the pool. The model simulates periods of elevated beta- and tremor-band activity to facilitate investigation of tremor- and beta-based closed-loop DBS control strategies, modulating DBS amplitude, pulse duration or frequency, using clinically accessible measures of tremor- (based on the measured force signal) and beta-band activity (based on the local field potential).
Model Type: Neuron or other electrically excitable cell
Region(s) or Organism(s): Basal ganglia; Subthalamic Nucleus; Spinal motoneuron; Thalamus
Cell Type(s): Globus pallidus neuron; Subthalamus nucleus projection neuron
Currents: I K; I Sodium; I Calcium; I_AHP; I L high threshold; I T low threshold
Model Concept(s): Activity Patterns; Deep brain stimulation; Oscillations; Parkinson's
Simulation Environment: NEURON; Python; PyNN
Implementer(s): Fleming, John E
References:
Fleming JE, Senneff S, Lowery MM. (2023). Multivariable closed-loop control of deep brain stimulation for Parkinson's disease. Journal of neural engineering. 20 [PubMed]