Optimizing deep brain stimulation parameters for balancing dopaminergic neural activities in Parkinson’s disease: a computational approach based on network resonance

Abstract The regulation of balanced dopaminergic neural activities between direct and indirect pathways in the putamen of the striatum plays a crucial role in maintaining daily healthy movements. An imbalanced dopaminergic neural activity in the putamen is known to cause neurological movement disorders such as Parkinson’s disease (PD) due to dopamine depletion. Deep brain stimulation (DBS) is a strong candidate for clinical treatments for drug-resistant PD. However, little is known about how the parameters of electric stimuli can be set systematically to alleviate PD symptoms. We tested the hypothesis that an optimal periodic electric stimulus can be found to compensate for the imbalanced spiking activities between direct and indirect pathways caused by weak subthreshold dopaminergic input from substantia nigra pars compacta (SNc). This stimulation is based on the concept of vibrational resonance (VR) or pulse-frequency-dependent resonance (PFDR), and studied through computer simulations. Sinusoidal or pulsatile electric stimulus was delivered to a pathway from SNc in a striatal neural network model with a population of Izhikevich type neuron models, and a balance index of the spike firing rates in direct and indirect pathways was calculated as a function of sinusoidal or pulse frequency. The results of computer simulations show that an optimal stimulation frequency to balance spiking activities between direct and indirect pathways in the putamen is observed for VR and PFDR. These findings could contribute to the determination of an optimal strategy to find the stimulating parameters for closed-loop DBS control in PD.