Computational Modeling of Neurotransmitter Release Evoked by Electrical Stimulation: Nonlinear Approaches to Predicting Stimulation-Evoked Dopamine Release

Abstract

Neurochemical changes evoked by electrical stimulation of the nervous system have been linked to both therapeutic and undesired effects of neuromodulation therapies used to treat obsessive-compulsive disorder, depression, epilepsy, Parkinson’s disease, stroke, hypertension, tinnitus, and many other indications. In fact, interest in better understanding the role of neurochemical signaling in neuromodulation therapies has been a focus of recent government- and industry-sponsored programs whose ultimate goal is to usher in an era of personalized medicine by creating neuromodulation therapies that respond to real-time changes in patient status. A key element to achieving these precision therapeutic interventions is the development of mathematical modeling approaches capable of describing the nonlinear transfer function between neuromodulation parameters and evoked neurochemical changes. Here, we propose two computational modeling frameworks, based on artificial neural networks (ANNs) and Volterra kernels, that can characterize the input/output transfer functions of stimulation-evoked neurochemical release. We evaluate the ability of these modeling frameworks to characterize subject-specific neurochemical kinetics by accurately describing stimulation-evoked dopamine release across rodent (R2 = 0.83 Volterra kernel, R2 = 0.86 ANN), swine (R2 = 0.90 Volterra kernel, R2 = 0.93 ANN), and non-human primate (R2 = 0.98 Volterra kernel, R2 = 0.96 ANN) models of brain stimulation. Ultimately, these models will not only improve understanding of neurochemical signaling in healthy and diseased brains but also facilitate the development of neuromodulation strategies capable of controlling neurochemical release via closed-loop strategies.

Stochastic Simulation of Dopamine Neuromodulation for Implementation of Fluorescent Neurochemical Probes in the Striatal Extracellular Space

“…13,14 We assume a constant probability of release and quantity of release in the simulation, although some temporal and spatial heterogeneities have been reported. 13,31,32 Furthermore, membrane depolarization, which drives neurotransmitter release, is mitigated by voltage-gated sodium channel activity which remain inactive for ∼10 ms following an action potential. 33 Thus, we impose a constraint in our simulation to limit sequential dopamine release events to occur at intervals greater than 10 ms per terminal, for a 100 Hz maximum release rate for any given terminal.…”

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

“…A small body of work exists that aims to better understand the underlying biological and chemical signals that impact DBS performance. Trevathan and colleagues [103] used NN and Volterra kernels to characterize stimulation-evoked neurochemical releases. They compared their proposed frameworks of stimulation-evoked dopamine releases in several animal models.…”

Application of Normalized Compression Distance and Lempel-Ziv Jaccard Distance in Micro-electrode Signal Stream Classification for the Surgical Treatment of Parkinson’s Disease

“…Kuhner et al (2017) used Random Forests with probability distributions to detect abnormal motor behaviour of PD patients performing several different motor tasks in two clinical conditions (DBS switch: off, on). Trevathan et al (2017) Shamir et al (2015) proposed a clinical support decision system to provide effective stimulation and adequate drug dosages, based on three machine learning methods, which included Support Vector Machines, Naïve Bayes, and Random Forest.…”