Predicting the Impact of Electric Field Stimulation in a Detailed Computational Model of Cortical Tissue

TL;DR

This paper develops a detailed computational model of cortical tissue to predict how electric field stimulation affects neuron populations, capturing key electrophysiological responses and variability based on stimulation parameters.

Contribution

The study introduces a novel layered cortical tissue model with explicit neuron morphologies that accurately predicts neural responses to electric stimulation, validated against experimental data.

Findings

Model replicates depolarization levels in pyramidal neurons

Accelerates intrinsic oscillations during stimulation

Shows response variability based on layer depth and dendrite angle

Abstract

Neurostimulation using weak electric fields has generated excitement in recent years due to its potential as a medical intervention. However, study of this stimulation modality has been hampered by inconsistent results and large variability within and between studies. In order to begin addressing this variability we need to properly characterise the impact of the current on the underlying neuron populations. To develop and test a computational model capable of capturing the impact of electric field stimulation on networks of neurons. We construct a cortical tissue model with distinct layers and explicit neuron morphologies. We then apply a model of electrical stimulation and carry out multiple test case simulations. The cortical slice model is compared to experimental literature and shown to capture the main features of the electrophysiological response to stimulation. Namely, the model showed 1) a similar level of depolarisation in individual pyramidal neurons, 2) acceleration of intrinsic oscillations, and 3) retention of the spatial profile of oscillations in different layers. We then apply alternative electric fields to demonstrate how the model can capture differences in neuronal responses to the electric field. We demonstrate that the tissue response is dependent on layer depth, the angle of the apical dendrite relative to the field, and stimulation strength. We present publicly available computational modelling software that predicts the neuron network population response to electric field stimulation.