Enabling Electric Field Model of Microscopically Realistic Brain

Abstract

Across all electrical stimulation (neuromodulation) domains, conventional analysis of cell polarization involves two discrete steps: i) prediction of tissue electric field ignoring presence of cells and; ii) prediction of cell polarization from tissue electric fields. The first step assumes that electric current flow is not distorted by the dense tortuous network of cell structures. The deficiencies of this assumption have long been recognized, but – except for trivial geometries – ignored, because it presented intractable computation hurdles.We leverage recent electron microscopic images of the brain that have made it possible to reconstruct microscopic brain networks over relatively large volumes and a charge-based formulation of boundary element fast multipole method (BEM-FMM) for the first stimulations of realistic neuronal polarization by electrical stimulation that consider field distortions by a cellular network. The dataset under study is a 250×140×90 μm section of the L2/L3 mouse visual cortex with 396 tightly spaced neurite cells and 34 microcapillaries. We report that brain microstructure significantly distorts the primary macroscopic electric field. Although being very local such distortions constructively accumulate along the neuronal arbor and reduce neuronal activating thresholds by 0.55-0.85-fold as compared to conventional theory.Significance statementThis study introduces a novel method for modeling perturbations of impressed electric fields within a microscopically realistic brain volume, with densely populated neuronal cells and blood microcapillaries. It addresses the limitation of present macroscopic-level electromagnetic models in brain stimulation, which predict 2.5–3 times higher activating thresholds than those observed in experiments. Despite the challenge posed by the small size of the brain sample, our model predicted a consistent average threshold reduction factor of 0.85–0.55 when compared to the macroscopic approach. The present study begins to bridge a critical gap in our understanding of neural activation and arguably creates a new standard for future research in neuronal network modeling in brain stimulation and electrophysiology.