Efficient Micro-electrode Recording Modeling using a Filtered Point Process

TL;DR

This paper introduces an efficient filtered point process model of neuronal potentials recorded by deep brain stimulation microelectrodes, accurately matching patient recordings and revealing insights into background activity origins.

Contribution

It presents a computationally efficient model using 10,000 neurons with extracellular filtering that closely replicates real patient recordings, incorporating Weibull-distributed interspike intervals.

Findings

Model accurately matches voltage amplitude distributions.

Weibull distribution with shape 0.8 best fits interspike intervals.

Background activity partly originates from local neuronal activity.

Abstract

In this paper we present an efficient model of the neuronal potentials recorded by a deep brain stimulation microelectrode (DBS MER) in the subthalamic nucleus. It is shown that a computationally efficient filtered point process consisting of 10,000 neurons, including extracellular filtering closely matches recordings from 13 Parkinson's disease patients. The recordings were compared using their voltage amplitude distributions, power spectral density estimates and phase synchrony. It was found that interspike interval times modeled using a Weibull distribution with a shape parameter of 0.8, slightly non-Poisosnian, gave the best fit of the simulations to patient recordings. These results indicate that part of the `background activity' present in an DBS MER can be considered to be a very local field potential due to the surrounding neuronal activity.Therefore, the statistics of the interspike interval times modify the structure of the background activity.