A Data-Driven Biophysical Computational Model of Parkinson's Disease based on Marmoset Monkeys

TL;DR

This paper introduces a novel data-driven biophysical computational model of Parkinson's Disease based on electrophysiological data from marmoset monkeys, capturing disease-specific neural dynamics and aiding in understanding and therapy development.

Contribution

It presents the first Parkinson's Disease model based on simultaneous recordings from multiple brain regions in marmosets, using biologically constrained parameters optimized with differential evolution.

Findings

Model successfully replicates neural firing rates and spectral signatures.

First to incorporate multi-region electrophysiological data in PD modeling.

Supports exploration of disease mechanisms and therapy development.

Abstract

In this work we propose a new biophysical computational model of brain regions relevant to Parkinson's Disease based on local field potential data collected from the brain of marmoset monkeys. Parkinson's disease is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled single-neuron mean firing rates and spectral signatures of local field potentials from healthy and parkinsonian marmoset brain data. As far as we are concerned, this is the first computational model of Parkinson's Disease based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results show that the proposed model could facilitate the investigation of the mechanisms of PD and support the development of techniques that can indicate new therapies. It could also be applied to other computational neuroscience problems in which biological data could be used to fit multi-scale models of brain circuits.