A hierarchical coding-window model of Parkinson's disease

TL;DR

This paper investigates how Parkinson's disease affects neuronal firing patterns in the basal ganglia, revealing a reduction in the temporal range of rate-coded information and proposing a hierarchical coding-window model to explain these changes.

Contribution

It introduces a hierarchical coding-window model of Parkinson's disease based on structure function analysis and a mathematical circuit model, highlighting the dominance of small neighborhood dynamics.

Findings

Reduced temporal range of rate-coded information in GPi neurons of Parkinson's model

Small neighborhood dynamics are more prominent in Parkinson's disease

Mathematical model supports the role of diffusion constant in neural coupling

Abstract

Parkinson's disease is an ongoing challenge to theoretical neuroscience and to medical treatment. During the evolution of the disease, neurodegeneration leads to physiological and anatomical changes that affect the neuronal discharge of the Basal Ganglia to an extent that impairs normal behavioral patterns. To investigate this problem, single Globus Pallidus pars interna (GPi) neurons of the 6-OHDA rat model of Parkinson's disease were extracellularly recorded at different degrees of alertness and compared to non-Parkinson control neurons. A structure function analysis of these data revealed that the temporal range of rate-coded information in GPi was substantially reduced in the Parkinson animal-model, suggesting that a dominance of small neighborhood dynamics could be the hallmark of Parkinson's disease. A mathematical-model of the GPi circuit, where the small neighborhood coupling is expressed in terms of a diffusion constant, corroborates this interpretation.