A computational model‐based analysis of basal ganglia pathway changes in Parkinson's disease inferred from resting‐state fMRI. (3rd July 2020)

• Record Type: Journal Article
• Authors: Maith, Oliver; Villagrasa Escudero, Francesc; Dinkelbach, Helge Ülo; Baladron, Javier; Horn, Andreas; Irmen, Friederike; Kühn, Andrea A.; Hamker, Fred H.
• Is Part Of: European journal of neuroscience. Volume 53:Number 7(2021)
• Page Start: 2278
• Page End: 2295
• Publication Date: 2020-07-03
• Subjects: BOLD correlations -- data fitting -- firing rate -- spiking neuron model; Nervous system -- Periodicals; 612.8
• DOI: 10.1111/ejn.14868
• Languages: English
• ISSNs: 0953-816X

Abstract

Abstract: Previous computational model‐based approaches for understanding the dynamic changes related to Parkinson's disease made particular assumptions about Parkinson's disease‐related activity changes or specified dopamine‐dependent activation or learning rules. Inspired by recent model‐based analysis of resting‐state fMRI, we have taken a data‐driven approach. We fit the free parameters of a spiking neuro‐computational model to match correlations of blood oxygen level‐dependent signals between different basal ganglia nuclei and obtain subject‐specific neuro‐computational models of two subject groups: Parkinson patients and matched controls. When comparing mean firing rates at rest and connectivity strengths between the control and Parkinsonian model groups, several significant differences were found that are consistent with previous experimental observations. We discuss the implications of our approach and compare its results also with the popular "rate model" of the basal ganglia. Our study suggests that a model‐based analysis of imaging data from healthy and Parkinsonian subjects is a promising approach for the future to better understand Parkinson‐related changes in the basal ganglia and corresponding treatments. Abstract : We fit connectivity parameters of a spiking neuro‐computational basal ganglia (BG) model to replicate correlations of rs‐fMRI in Parkinson patients and control subjects and obtained data‐driven models of both groups. Our results (differences in connectivity, firing rates at rest and heterogeneity) show agreements with experimental findings and suggest that a model‐based analysis of imaging data from controls and patients is a promising approach to understand Parkinson induced changes in the BG.