A computational model‐based analysis of basal ganglia pathway changes in Parkinson’s disease inferred from resting‐state fMRI

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 neurocomputational 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. K E Y W O R D S BOLD correlations, data fitting, firing rate, spiking neuron model How to cite this article: Maith O, Villagrasa Escudero F, Dinkelbach HÜ, et al. A computational model-based analysis of basal ganglia pathway changes in Parkinson's disease inferred from resting-state fMRI.

Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain

“…The spiking network model and its dynamics (including parameters) were taken from a previous publication (Maith et al, 2020) (Figure 1). Eight neuronal populations were included, each with different properties.…”

“…In contrast, Hamker and colleagues proposed a data-driven spiking model of the BG (Baladron et al, 2019;Maith et al, 2020), that is a generic BG model has been fit to the individual subject data by optimizing its parameters such that features of the simulated activity correlated with the same features of the measurements. Recently, Maith et al (2020) fitted this BG model for 20 PD patients after DBS implantation and 15 healthy controls with individual resting-state functional magnetic resonance imaging (fMRI) data. However, the whole cortex was so far modeled as a single spiking network node, lacking a whole-brain perspective.…”

“…Network models in ANNarchy are defined through equations written in "natural language". ANNarchy has been used to implement models of the BG pathways (Baladron et al, 2019;Gönner et al, 2020;Maith et al, 2020;Villagrasa et al, 2018), spatial attention and vision (Bergelt & Hamker, 2019;Jamalian et al, 2017;Larisch et al, 2020) and learning and memory (Gönner et al, 2017;J. Müller et al, 2018;Schmid et al, 2019).…”

“…In this study, we combine the detailed spiking network model by Maith et al (2020) for the BG with mean-field simulations in TVB for all cortical regions. We interface the spiking network software ANNarchy with TVB to build the TVB-ANNarchy co-simulation framework (Schirner et al, 2021).…”

“…We interface the spiking network software ANNarchy with TVB to build the TVB-ANNarchy co-simulation framework (Schirner et al, 2021). As an underlying connection between BG and cortical regions, we utilize a recently published normative connectivity atlas of these tracts (Petersen et al, 2019) and combine it with individually -that is subject-specific -fitted probabilities and weights from Maith et al (2020) for the connections among the BG regions. As a first proof of concept, we simulate resting-state conditions for an exemplary control and PD patient network and perform virtual DBS targeting STN and GPi in the patient network.…”