Abstract
In order to understand the link between substantia nigra pars compacta (SNc) cell loss and Parkinson's disease (PD) symptoms, we developed a multiscale computational model that can replicate the symptoms at the behavioural level by incorporating the key cellular and molecular mechanisms underlying PD pathology. There is a modelling tradition that links dopamine to reward and uses reinforcement learning (RL) concepts to model the basal ganglia. In our model, we replace the abstract representations of reward with the realistic variable of extracellular DA released by a network of SNc cells and incorporate it in the RL-based behavioural model, which simulates the arm reaching task. Our results successfully replicated the impact of SNc cell loss and levodopa (L-DOPA) medication on reaching performance. It also shows the side effects of medication, such as wearing off and peak dosage dyskinesias. The model demonstrates how differential dopaminergic axonal degeneration in basal ganglia results in various cardinal symptoms of PD. It was able to predict the optimum L-DOPA medication dosage for varying degrees of cell loss. The proposed model has a potential clinical application where drug dosage can be optimised as per patient characteristics.
Highlights
Parkinson’s disease is the second most prominent neurodegenerative disease after Alzheimer’s (Gonzalez-Rodriguez et al, 2020; Marino et al, 2020; Muddapu and Chakravarthy, 2021)
In the multiscale cortico-basal ganglia (MCBG) model, Parkinson’s disease (PD) conditions simulated were subdivided into two categories: in PD1, the substantia nigra pars compacta (SNc) cell loss impacts only striatum whereas in PD2, the SNc cell loss impacts both striatum and subthalamic nucleus (STN)
The MCBG model reaches the target in 0.46 ± 0.02 s compared to the CBG model and the experimental subject which reaches the target in 0.56 ± 0.1 s and 0.3432 ± 0.04 s, respectively (Figure 4A, dark blue bar)
Summary
Parkinson’s disease is the second most prominent neurodegenerative disease after Alzheimer’s (Gonzalez-Rodriguez et al, 2020; Marino et al, 2020; Muddapu and Chakravarthy, 2021). The major cause of Parkinson’s disease (PD) is the death of dopaminergic neurons in substantia nigra pars compacta (SNc) (Michel et al, 2016; Surmeier, 2018; Muddapu et al, 2020a). Dopamine (DA) deficiency due to SNc cell loss manifest as the cardinal PD symptoms that include tremor, Multiscale Cortico-Basal Ganglia Model rigidity, bradykinesia, and postural imbalance (Bereczki, 2010; Poewe et al, 2017; Balestrino and Schapira, 2020). It is important to have a multi-scale model that spans molecular mechanisms to behavioural outputs. With this motivation in mind, we present a computational model that relates DA deficiency in PD to motor symptoms in ON and OFF conditions of medication. As an example of drug action, we simulate the effect of levodopa (L-DOPA) drug administration in our model
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