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.

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Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modeled the aforementioned cognitive tasks and show how individual performance can be mapped to model meta-parameters. This model has the potential to serve as a proxy for cognitively impaired conditions, and can be used as a clinical testbench on which therapeutic interventions can be simulated first before delivering to human subjects.

The root cause of Parkinson's disease (PD) is the death of dopaminergic neurons in Substantia Nigra pars compacta (SNc). The exact cause of this cell death is still not known. Loss of SNc cells manifest as the cardinal symptoms of PD, including tremor, rigidity, bradykinesia, and postural imbalance. To investigate the PD condition in detail and understand the link between loss of cells in SNc and PD symptoms, it is important to have an integrated multiscale computational model that can replicate the symptoms at the behavioural level by evoking the key cellular and molecular underlying mechanisms that contribute to the pathology. In line with this objective, we present a multiscale integrated model of cortico-basal ganglia motor circuitry for arm reaching task, incorporating a detailed biophysical model of SNc dopaminergic neuron. Earlier researchers have shown that fluctuations in dopamine (DA) signals are analogous to reward/punishment signals, thereby prompting application of concepts from reinforcement learning (RL) to modelling the basal ganglia system. 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 with the RL-based behavioural model, which simulates the arm reaching task. Our results showed that as SNc cell loss increases, the percentage success rate to reach the target decreases, and average time to reach the target increases. With levodopa (L-DOPA) medication, both the success rate and the average time to reach the target improved significantly. The proposed model also exhibits how differential dopaminergic axonal degeneration in basal ganglia results in various cardinal symptoms of PD as manifest in reaching movements. From the model results, we were able to show the side effects of L-DOPA mediation, such as wearing off and peak dosage dyskinesias. Moreover, from the results, we were able to predict the optimum dosage for varying degrees of cell loss and L-DOPA medication. The proposed model has a potential clinical application where drug dosage can be optimized as per patient characteristics. We conclude that our model presents a realistic and efficient way of simulating the PD pathology conditions and the effect of levodopa medication, thereby giving a reliable indicator towards the optimization of the drug dosage.

Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modelled the aforementioned cognitive tasks and show how individual performance can be mapped to model meta-parameters. This model has the potential to serve as a proxy for cognitively impaired conditions, and can be used as a clinical testbench on which therapeutic interventions can be simulated first before delivering to human subjects.