A Multiscale, Systems-Level, Neuropharmacological Model of Cortico-Basal Ganglia System for Arm Reaching Under Normal, Parkinsonian, and Levodopa Medication Conditions

Nair, Sandeep Sathyanandan; Muddapu, Vignayanandam Ravindernath; Chakravarthy, V. Srinivasa

doi:10.3389/fncom.2021.756881

Cited by 5 publications

(6 citation statements)

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“…From the wide range of literature that we discussed in relation to the role of BG in WM tasks we could infer that there is a lot of scope in exploring the working memory properties of BG and the related impairments caused due to PD. Computational and mathematical studies have explored dopamine's influence and role of BG in WM functions (Balasubramani et al, 2015;Frank et al, 2001;Gruber & von Cramon, 2003;Nair et al, 2022;O'Reilly & Frank, 2006). There are also modelling studies representing the circuitry at more abstract levels and more detailed levels also Brunel & Wang, 2001;Dreher & Grafman, 2002).…”

Section: Discussionmentioning

confidence: 99%

“…Many studies demonstrate WM deficits in animal models with loss of dopamine neurons and report improvement in performance after delivery of dopamine agonist drugs (Miyoshi et al, 2002;Rusu et al, 2013). In Parkinson's disease (PD), depletion of dopaminergic cells in Substantia Nigra pars Compacta (SNc) affects the patient in different motor modalities including tremor, rigidity, and bradykinesia in arm movements, micrographia in handwriting generation and, dysarthria in speech (Fekete & Jankovic, 2011;Magdoom et al, 2011;Mandali et al, 2016;Nair et al, 2022;Oliveira et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

“…Computational and mathematical models at various levels of abstraction from detailed, biophysical single neuron to abstract networks of neuron-like units have been proposed to understand the underlying mechanism of BG and dopamine's influence on WM (Balasubramani et al, 2015;Frank et al, 2001;Gruber et al, 2006;Nair et al, 2022;O'Reilly & Frank, 2006). Using a conductance-based neuronal network model of prefrontal cortex, (Durstewitz & Seamans, 2002) showed that dopamine's modulation of the network via D1-receptors, resulted in deepening and widening of the attractor basins of WM states.…”

Section: Introductionmentioning

confidence: 99%

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A Basal Ganglia Model for understanding Working Memory Functions in Healthy and Parkinson’s Conditions

Vigneswaran

Nair

Chakravarthy

2023

Preprint

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Working memory is considered as the scratchpad to write, read, and process information to perform cognitive tasks. Basal Ganglia (BG) and Prefrontal Cortex are two important parts of the brain that are involved in working memory functions and both the structures receive projections from dopaminergic nuclei. In this modelling study, we specifically focus on modelling the working memory functions of the BG, the working memory deficits in Parkinson's disease conditions, and the impact of dopamine deficiency on different kinds of working memory functions. Though there are many experimental and modelling studies of working memory properties, there is a paucity of models of the BG that provide insights into the contributions of the BG in working memory functions. The proposed model of the BG is a unified model that can explain the working memory functions of the BG over a wide variety of tasks in normal and Parkinson's disease conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Basal Ganglia Model for understanding Working Memory Functions in Healthy and Parkinson’s Conditions

Vigneswaran

Nair

Chakravarthy

2023

Preprint

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“…Our studies show that L-DOPA dosage plays an important role in the progression of the disease. The proposed model will be integrated with a behavioral model of cortico-basal ganglia circuitry (Muralidharan et al, 2018 ; Nair et al, 2022a ) to show the effect of L-DOPA-induced toxicity at the behavioral level and optimize the L-DOPA dosage to achieve maximum effect on the symptoms with a minimal dosage of the drug (Nair et al, 2022b ).…”

Section: Discussionmentioning

confidence: 99%

A Multi-Scale Computational Model of Levodopa-Induced Toxicity in Parkinson's Disease

Muddapu

Vijayakumar²,

Ramakrishnan³

et al. 2022

Front. Neurosci.

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Parkinson's disease (PD) is caused by the progressive loss of dopaminergic cells in substantia nigra pars compacta (SNc). The root cause of this cell loss in PD is still not decisively elucidated. A recent line of thinking has traced the cause of PD neurodegeneration to metabolic deficiency. Levodopa (L-DOPA), a precursor of dopamine, used as a symptom-relieving treatment for PD, leads to positive and negative outcomes. Several researchers inferred that L-DOPA might be harmful to SNc cells due to oxidative stress. The role of L-DOPA in the course of the PD pathogenesis is still debatable. We hypothesize that energy deficiency can lead to L-DOPA-induced toxicity in two ways: by promoting dopamine-induced oxidative stress and by exacerbating excitotoxicity in SNc. We present a systems-level computational model of SNc-striatum, which will help us understand the mechanism behind neurodegeneration postulated above and provide insights into developing disease-modifying therapeutics. It was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During L-DOPA therapy, it was observed that higher L-DOPA dosage results in increased loss of terminals in SNc. It was also observed that co-administration of L-DOPA and glutathione (antioxidant) evades L-DOPA-induced toxicity in SNc neurons. Our proposed model of the SNc-striatum system is the first of its kind, where SNc neurons were modeled at a biophysical level, and striatal neurons were modeled at a spiking level. We show that our proposed model was able to capture L-DOPA-induced toxicity in SNc, caused by energy deficiency.

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“…While this research didn’t focus on the underlying neural mechanisms, parallel research were conducted on neural substrates and neural mechanisms involved in reaching [43], [44], [45]. Soon reinforcement learning-based models were in use [46] and in our study this arm model [46] was integrated into a basal ganglia thalamocortical model consisting of the oscillatory model of STN-GPe network [47] to simulate motor movements at the behavioral level [48], [49]. In order to accommodate the DBS effect on performance, we replace the rate coded model of pallido-subthalamic circuitry with a spiking neuron model and study DBS effects.…”

Section: Introductionmentioning

confidence: 99%

A Computational Model of Deep Brain Stimulation for Parkinson’s Disease Tremor

Nair,

Chakravarthy

2024

Preprint

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Parkinsons Disease (PD) is a progressive neurological disorder that is typically characterized by a range of motor dysfunctions and its impact extends beyond physical abnormalities into emotional well-being and cognitive symptoms. The loss of dopaminergic neurons in the Substantia nigra pars compacta (SNc) leads to an array of dysfunctions in the functioning of Basal Ganglia (BG) circuitry that manifests into PD. While active research is being carried out in finding the root cause of SNc cell deaths, various therapeutic techniques are prevalent to manage the symptoms of PD. The most common approach in managing the symptoms is replenishing the lost dopamine in the form of taking dopaminergic medications such as Levodopa amidst its long-term complications. Another commonly used intervention for PD is deep brain stimulation (DBS), which is a invasive technique where an electrode is surgically inserted into the skull and a high frequency current of appropriate characteristics is delivered to the brain region. DBS is most commonly used when levodopa medication efficacy reduces and also in combination with levodopa medication that will help reducing the required dosage of medication prolonging the therapeutic effect. DBS is also a go to option when motor complications such as dyskinesias emerge as a side effect of medication. Several studies have also reported that though DBS is found to be effective in suppressing severe motor symptoms such as tremor and rigidity, it has adverse effect on cognitive capabilities. Henceforth it is important to understand the exact mechanism of DBS in alleviating the motor symptoms. A computational model of DBS stimulation for motor symptoms will offer great insights in understanding the mechanisms underlying the DBS and in this line in our current study we model a cortico-basal ganglia circuitry of arm reaching where we simulate healthy controls (HC) and PD symptoms as well as the DBS effect on the PD tremor. With DBS current characteristics of 220 pA, 130 Hz and 100 microseconds pulse-width we were able to see maximum therapeutic effect using our model. This model can be extended to accommodate cognitive dynamics in future so as to study the impact of DBS on cognitive symptoms and optimizing the parameters to get optimal performance effect across modalities.

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A Multiscale, Systems-Level, Neuropharmacological Model of Cortico-Basal Ganglia System for Arm Reaching Under Normal, Parkinsonian, and Levodopa Medication Conditions

Cited by 5 publications

References 73 publications

A Basal Ganglia Model for understanding Working Memory Functions in Healthy and Parkinson’s Conditions

A Basal Ganglia Model for understanding Working Memory Functions in Healthy and Parkinson’s Conditions

A Multi-Scale Computational Model of Levodopa-Induced Toxicity in Parkinson's Disease

A Computational Model of Deep Brain Stimulation for Parkinson’s Disease Tremor

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