Is There a Better Way to Assess Parkinsonian Motor Symptoms?—Experimental and Modelling Approach

Nair, Sandeep Sathyanandan; Muddapu, Vignayanandam Ravindernath; Sriram, M. S.; Aditya, R.; Gupta, Vinay; Chakravarthy, Srinivasa

doi:10.1007/978-981-16-3056-9_10

Cited by 3 publications

(3 citation statements)

References 100 publications

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“…Hence this model has the scope to be developed into a test bench for PD. The current diagnostics and treatment methodologies for PD are based on direct observation and therefore suffer from subjectivity (Nair et al, 2022 ) and we believe that our model can be developed further to provide a more quantitative approach to diagnose PD symptoms and optimise therapeutic interventions.…”

Section: Discussionmentioning

confidence: 99%

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

Nair

Muddapu

Chakravarthy

2022

Front. Comput. Neurosci.

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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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Section: Discussionmentioning

confidence: 99%

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

Nair

Muddapu

Chakravarthy

2022

Front. Comput. Neurosci.

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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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“…Currently, there is a debate in Parkinson's disease research whether digital methods can yield more detailed and precise data than traditional assessment methods, especially in detecting invisible cognitive and motor changes [24,25].…”

Section: Implementation Of Time-based Measuresmentioning

confidence: 99%

Classification of Parkinson’s Disease Using Machine Learning with MoCA Response Dynamics

Chudzik,

Przybyszewski

2024

Applied Sciences

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Neurodegenerative diseases (NDs), including Parkinson’s and Alzheimer’s disease, pose a significant challenge to global health, and early detection tools are crucial for effective intervention. The adaptation of online screening forms and machine learning methods can lead to better and wider diagnosis, potentially altering the progression of NDs. Therefore, this study examines the diagnostic efficiency of machine learning models using Montreal Cognitive Assessment test results (MoCA) to classify scores of people with Parkinson’s disease (PD) and healthy subjects. For data analysis, we implemented both rule-based modeling using rough set theory (RST) and classic machine learning (ML) techniques such as logistic regression, support vector machines, and random forests. Importantly, the diagnostic accuracy of the best performing model (RST) increased from 80.0% to 93.4% and diagnostic specificity increased from 57.2% to 93.4% when the MoCA score was combined with temporal metrics such as IRT—instrumental reaction time and TTS—submission time. This highlights that online platforms are able to detect subtle signs of bradykinesia (a hallmark symptom of Parkinson’s disease) and use this as a biomarker to provide more precise and specific diagnosis. Despite the constrained number of participants (15 Parkinson’s disease patients and 16 healthy controls), the results suggest that incorporating time-based metrics into cognitive screening algorithms may significantly improve their diagnostic capabilities. Therefore, these findings recommend the inclusion of temporal dynamics in MoCA assessments, which may potentially improve the early detection of NDs.

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Is There a Better Way to Assess Parkinsonian Motor Symptoms?—Experimental and Modelling Approach

Cited by 3 publications

References 100 publications

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

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

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

Classification of Parkinson’s Disease Using Machine Learning with MoCA Response Dynamics

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