A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys
In this work we propose a new biophysical computational model of brain regions relevant to Parkinson’s Disease (PD) based on local field potential data collected from the brain of marmoset monkeys. PD is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the subs...
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doaj-b1a2833cd6ba44848c98747c5e51c1f52021-09-09T23:00:27ZengIEEEIEEE Access2169-35362021-01-01912254812256710.1109/ACCESS.2021.31086829524925A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset MonkeysCaetano M. Ranieri0https://orcid.org/0000-0001-5680-9085Jhielson M. Pimentel1https://orcid.org/0000-0002-4825-8508Marcelo R. Romano2Leonardo A. Elias3https://orcid.org/0000-0003-4488-3063Roseli A. F. Romero4https://orcid.org/0000-0001-9366-2780Michael A. Lones5https://orcid.org/0000-0002-2745-9896Mariana F. P. Araujo6Patricia A. Vargas7https://orcid.org/0000-0002-3272-2521Renan C. Moioli8https://orcid.org/0000-0001-6036-8358Institute of Mathematical and Computer Sciences, University of Sao Paulo, Sao Carlos, Sao Paulo, BrazilEdinburgh Centre for Robotics, Heriot-Watt University, Scotland, Edinburgh, U.KNeural Engineering Research Laboratory, Centre for Biomedical Engineering, University of Campinas, Campinas, Sao Paulo, BrazilNeural Engineering Research Laboratory, Centre for Biomedical Engineering, University of Campinas, Campinas, Sao Paulo, BrazilInstitute of Mathematical and Computer Sciences, University of Sao Paulo, Sao Carlos, Sao Paulo, BrazilEdinburgh Centre for Robotics, Heriot-Watt University, Scotland, Edinburgh, U.KDepartment of Physiological Sciences, Health Sciences Centre, Federal University of Espirito Santo, Vitoria, Espirito Santo, BrazilEdinburgh Centre for Robotics, Heriot-Watt University, Scotland, Edinburgh, U.KBioinformatics Multidisciplinary Environment (BioME), Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, State of Rio Grande do Norte, BrazilIn this work we propose a new biophysical computational model of brain regions relevant to Parkinson’s Disease (PD) based on local field potential data collected from the brain of marmoset monkeys. PD is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex (BG-T-C) neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled spectral signatures of local field potentials and single-neuron mean firing rates from healthy and parkinsonian marmoset brain data. This is the first computational model of PD based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results indicate that the proposed model may facilitate the investigation of the mechanisms of PD and eventually support the development of new therapies. The DE method could also be applied to other computational neuroscience problems in which biological data is used to fit multi-scale models of brain circuits.https://ieeexplore.ieee.org/document/9524925/Basal gangliabrain modellingcomputational modellingevolutionary computationneural engineeringParkinson’s disease |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Caetano M. Ranieri Jhielson M. Pimentel Marcelo R. Romano Leonardo A. Elias Roseli A. F. Romero Michael A. Lones Mariana F. P. Araujo Patricia A. Vargas Renan C. Moioli |
spellingShingle |
Caetano M. Ranieri Jhielson M. Pimentel Marcelo R. Romano Leonardo A. Elias Roseli A. F. Romero Michael A. Lones Mariana F. P. Araujo Patricia A. Vargas Renan C. Moioli A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys IEEE Access Basal ganglia brain modelling computational modelling evolutionary computation neural engineering Parkinson’s disease |
author_facet |
Caetano M. Ranieri Jhielson M. Pimentel Marcelo R. Romano Leonardo A. Elias Roseli A. F. Romero Michael A. Lones Mariana F. P. Araujo Patricia A. Vargas Renan C. Moioli |
author_sort |
Caetano M. Ranieri |
title |
A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys |
title_short |
A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys |
title_full |
A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys |
title_fullStr |
A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys |
title_full_unstemmed |
A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys |
title_sort |
data-driven biophysical computational model of parkinson’s disease based on marmoset monkeys |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
In this work we propose a new biophysical computational model of brain regions relevant to Parkinson’s Disease (PD) based on local field potential data collected from the brain of marmoset monkeys. PD is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex (BG-T-C) neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled spectral signatures of local field potentials and single-neuron mean firing rates from healthy and parkinsonian marmoset brain data. This is the first computational model of PD based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results indicate that the proposed model may facilitate the investigation of the mechanisms of PD and eventually support the development of new therapies. The DE method could also be applied to other computational neuroscience problems in which biological data is used to fit multi-scale models of brain circuits. |
topic |
Basal ganglia brain modelling computational modelling evolutionary computation neural engineering Parkinson’s disease |
url |
https://ieeexplore.ieee.org/document/9524925/ |
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