A Data-Driven Biophysical Computational Model of Parkinson's Disease Based on Marmoset Monkeys

Author(s)

Ranieri, Caetano M. ^[1] ; Pimentel, Jhielson M. ^[2] ; Romano, Marcelo R. ^{[3, 4]} ; Elias, Leonardo A. ^{[3, 4]} ; Romero, Roseli A. F. ^[1] ; Lones, Michael A. ^[2] ; Araujo, Mariana F. P. ^{[5, 6]} ; Vargas, Patricia A. ^[2] ; Moioli, Renan C. ^[7]

Affiliation

^[1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil

^[2] Heriot Watt Univ, Edinburgh Ctr Robot, Edinburgh EH14 4AS, Midlothian - Scotland

^[3] Univ Estadual Campinas, Ctr Biomed Engn, Neural Engn Res Lab, BR-13083881 Campinas, SP - Brazil

^[4] Univ Estadual Campinas, Sch Elect & Comp Engn, Dept Elect & Biomed Engn, BR-13083852 Campinas, SP - Brazil

^[5] Univ Fed Espirito Santo, Hlth Sci Ctr, Dept Physiol Sci, BR-29047105 Vitoria, ES - Brazil

^[6] Santos Dumont Inst, Edmond & Lily Safra Int Inst Neurosci IINELS, BR-59280000 Macaiba, State Of Rio Gr - Brazil

^[7] Univ Fed Rio Grande do Norte, Digital Metropolis Inst, Bioinformat Multidisciplinary Environm BioME, BR-59078970 Natal, RN - Brazil

• Document type: Journal article
• Source: IEEE ACCESS; v. 9, p. 122548-122567, 2021.
• Web of Science Citations: 0

Abstract

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. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 17/02377-5 - Machine Learning and Applications for Robotics in Smart Environments
• Grantee: Caetano Mazzoni Ranieri
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 18/25902-0 - Machine learning for help unveiling neural correlates of Parkinson's Disease
• Grantee: Caetano Mazzoni Ranieri
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 18/11075-5 - A computer simulation study on the effects of stochastic stimulation on the activity of a brain circuit involved in Parkinson's disease
• Grantee: Marcelo Ramos Romano
• Support Opportunities: Scholarships in Brazil - Master