  In this work we propose a new biophysical computational model of brain
regions relevant to Parkinson's Disease based on local field potential data
collected from the brain of marmoset monkeys. Parkinson's disease 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 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
single-neuron mean firing rates and spectral signatures of local field
potentials from healthy and parkinsonian marmoset brain data. As far as we are
concerned, this is the first computational model of Parkinson's Disease based
on simultaneous electrophysiological recordings from seven brain regions of
Marmoset monkeys. Results show that the proposed model could facilitate the
investigation of the mechanisms of PD and support the development of techniques
that can indicate new therapies. It could also be applied to other
computational neuroscience problems in which biological data could be used to
fit multi-scale models of brain circuits.
