Data-driven computational modelling for some of the implications of dopamine in the brain : From subcellular signalling to area networks

Alexandre Foncelle 1, 2
2 BEAGLE - Artificial Evolution and Computational Biology
LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558, Inria Grenoble - Rhône-Alpes, LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In the brain, the high connectivity level makes it difficult to set up experiments with an appropriate level of control. To address that issue, mathematical models are used to represent the brain in a more comprehensive way. Easier than experiments to test hypotheses, mathematical models can extend them closer to reality and aim to extract the studied principle essence, by simplifying it. Computational modelling is a specific branch of mathematical modelling allowing to solve large numerical calculations. In this thesis, I used computational modelling to study brain parts through different approaches, all in collaboration with neurobiologists and applied to experimental data. A common framework is given by the goal of contributing to a picture of the action of the neuromodulator dopamine. I studied the diversity of dopamine's action at three different scales: the brain region, the cellular level and the molecular level. Dopamine has a large impact on the brain and it is mainly known for its rewarding dimension, it is, indeed, the molecule associated with reward prediction and punishment. Few regions in the brain produce dopamine and these regions are impaired in Parkinson's disease or disrupted in major depressive disorders. Concerning Parkinson's disease, I designed a firing-rate model to fit experimental basal ganglia neural activity, which disclosed significant changes of the neural response between control and Parkinsonian condition. Furthermore, with a Hodgkin-Huxley model accounting for the dynamics of the potassium ion, I could support the hypothesis that the brain region called lateral habenula hyper-activates and induces major depressive disorders because of unbalanced potassium concentration due to astrocyte dysfunction (Kir4.1 channels overexpression). Dopamine is also involved in synaptic plasticity, a phenomenon at the basis of memory that I explored with a third model accounting for several experimental results pertaining to spike-timing-dependent plasticity and its modulation.
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Alexandre Foncelle. Data-driven computational modelling for some of the implications of dopamine in the brain : From subcellular signalling to area networks. Quantitative Methods [q-bio.QM]. Université de Lyon, 2018. English. ⟨NNT : 2018LYSEI028⟩. ⟨tel-02090797⟩

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