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Poster De Conférence Année : 2019

Large scale modeling of the mouse brain dynamics

Lionel Kusch
Viktor Jirsa

Résumé

Modeling the mouse whole-brain dynamics is viable using the paradigm of brain network models based on the structural connectivity of the mouse brain. This approach is implemented in the neuroinformatic platform The Virtual Brain (TVB) [1], where the tracing data from Allen Institute [2] can be combined with a range of neural mass models [3]. Another common strategy is based on spiking neural networks and uses experimental data at the cellular level for the parameters, leading to bottom-up models such as the Blue Brain project. The former are better suited for study from dynamical systems viewpoint, while the complexity of the latter makes them more physiological plausible. In this work we bridge the two approaches, by building a connectome based large-scale brain network model, where each region contains a population or a surface of spiking neurons, thus allowing a direct link to neuroimaging data, while increasing the biological realism. As a first step we use our modeling paradigm to analyze the impact of heterogeneous connectivity on the network synchronisation. For this, we reproduce earlier analysis of FitzHugh-Nagumo neurons on a torus[4], using adaptive exponential integrate and fire neurons. This is a more complex and realistic neuron model [5] and it is implemented with the Nest simulator [6] After this, we analyze the dynamics of the whole-brain model, and we compare the simulated activity with experimental results, with a focus on different metrics of functional connectivity. This allows us to link the results at the brain activity levels to the spiking neural networks, and to validate the model by using functional data. Hence, the new modelling approach allows bridging from neural mass model to spiking neural networks and using the advantages of the macro- and meso-scopic scales. Reference: [1]P. Sanz Leon et al., “The Virtual Brain: a simulator of primate brain network dynamics,” Front. Neuroinform., vol. 7, 2013. [2]S. W. Oh et al., “A mesoscale connectome of the mouse brain,” Nature, vol. 508, no. 7495, pp. 207-214, Apr. 2014. [3]F. Melozzi, M. M. Woodman, V. K. Jirsa, and C. Bernard, “The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics,” eNeuro, vol. 4, no. 3, p. ENEURO.0111-17.2017, May 2017. [4]V. K. Jirsa and R. A. Stefanescu, “Neural Population Modes Capture Biologically Realistic Large Scale Network Dynamics,” Bulletin of Mathematical Biology, vol. 73, no. 2, pp. 325-343, Feb. 2011. [5]J. Touboul and R. Brette, “Dynamics and bifurcations of the adaptive exponential integrate-and-fire model,” Biol Cybern, vol. 99, no. 4-5, p. 319, Nov. 2008. [6]Peyser, Alexander et al. (2017). NEST 2.14.0. Zenodo.
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hal-04379195 , version 1 (08-01-2024)

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Paternité - Pas d'utilisation commerciale

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  • HAL Id : hal-04379195 , version 1

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Lionel Kusch, Spase Petkoski, Viktor Jirsa. Large scale modeling of the mouse brain dynamics. Society for Neuroscience 2019, Oct 2019, Chicago, United States. ⟨hal-04379195⟩
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