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Meeting ID: 953 3761 7165 Passcode: CNS*2020!
Jinseok Eo,
Jiyoung Kang,
Dongmyeong Lee,
Junho Son,
Hae-Jeong ParkMulti-photon calcium imaging (CaI) makes it possible to analyze distributed neural activity in the neuronal level. Most studies using CaI have analyzed the neural system in terms of functional connectivity, i.e., temporal synchrony among nodes, which lacks information on the asymmetric interactions, called effective connectivity. Recent computational modelling techniques have been introduced to infer effective connectivity among neural populations using dynamic causal modelling (DCM) (Jung et al., 2019;Rosch et al., 2018). These studies have used a firing-rate based neural state dynamic model, combined with a calcium ion kinetic model in the neural population level, which is not appropriate to model neural interactions among individual neurons. In the single neuron level, a quadratic gaussian integrate-and-fire neural state model (QGIF) in combination with a calcium kinetic equation was proposed to fit CaI at a single neuron (Rhamati et al., 2016). To make it applicable to exploring interactions among multiple neurons, we extended the previous model of Rhamati et al. to a general circuit with multi-nodes in the DCM framework. We utilized a QGIF model for a single neuronal activity, with conductance based neural connectivity (synaptic conductances are approximated with alpha function), and a CaI state dynamic equation. Bayesian model optimization is applied to find optimal model parameters (e.g., effective connectivity) using a variational expectation maximization scheme implemented in the DCM. We confirmed the reliability of the proposed modeling and model inversion process using simulation experiments. We applied the proposed method to explore effective connectivity among neural cells from the neural activity observed in CaI at the rodent’s barrel cortex during successful and failed whisking. The results suggest the plausibility of the proposed method in the analysis of neural interactions observed in the CaI in the neuron level.
