Inference and Visualization of Information Flow in the Visual Pathway using dMRI and EEG

Abstract : We propose a method to visualize information flow in the visual pathway following a visual stimulus. Our method estimates structural connections using diffusion magnetic resonance imaging and functional connections using electroencephalography. First, a Bayesian network which represents the cortical regions of the brain and their connections is built from the structural connections. Next, the functional information is added as evidence into the network and the posterior probability of activation is inferred using a maximum entropy on the mean approach. Finally, projecting these posterior probabilities back onto streamlines generates a visual depiction of pathways used in the network. We first show the effect of noise in a simulated phantom dataset. We then present the results obtained from left and right visual stimuli which show expected information flow traveling from eyes to the lateral geniculate nucleus and to the visual cortex. Information flow visualiza-tion along white matter pathways has potential to explore the brain dynamics in novel ways.
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Samuel Deslauriers-Gauthier, Jean-Marc Lina, Russell Butler, Pierre-Michel Bernier, Kevin Whittingstall, et al.. Inference and Visualization of Information Flow in the Visual Pathway using dMRI and EEG. MICCAI 2017 Medical Image Computing and Computer Assisted Intervention, Sep 2017, Québec, Canada. ⟨hal-01556352⟩

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