Multi-sensory integration in the mouse cortical connectome using a network diffusion model

Abstract

Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and information processing in the brain. In this work, we make steps towards understanding multi-sensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the Asynchronous Linear Threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). By comparing the model results to functional datasets based on Voltage Sensitive Dye (VSD) imaging, we find that in most cases the ALT model predicts the temporal ordering of an activation cascade correctly. Our results on the Mouse Connectivity Atlas from the Allen Institute for Brain Science show that a small number of brain regions are involved in many primary sensory streams – the claustrum and the parietal temporal cortex being at the top of the list. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multi-sensory information from multiple sensory regions, before utilizing that lower-dimensionality representation in higher-level association regions and more complex cognitive tasks.