Wilsenach, J. | bioRxiv Science

bioRxiv2024

Graph models of brain state in deep anaesthesia reveal sink state dynamics of reduced spatiotemporal complexity and integration

Anaesthetisia is an important surgical and explorative tool in the study of consciousness. Much work has been done to connect the deeply anaesthetised condition with decreased complexity. However, anaesthesia-induced unconsciousness is also a dynamic condition in which functional activity and complexity may fluctuate, being perturbed by internal or external (e.g. noxious) stimuli. We use fMRI data from a cohort undergoing deep propofol anaesthesia to investigate resting state dynamics using dynamic brain state models and spatiotemporal network analysis. We focus our analysis on group-level dynamics of brain state temporal complexity, functional activity, connectivity and spatiotemporal modularization in deep anaesthesia and wakefulness. We find that in contrast to dynamics in the wakeful condition, anaesthesia dynamics are dominated by a handful of sink states that act as low-complexity attractors to which subjects repeatedly return. On a subject-level, our analysis provides tentative evidence that these low complexity attractor states appear to depend on subject-specific age and anaesthesia susceptibility factors. Finally, our spatiotemporal analysis, including a novel spatiotemporal clustering of graphs representing hidden Markov models, suggests that dynamic functional organisation in anaesthesia can be characterised by mostly unchanging, isolated regional subnetworks that share some similarities with the brains underlying structural connectivity, as determined from normative tractography data.

neuroscience↗

bioRxiv2023

The feature landscape of visual cortex

Understanding computations in the visual system requires a characterization of the distinct feature preferences of neurons in different visual cortical areas. However, we know little about how feature preferences of neurons within a given area relate to that areas role within the global organization of visual cortex. To address this, we recorded from thousands of neurons across six visual cortical areas in mouse and leveraged generative AI methods combined with closed-loop neuronal recordings to identify each neurons visual feature preference. First, we discovered that the mouses visual system is globally organized to encode features in a manner invariant to the types of image transformations induced by self-motion. Second, we found differences in the visual feature preferences of each area and that these differences generalized across animals. Finally, we observed that a given areas collection of preferred stimuli ( own-stimuli) drive neurons from the same area more effectively through their dynamic range compared to preferred stimuli from other areas ( other-stimuli). As a result, feature preferences of neurons within an area are organized to maximally encode differences among own-stimuli while remaining insensitive to differences among other-stimuli. These results reveal how visual areas work together to efficiently encode information about the external world.

neuroscience↗