# Attractor dynamics of a whole-cortex network model predicts emergence and structure of fMRI co-activation patterns in the mouse brain

## TL;DR

The study shows that recurring brain activity patterns in mice can be explained by attractor dynamics in a computational model of cortical connectivity.

## Contribution

The novel contribution is linking attractor dynamics in a whole-cortex model to empirical fMRI co-activation patterns using directed anatomical connectivity data.

## Key findings

- Model attractors recapitulate the organization of empirical fMRI co-activation patterns (CAPs).
- Neglecting fiber directionality reduces the model's ability to explain CAPs.
- Inter-hemispheric connectivity strength affects the number and features of model attractors.

## Abstract

Resting state fMRI signals in mammals exhibit rich dynamics on a fast, frame-by-frame timescale of seconds, including the robust emergence of recurring fMRI co-activation patterns (CAPs). To understand how such dynamics emerges from the underlying anatomical cortico-cortical connectivity, we developed a whole-cortex model of resting-state fMRI signals in the mouse. Our model implemented neural input-output nonlinearities and excitatory-inhibitory interactions within cortical regions, as well as directed anatomical connectivity between regions inferred from the Allen mouse brain atlas. We found that, even if the model parameters were fitted to explain static properties of fMRI signals on the timescale of minutes, the model generated rich frame-by-frame attractor dynamics, with multiple stationary and oscillatory attractors. Guided by these theoretical predictions, we found that empirical mouse fMRI time series exhibited analogous signatures of attractor dynamics, and that model attractors recapitulated the topographical organization of empirical fMRI CAPs. The model established key relationships between attractor dynamics, CAPs and features of the directed cortico-cortical intra- and inter-hemispheric anatomical connectivity. Specifically, we found that neglecting fiber directionality severely affected the number of model’s attractors and their ability to explain CAPs. Furthermore, modifying inter-hemispheric anatomical connectivity strength by decreasing or increasing it from the value of real mouse anatomical data, resulted in fewer attractors generated by cortico-cortical interactions and reduced non-homotopic features of the attractors generated by the model, which were important for better predicting empirical CAPs. These results offer novel theoretical insight into the dynamic organization of resting state fMRI in the mouse brain and suggest that the frame-wise BOLD activity captured by CAPs reflects an emerging property of cortical dynamics resulting from directed cortico-cortical interactions.

Whole-brain activity at rest transitions on a timescale of seconds between stereotyped co-activation patterns (CAPs), each with a distinct spatial profile of co-activation across different brain regions. CAPs have been robustly reported across datasets and mammalian species, including humans and mice. However, the significance and origin of these patterns remain unknown. Here we studied the origin of CAPs using a computational model of the whole cortex based on real-world directed measurements of mouse cortical anatomical connectivity. We found that we could explain the formation and topography of CAPs in terms of attractors (that is, states the network tends to converge to) that reflect the information in the anatomical connections between cortical regions. Attractors and CAPs are significantly influenced by the directionality of connectivity (available in mice but not in humans) and the strength of inter-hemispheric coupling. Together, our findings suggest an additional possible mechanism of CAP generation based on cortico-cortical connectivity which adds to current explanations based on contributions from arousing modulatory nuclei. We thus suggest that CAPs may at least in part emerge from cortico-cortical interactions.