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A model of the mouse cortex with attractor dynamics explains the structure and emergence of rsfMRI co-activation patterns

View ORCID ProfileDiego Fasoli, View ORCID ProfileLudovico Coletta, View ORCID ProfileDaniel Gutierrez-Barragan, View ORCID ProfileAlessandro Gozzi, View ORCID ProfileStefano Panzeri
doi: https://doi.org/10.1101/2022.04.28.489908
Diego Fasoli
1Laboratory of Neural Computation, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
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  • For correspondence: diego.fasoli@neuroinformatics.it s.panzeri@uke.de
Ludovico Coletta
2Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto (TN), Italy
3Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, 38068 Rovereto (TN), Italy
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Daniel Gutierrez-Barragan
3Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, 38068 Rovereto (TN), Italy
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Alessandro Gozzi
3Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, 38068 Rovereto (TN), Italy
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Stefano Panzeri
1Laboratory of Neural Computation, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
4Department of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, D-20251 Hamburg, Germany
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  • For correspondence: diego.fasoli@neuroinformatics.it s.panzeri@uke.de
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Abstract

Neural network models have been instrumental in revealing the foundational principles of whole-brain dynamics. Here we describe a new whole-cortex model of mouse resting-state fMRI (rsfMRI) activity. Our model implements neural input-output nonlinearities and excitatory-inhibitory interactions within areas, as well as a directed connectome obtained with viral tracing to model interareal connections. Our model makes novel predictions about the dynamic organization of rsfMRI activity on a fast scale of seconds, and explains its relationship with the underlying axonal connectivity. Specifically, the simulated rsfMRI activity exhibits rich attractor dynamics, with multiple stationary and oscillatory attractors. Guided by these theoretical predictions, we find that empirical mouse rsfMRI time series exhibit analogous signatures of attractor dynamics, and that model attractors recapitulate the topographical organization and temporal structure of empirical rsfMRI co-activation patterns (CAPs). The richness and complexity of attractor dynamics, as well as its ability to explain CAPs, are lost when the directionality of underlying axonal connectivity is neglected. Finally, complexity of fast dynamics on the scale of seconds was maximal for the values of inter-hemispheric axonal connectivity strength and of inter-areal connectivity sparsity measured in real anatomical mouse data.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 29, 2022.
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A model of the mouse cortex with attractor dynamics explains the structure and emergence of rsfMRI co-activation patterns
Diego Fasoli, Ludovico Coletta, Daniel Gutierrez-Barragan, Alessandro Gozzi, Stefano Panzeri
bioRxiv 2022.04.28.489908; doi: https://doi.org/10.1101/2022.04.28.489908
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A model of the mouse cortex with attractor dynamics explains the structure and emergence of rsfMRI co-activation patterns
Diego Fasoli, Ludovico Coletta, Daniel Gutierrez-Barragan, Alessandro Gozzi, Stefano Panzeri
bioRxiv 2022.04.28.489908; doi: https://doi.org/10.1101/2022.04.28.489908

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