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A dynamic gradient architecture generates brain activity states

Jesse A. Brown, Alex J. Lee, Lorenzo Pasquini, William W. Seeley
doi: https://doi.org/10.1101/2020.08.12.248112
Jesse A. Brown
1University of California, San Francisco, Memory and Aging Center, Department of Neurology, San Francisco, CA, USA
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  • For correspondence: jesse.brown@ucsf.edu
Alex J. Lee
1University of California, San Francisco, Memory and Aging Center, Department of Neurology, San Francisco, CA, USA
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Lorenzo Pasquini
1University of California, San Francisco, Memory and Aging Center, Department of Neurology, San Francisco, CA, USA
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William W. Seeley
1University of California, San Francisco, Memory and Aging Center, Department of Neurology, San Francisco, CA, USA
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Abstract

A central goal of systems neuroscience is to determine the functional-anatomical basis of brain-wide activity dynamics. While brain activity patterns appear to be low-dimensional and guided by spatial gradients, the set of gradients remains provisional and their mode of interaction is unclear. Here we applied deep learning-based dimensionality reduction to task-free fMRI images to derive an intrinsic latent space of human brain activity. Each dimension represented a discrete, dynamically fluctuating spatial activity gradient. The principal dimension was a novel unipolar sensory-association gradient underlying the global signal. A small set of gradients appeared to underlie key functional connectomics phenomena. Different task activation patterns were generated by gradients adopting task-specific configurations. Dynamical systems modelling revealed that gradients interact via state-specific coupling parameters, allowing accurate forecasts and simulations of task-specific brain activity. Together, these findings indicate that a small set of dynamic, interacting gradients create the repertoire of possible brain activity states.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 03, 2021.
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A dynamic gradient architecture generates brain activity states
Jesse A. Brown, Alex J. Lee, Lorenzo Pasquini, William W. Seeley
bioRxiv 2020.08.12.248112; doi: https://doi.org/10.1101/2020.08.12.248112
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A dynamic gradient architecture generates brain activity states
Jesse A. Brown, Alex J. Lee, Lorenzo Pasquini, William W. Seeley
bioRxiv 2020.08.12.248112; doi: https://doi.org/10.1101/2020.08.12.248112

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