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A generative modeling approach for interpreting population-level variability in brain structure

Ran Liu, Cem Subakan, Aishwarya H. Balwani, Jennifer Whitesell, Julie Harris, Sanmi Koyejo, Eva Dyer
doi: https://doi.org/10.1101/2020.06.04.134635
Ran Liu
1School of Electrical & Computer Engineering, Georgia Institute of Technology
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Cem Subakan
2Montreal Institute for Learning Algorithms, University of Montreal
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Aishwarya H. Balwani
1School of Electrical & Computer Engineering, Georgia Institute of Technology
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Jennifer Whitesell
3Neuroanatomy Division, Allen Institute for Brain Science
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Julie Harris
3Neuroanatomy Division, Allen Institute for Brain Science
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Sanmi Koyejo
4Computer Science, University of Illinois at Urbana Champaign
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Eva Dyer
1School of Electrical & Computer Engineering, Georgia Institute of Technology
5Department of Biomedical Engineering, Georgia Institute of Technology
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  • For correspondence: evadyer@gatech.edu
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Abstract

Understanding how neural structure varies across individuals is critical for characterizing the effects of disease, learning, and aging on the brain. However, disentangling the different factors that give rise to individual variability is still an outstanding challenge. In this paper, we introduce a deep generative modeling approach to find different modes of variation across many individuals. To do this, we start by training a variational autoencoder on a collection of auto-fluorescence images from a little over 1,700 mouse brains at 25 micron resolution. To then tap into the learned factors and validate the model’s expressiveness, we developed a novel bi-directional technique to interpret the latent space–by making structured perturbations to both, the high-dimensional inputs of the network, as well as the low-dimensional latent variables in its bottleneck. Our results demonstrate that through coupling generative modeling frameworks with structured perturbations, it is possible to probe the latent space to provide insights into the representations of brain structure formed in deep neural networks.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • http://connectivity.brain-map.org/

Copyright 
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 June 05, 2020.
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A generative modeling approach for interpreting population-level variability in brain structure
Ran Liu, Cem Subakan, Aishwarya H. Balwani, Jennifer Whitesell, Julie Harris, Sanmi Koyejo, Eva Dyer
bioRxiv 2020.06.04.134635; doi: https://doi.org/10.1101/2020.06.04.134635
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A generative modeling approach for interpreting population-level variability in brain structure
Ran Liu, Cem Subakan, Aishwarya H. Balwani, Jennifer Whitesell, Julie Harris, Sanmi Koyejo, Eva Dyer
bioRxiv 2020.06.04.134635; doi: https://doi.org/10.1101/2020.06.04.134635

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