PT - JOURNAL ARTICLE AU - Ran Liu AU - Cem Subakan AU - Aishwarya H. Balwani AU - Jennifer Whitesell AU - Julie Harris AU - Sanmi Koyejo AU - Eva Dyer TI - A generative modeling approach for interpreting population-level variability in brain structure AID - 10.1101/2020.06.04.134635 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.06.04.134635 4099 - http://biorxiv.org/content/early/2020/06/05/2020.06.04.134635.short 4100 - http://biorxiv.org/content/early/2020/06/05/2020.06.04.134635.full AB - 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 StatementThe authors have declared no competing interest.