Biology subjects

Balwani, A. H.

Publications and source records attributed to Balwani, A. H..

3 recordsLinked to original sources

bioRxiv2020

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

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 models 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.

neuroscience↗

bioRxiv2020

A Deep Feature Learning Approach for Mapping the Brains Microarchitecture and Organization

Models of neural architecture and organization are critical for the study of disease, aging, and development. Unfortunately, automating the process of building maps of microarchitectural differences both within and across brains still remains a challenge. In this paper, we present a way to build data-driven representations of brain structure using deep learning. With this model we can build meaningful representations of brain structure within an area, learn how different areas are related to one another anatomically, and use this model to discover new regions of interest within a sample that share similar characteristics in terms of their anatomical composition. We start by training a deep convolutional neural network to predict the brain area that it is in, using only small snapshots of its immediate surroundings. By requiring that the network learn to discriminate brain areas from these local views, it learns a rich representation of the underlying anatomical features that allow it to distinguish different brain areas. Once we have the trained network, we open up the black box, extract features from its last hidden layer, and then factorize them. After forming a low-dimensional factorization of the networks representations, we find that the learned factors and their embeddings can be used to further resolve biologically meaningful subdivisions within brain regions (e.g., laminar divisions and barrels in somatosensory cortex). These findings speak to the potential use of neural networks to learn meaningful features for modeling neural architecture, and discovering new patterns in brain anatomy directly from images.

neuroscience↗

bioRxiv2020

A three-dimensional thalamocortical dataset for characterizing brain heterogeneity

Neural cytoarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures. In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure.

neuroscience↗