Prasad, J. A. | Search bioRxiv

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↗

bioRxiv2018

Cytoarchitecture and Layer Estimation in High-Resolution Neuroanatomical Images

Robust methods for characterizing the cellular architecture (cytoarchitecture) of the brain are needed to differentiate brain areas, identify neurological diseases, and model architectural differences across species. Current methods for mapping the cytoarchitecture and, in particular, identifying laminar (layer) divisions in tissue samples require the expertise of trained neuroanatomists to manually annotate the various regions-of-interest and cells within an image. However, as neuroanatomical datasets grow in volume, manual annotations become inefficient, impractical, and risk the potential of biasing results. In this paper, we propose an automated framework for cellular detection and density estimation that enables the detection of laminar divisions within retinal and neocortical histology datasets. Our approach for layer detection uses total variation minimization to find a small number of change points in the density that signify the beginning and end of each layer. We apply these methods to micron-scale histology images from a variety of cortical areas of the mouse brain and retina, as well as synthetic datasets. Our results demonstrate the feasibility of using automation to reveal the cytoarchitecture of neurological samples in high-resolution images.

neuroscience↗