Research Project
10370064
Digitized reference brains, also referred to as Common Coordinate Frameworks (CCFs), together with superposed atlas annotations, are of central importance to neuroscience. They bear the same relation to neuroscience as do reference genomes and genome annotations to cellular and molecular biology. Strikingly, however, such reference brains for humans lag far behind the corresponding CCFs for non-human model organisms such as the laboratory mouse. Existing data sets either have sections spaced relatively far apart or lack in-plane resolution down to the micron scale. Crucially, existing data sets are not well connected to the major areas in medicine that deal with the human brain, namely neuroradiology and neuropathology. We will meet this need by creating an unprecedented micron-scale, 3D atlas that combines multiple MRI modalities as well as continuous serial section histology. In particular, the reference atlas will consist of Nissl, Myelin and H&E stains, with 20 micron contiguous serial sections, and approximately ~8000 sections/brain. We will do so using the tape-transfer method, which preserves tissue geometry even in the presence of disconnected pieces to the brain being sectioned, and permits 3D reassembly of the sections into a 3D volume. We will utilize diffeomorphic mapping methods to co-register the MRI and histological data, and will create a human brain CCF in which single-cell transcriptomic and epigenomic data can be pinned in order to create a Human Brain Cell atlas. We will use machine learning approaches to segment cells and processes in these images and to algorithmically detect cytoarchitectonic boundaries; such machine learning methods will also be used to predict histology and cytoarchitecture from MRI data, with our collected data as a training set. We will make our data freely available to scientists as well as medical professionals through an online data portal with a multi-resolution viewer for zooming and panning through terapixel image data, and also deposit the data in a shared data repository to make it easily accessible to other researchers. We will connect our data to a unique on-line neuropathology resource containing over a petabyte of neuropathological images, including H&E stained sections from the coronal plane. We expect that the reference brain data we produce will become the de- facto standard for a high-resolution reference atlas for the human brain.
