(maximum 30 lines) The BRAIN initiative seeks to develop and apply technologies in order to understand of how brain cells interact in both time and space to give rise to brain function. A key deliverable in BRAIN is a systematic census of neuronal and glial cell types, which is a prerequisite to understand how these cells interact and change in healthy aging and in disease. Moreover, such a census will provide a common reference cell taxonomy, which is crucial to harmonize studies at different sites and achieve the goals of BRAIN. A necessary companion of the census is a reference coordinate system, which enables us to understand the spatio-anatomical context in which cells interact, as well as their connectivity. Building such a coordinate system requires advanced spatial alignment (registration) tools, since virtually every lab technique used in microscopic brain cell phenotyping ? particularly in human brain ? requires blocking and/or sectioning of samples, hence distorting the structure of tissue. Due to the difficulty of providing support for datasets and acquisition setups different to the original, most publicly available techniques to recover the lost tissue structure (?3D reconstruction?) rely on very simple techniques, such as vanilla pairwise registration of neighboring sections. Moreover, conventional reconstruction methods are notoriously slow, and no available method is designed to 3D reconstruct whole human brains. In this interdisciplinary project, which lies at the nexus of computer science, MRI physics, histology, optical imaging, anatomy and statistics, we propose to extend, robustify, test, distribute and support our recently developed, state-or-the-art techniques that will enable the constructions of a coordinate system capable of representing multi-scale maps of human brain anatomy and function. This includes algorithms and software for: image analysis of ex vivo MRI; construction of laminar models of the human cerebral cortex; 3D reconstruction of microscopic images and alignment to the laminar models; surface based analysis of microscopy data on the laminar structure; and alignment of ex vivo and in vivo images to accurately transfer information from microscopy to MRI studies of the living brain, in health and in disease. The tools we propose to build and disseminate will combine modern deep learning techniques with principled Bayesian inference, and have the potential to deliver accurate registration at the macroscopic, mesoscopic, and microscopic level, with high throughput delivered using cutting-edge machine learning algorithms. Effective dissemination of these tools, along with companion test data, will be achieved through our widespread package FreeSurfer. The distributed tools will not only enable the construction of a cell census with rich spatial information at human brain scale (including a novel laminar model), but will also have a tremendous impact in other areas of neuroimaging, including overarching goals of BRAIN such as: linking cellular-level activity to functional MRI, atlas building, or connecting axonal anatomy to diffusion MRI.

Public Health Relevance

(maximum 3 sentences) In this project, we seek resources to develop, integrate, distribute and support a set of tools for the automated spatial mapping of in vivo (MRI, PET, etc?) and ex vivo (microscopy) imaging modalities, which can be used to enhance the BRAIN cell census with a coordinate system for representing maps of human brain function and anatomy at multiple scales ? including laminar models of the cerebral cortex that have been long desired by the neuroimaging community. The tools will include algorithms for segmentation of ex vivo MRI; laminar modeling; registration of microscopic images to MRI; and analysis of histological data on spherical coordinate systems of the cortex. These tools will not only enable the scientific community to include spatial information into the cell census, but will also have a dramatic impact on other neuroimaging projects related to the BRAIN initiative in terms of their ability to share and compare data in a unified coordinate system and use the results of BRAIN research to make inferences in studies of living human beings.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Multi-Year Funded Research Project Grant (RF1)
Project #
Application #
Study Section
Special Emphasis Panel (ZMH1)
Program Officer
Zhan, Ming
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Massachusetts General Hospital
United States
Zip Code