A human brain is estimated to have roughly 100 billion neurons connected through more than 100 thousand miles of axons and a quadrillion of synaptic connections (~10^15 or 2^50 connections). As a comparison, there are more synaptic connections in human brains in the city of Boston alone than grains of sand in all the desserts and beaches in the world (~10^20). The neural circuit within each brain is called its connectome, and understanding how it works and enables cognition, consciousness, or intelligence is one of the most fundamental questions in science. Given this complexity it is not surprising that the neural circuits underlying even the simplest of behaviors are not understood. Until recently, attempts to fully describe such circuits were never even seriously entertained, as it was considered too numerically complex. However, modern advances in the preparation, sectioning, and imaging of brain tissue in the last five years have enabled biologists to image neural connectivity at scales of only a few nanometers in a highly automated manner. Neuroscience researchers are using confocal and electron microscopy techniques to image serial sections at high resolution. Current three-dimensional image datasets are up to several terabytes in size. With automation and faster imaging, we expect dataset sizes to increase by orders of magnitude. Unfortunately, processing and analyzing these images in order to identify the connectome of any mammalian brain is still an incredibly difficult task, and only a few groups across the world have started to address this problem. We propose to develop the computational infrastructure necessary for mapping the wiring of neurons in a large volume of neural tissue that has been cut into ultrathin serial sections. We will develop an open- source system that supports analysis of arbitrarily large image volumes. By being able to trace every neural process in a volume within a reasonable amount of time (days or weeks instead of years), our system will enable a collaborative effort to develop efficient automatic methods for segmentation and tracing. The proposed system will support remote data access so that the enormous datasets can be accessed simultaneously by geographically diverse research groups. Custom clients will be developed to implement various segmentation algorithms, with results uploaded to a central database. In this way the segmentation results obtained with one algorithm can be compared against those obtained with another algorithm on the same datasets. We will also implement fusion methods that will take as input the segmentation results from different algorithms and that will generate the tracings of neural processes by linking segmentation results from one section to the next.

Public Health Relevance

Connectome scientists are using ultra-thin serial sections and nanometer resolution electron microscopes to reverse engineer neural circuits of the brain. In the not too distant future we will be able to compare neural activity with their circuit diagrams o understand how higher level cognitive tasks such as learning, memory, association and inductive reasoning are implemented in the mammalian brain. We propose to create a scalable open-source software system that will help biologists trace neurons through massive 50-terabyte connectome datasets.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
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Special Emphasis Panel (ZRG1-ETTN-G (13))
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Grabb, Margaret C
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Kitware, Inc.
Clifton Park
United States
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Kaynig, Verena; Vazquez-Reina, Amelio; Knowles-Barley, Seymour et al. (2015) Large-scale automatic reconstruction of neuronal processes from electron microscopy images. Med Image Anal 22:77-88
Lichtman, Jeff W; Pfister, Hanspeter; Shavit, Nir (2014) The big data challenges of connectomics. Nat Neurosci 17:1448-54