Scalable Software for Reverse Engineering Neural Circuits from Histology

描述（由申请人提供）：据估计，人脑大约有1000亿个神经元，通过超过10万英里的轴突和1千万亿突触连接（~10^15或2^50连接）连接在一起。作为比较，仅在波士顿市，人类大脑中的突触连接就比世界上所有甜点和海滩中的沙粒还要多（~10^20）。每个大脑中的神经回路被称为连接组，了解它如何工作并使认知、意识或智力成为可能是科学中最基本的问题之一。考虑到这种复杂性，即使是最简单的行为背后的神经回路也不奇怪。直到最近，完全描述这种电路的尝试从未被认真对待过

DESCRIPTION (provided by applicant): 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.