CRCNS: Large-scale computational reconstruction of three-dimensional neural

University Of Utah, Salt Lake City UT

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Abstract

DESCRIPTION (provided by applicant): Neurophysiological modeling is an important tool in understanding the human brain, and yet state-of-the-art models are poorly constrained by anatomical data. High-magnification, serial-section microscopy images have the potential to expand the field of neurophysiological modeling by providing ground-truth neuroanatomical data. This project addresses the problem of building three-dimensional (3D) connectivity maps for neurons from serial-section microscopy. Sectional data consists of stacks of very high-resolution, two-dimensional (2D) images that are oriented to capture cross sections of elongated neuronal processes. The work focuses on two driving biological applications. The first application is the development of complete connectivity maps for ganglion cells in the mammalian retina. The second is the study of the organization of axons in the optic tract of wildtype and mutant zebrafish. In both applications, the complexity and vast size of the high-resolution, serial-section data make them impractical for human interpretation. Two areas of research are proposed. The first is basic technology development for serial microscopy data processing. Such data exhibits unique structural and statistical properties (e.g. textures), which present a distinct set of challenges that surpass the state-of-the-art in 2D and 3D image processing. The second area is the development of practical, computational tools with which scientists can quantitatively analyze the very large data sets associated with this problem. In order to maximize its exposure to the research community, the software produced in this project will be made publicly available as part of the open source toolkit ITK (www.itk.org). This research will allow scientists to systematically analyze serial section data sets by developing the necessary algorithms and computational tools for processing and visualization. This will result in an improved understanding how neural circuits are constructed at the level of single cells, which is important in advancing our knowledge of the basic wiring of the human brain. Significantly improved imaging, analysis and visualization resources will be critical in characterizing disease progressions, therapeutic arrests, and regeneration patterns in neurological diseases, such as temporal lobe epilepsy, retinal degenerations, spinal injury, and drug dependence, which are associated with anomalies in large-scale neural connectivity.