Mathematical/Computational Tools for Mapping Brain Data Bases

点击翻译按钮获取中文摘要

Modern whole brain cryosection and digitization techniques coupled to modern tissue section and staining methods allow anatomists to provide exquisitely detailed in vivo information regarding the anatomical structure and function of individual brains. The difficulty lies in two areas: images and sections within the same anatomy must be registered. But more fundamentally, even when registered, normal variation across disparate anatomes makes pooling of inter anatomical data difficult, if not impossible. In this proposal, we strive towards the development of algorithmic tools for the fusion of anatomical and functional data as measured via gross whole brain cryosections (0.05-0.1 mm resolution) and stained tissue sections in the macaque monkey brain. The major focus of this proposal is on the development of _ as manifest in the adult macaque, allowing for the mapping of a single atlas to multiple individual targets. The atlas contains all subvolumes of biological significance to the investigator in our work these would include the sulci and gyri, deep thalamic nuclei, ventricles and various sub areas in the occipital lobe, including primary visual cortex (area V1) and other visual areas. Our representations provide a tool for the algorithmic generation of smooth maps (maps preserving differential geometric lengths, areas, volumes) from an atlas (template) and its subvolumes to a family of target anatomies. These maps allow for the fusion and pooling of geometric and physiologic information from varied experimental preparations into one common coordinate system or data base. As part of such a representation of normal macaque anatomical variability, a probabilistic measure will be constructed from a family of representative macaque whole brain anatomies. The probability measures normal anatomical variation in act ual brain anatomy. This will provide neuroanatomists with a direct tool for placing their samples into a common coordinate system with a precise degree of certainty or confidence. Such a measure of anatomical variability reflects both the underlying stability of pre-defined anatomically labeled regions, i.e. subareas and volumes such as sulci, eyri, and different visual areas and their covariation relative to a single biologically meaningful coordinate system.