This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The development of high dimensional brain mapping within the field of computational anatomy and its integration with other state-of-the-art brain imaging technologies represents a unique opportunity to study the underlying neurobiology of brain structure and its connections, and to determine the relationships between brain structure abnormalities and patterns of cognitive deficits. Such mapping tools permit the formulation of hypotheses concerning brain shape and function determined by patterns of connectivity. Over the past decade many investigators have been studying the shape and structure of the human brain in multiple anatomies and species in common coordinates. Further, emerging methodologies that integrate anatomical and functional information from multiple physical specimens provide an opportunity to ask detailed anatomical questions in a single set of stereotypical coordinates. It is now possible to perform functional measurements and anatomical measurement s at rough ly identical 0.1-1.0 mm resolutons. Systems now exist in isolation of each other for examining gray matter reconstructions of the neocortex, studying the gyrification, folding and sulcal patterning of the inner and outer gray/white and gray/CSF boundaries of the neocortex, as well as the anatomical size and shapes of deep nuclei in the brain such as the hippocampus, thalamus, and other structures. Our own group has been involved in the development of each of these aforementioned tools, including volume mapping tools, cortical and surface generation tools, gyral and sulcal generation and matching tools. As driven by the technical needs of our collaborators, the aims of TRD4 are therefore to develop computational anatomy tools for construction and analysis of cortical surfaces, and gyral and sulcal folds of various brain substructures. The new tools will be automated and made available to our collaborators and others in the scientific community.
Our specific aims are to integrate such anatomical analysis tools into one common Brain Analyzer. Specifically we shall build a system that integrates the following specific aims:
Aim 1 is to construct and validate algorithms for Bayesian segmentation of the neocortex and the construction of two-dimensional surface geometry. We have validated the Bayesian segmentation algorithms for reconstruction of gyral areas Cingulate and Medial Pre-Frontal gyrus. On 10 brains in each we have hand segmented and automatically segmented these brain volumes and computed error rates for the automated algorithms.This is work done by Miller and Ratnanather.
Aim 2 is to construct and validate algorithms for invertible matching of volume and surface representations of coordinate systems associated with brain substructures. We have now generated several whole 3D hippocampus maps using the large deformation geodesic matching. This is work done by Faisal Beg as part fo the resource Aim 3 is to construct and validate algorithms for invertible sulcus/gyrus/geodesic curve matching, and automatic generation of sulcus/gyrus/geodesic curves on brain geometries. We have tested geodesic, gyrus and sulcus curve generation on MRI volumes generated in the temporal lobe with KKI collaborators Barta and Pearlson.
Aim 4 is to visualize and co-register white matter tracts generated in TRD3 with cortical surface reconstructions in 3D and flat maps. We have derived the probability laws for DTI data resulting in dynamic programming algorithm for tract tracing in DTI working with Susumu Mori in TRD3 Aim 5 is to coordinate these cortical analysis tools with the deep nucleus landmark and image mapping methods developed for the hippocampus and other deep nuclei and other functional brain maps generated in TRD1.
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