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Small neuroanatomical difference in the normal brain due to growth and development, aging, sexual dimorphism, laterality, and handedness are observable by MRI but require precise and accurate methods for their detection. We propose to automate the search for neuromorphological differences in brain substructures by a new mathematically robust method with unprecedented sensitivity to small local and regional shape differences. Modern MR brain imaging methods provide detailed in vivo information regarding the anatomical structure of individual brains. However, interpretation of these anatomic data has been hindered by the inability to expeditiously quantify morphological differences across individuals. The difficulty lies in two areas. First, images between different individuals must be in a common reference frame, but they are not collected in this fashion. Second, even when registered, normal variation makes comparisons difficult if not impossible. We propose to further develop algorithmic tools for fusion of anatomical data, as measured via whole brain MRI, so normal control populations can be described and compared.

The major focus of this proposal is the development of mathematical representations of neuroanatomical variation of the brain and specific subregions, especially in the hippocampus and temporal lobe. This involves mapping of a single morphometric atlas to multiple individual target MR image volumes. An atlas is a multivalued spatial array that contains signal values (corresponding to CT, MR and color cryosection images) with their symbolic labels (tissue type, anatomic nomenclature) for all subvolumes of medical and biological significance to the investigation. The subvolumes include specific sulci and various of the temporal lobe (hippocampus, parahippocampal region and temporal gyri). These representations provide a unique tool for the algorithmic generation of smooth maps from an atlas (template) and its subvolumes onto families of target anatomies. In this way, selected morphometric features from groups of normal individuals will be compared.

By assessing the performance of these methods in athe characterization of normal populations, we will be able to definitively test hypotheses regarding brain substructure changes in abnormal populations that cannot be resolved with present methods. This work will provide a direct morphometric tool for measuring small effects of disease processes. The deformable brain atlas will reflect both the underlying stability or predefined anatomically labeled regions, and t heir covariation relative to a single biologically meaningful coordinate system in sets of normals. We will test the method's ability to identify hypothesized intergroup differences in the size and shape of brain structures. This new mapping tool will be shown to locate the quantify individual and population brain structural differences due to normal variation, gender, handedness, and laterality.
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