NORMAL MR NEUROMORPHOMETRY BY GLOBAL PATTERN MATCHING

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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. Small neuroanatomical differences 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. 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. 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 m orphometric 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. 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 being developed in aims 2,3 and 5 of TRD4. In this way, selected morphometric features from groups of normal individuals will be compared. By assessing the performance of these methods in the 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. Progress: Working with Dr. Pat Barta we have now implemented the algorithms for automated segmentation of superior temporal areas allowing for the study of normal variation of anatomy as specified in the Vannier grant.