As part of the growing field of Computational Anatomy (CA) (Grenander and Miller 1998;Miller 2004), our group has developed tools to identify and characterize brain structural abnormalities in schizophrenia, some of which meet the criteria for disease endophenotypes. In this effort, we have collected high resolution magnetic resonance (MR) datasets from more than 270 subjects using the same MR scanner platform and sequences. Longitudinal MR data are also available on a majority of these subjects. Using CA tools, we have generated surface maps for all of the deep subcortical structures (i.e., hippocampus, amygdala, thalamus, caudate nucleus, nucleus accumbens, putamen and globus pallidus). In addition, smaller datasets of variables related to the volume, thickness and surface area of cortical structures (e.g., the cingulate gyrus) have been generated. Finally, we have constructed manual segmentation datasets for all these structures, which can be used for the validation of new computational methods. If made publically accessible, these high resolution scans and the associated structural data will be invaluable to the neuroscience community in many ways. First, other groups of scientists will be able to use these data to generate or test new hypotheses related to the maldevelopment of brain structures and neural networks in individuals with schizophrenia. Second, scientists in other groups would be able to rapidly replicate findings produced using their own datasets. Third, the data could be used to test and validate new brain mapping tools. Further, the CA pipeline designed for the analysis of these datasets, which consists of landmarking and diffeomorphic mapping tools along with training and validation datasets, will enable others to study other MR datasets collected from other clinical samples.
The specific aims of this application are:
Aim 1. To make available imaging data along with demographic, clinical neurocognitive and genotyping data from 139 subjects with schizophrenia and 136 control subjects group matched for age and gender in a federated database. All data will be appropriately de-identified and anonymized to comply with HIPAA regulations.
Aim 2. To make available neuroanatomical variable datasets, including data on the volumes and surfaces of subcortical structures, and data on the gray matter volumes, thicknesses and surface areas of cortical structures.
Aim 3. To federate software tools and training and validation datasets. A key group of intended users of our distribution is researchers who may have their own imaging data but lack software tools for mapping subcortical brain structures. In that group, our software tools would be needed and used on our training data, therefore enabling these researchers to obtain structural measurements in their images. The software tools will include all that are required by our mapping protocol such as landmarking tools.
We propose to share and federate our schizophrenia research data, including structural magnetic resonance (MR) scans, neuroanatomic data, clinical and cognitive data, all appropriately anonymized, as well as software tools, with the neuroscience community using the infrastructure of the Biomedical Informatics Research Network (BIRN). Other groups of scientists will be able to use these data to generate or test new hypotheses related to abnormalities of brain structures and neural networks in individuals with schizophrenia. They would also be able to rapidly replicate findings produced using their own datasets or test and validate new brain mapping tools. Further, the software tools would enable others to study other MR datasets collected from other clinical samples.
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