In the last decade, MRI studies of human brain morphometry have been used to investigate a multitude of pathologies and drug-related effects in psychiatric research. The morphometric measures that differentiate patient populations or track longitudinal changes are often subtle and require a large number of subjects or repeated studies to detect and statistically model with significance. Cost, patient compliance, risks to the patients, and the rarity of certain diseases often limit traditional, clinical morphometric studies. These complications have motivated the use of model organisms such as of mice and rats. Animal studies are also very popular due to their small size and rapid development cycle, the wealth of genotype and phenotype data, as well as the maturity of the technology to manipulate their genetic information to induce disease. Brain morphometry models of rat and mice typically involve histological slides, behavioral data, genetic testing, and, increasingly, MRI scans. In particular, in addition to brain morphometry, MRI scans are being employed as a hypothesis generation method for focused histological and molecular examinations, and for strain comparisons. Effective methods have been developed for extracting brain morphometry from human MRI scans. We are leaders in the field for their development and their application. We have developed Legrendre polynomial methods for MRI bias correction methods, atlas-based methods for tissue classification, and spherical harmonics techniques for shape parameterization. We have applied these methods to correlate hippocampus shape variations that distinguish patients suffering from schizophrenia. By contrast, few automated quantitative analysis methods exist for small animal MRI. The standard is to manually outline brain features in MRI slices for a large number of animals, and such manual methods lack reproducibility and are extremely time consuming. The lack of automated MRI analysis methods is the limiting factor in many animal studies. We propose to develop automatic, reliable, high-throughput MR image analysis methods for small animal, brain morphometry studies. Additionally, we propose to develop an intuitive web-based interface for collecting and distributing the imaging data of small animal studies as well as initiating the processing of that data on a distributed processing network. The web-based data sharing and processing system also supports the inspection of the ongoing processing and the examination of the computed results. This web-based processing system is generic in nature and can be extended to host and process human MRI data as well as data from other modalities and other applications. To demonstrate and evaluate the data system, we will apply it to the study of the neuroanatomy of a fragile-X syndrome mouse model. This mouse model Is based on a knockout of the FMR1 mouse model, and it has shown behavioral deficits consistent with a Fragile X/autism human phenotype. The proposed software will advance murine MRI studies of morphometry and connectivity for neuro-developmental, and neuro-degenerative psychiatry diseases. The analysis of MR images of entire brain studies will become the matter of a few mouseclicks on a web-interface. ? ? ?
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