Magnetic resonance imaging (MRI) is the most widely used diagnostic imaging tool for detecting neurodegenerative disorders such as Parkinson's Disease. This project will develop new automated methods for detecting subtle effects that can be revealed by MRI, including changes in water diffusional properties of human brain tissue, and functional brain activity. To assess the deviation from the normal brains, a computationally efficient algorithm will be developed to construct a population-specific brain structural template from a normal brain population. Further, a new algorithm will be developed to facilitate the detection of Parkinson's using diffusion MRI data. Finally, novel algorithms for establishing the correlation between the information derived from diffusion and functional MRI data will be developed, enabling prediction of functional activity given the anatomical information and vice-versa. Inferring such a correlation will make it possible to predict functional changes due to changes in tissue microstructure caused by neurodegenerative disorders and vice-versa.

In summary, the precise project goals are: (i) To develop a computationally efficient template brain map construction algorithm for features derived from diffusion MRI. In this context, the ensemble average propagator (EAP), which captures both orientation and shape information of the diffusion process at each voxel in the diffusion MRI data, is proposed. Validation of the constructed template will be performed using standard evaluation metrics for template-based segmentation. (ii) To develop novel methods to automatically discriminate between control and Parkinson's groups using the EAP fields as well as Cauchy deformation tensors (that capture the changes in EAP fields). Validation of the classifier will be achieved using the standard leave-k-out strategy. (iii) To develop a novel algorithm for kernel-based nonlinear regression between EAP fields derived from diffusion MRI and scalar-valued fields derived from functional MRI activation maps. The algorithm will be able to predict the level of activation given the EAP fields and vice-versa. These predictions will be validated using a priori labeled data sets. Predicting functional responses from structural information and vice-versa will significantly impact treatment planning of patients with Parkinson's Disease and other neurodegenerative disorders. The multidisciplinary nature of this project will provide the opportunity to collectively train graduate students from diverse backgrounds in the STEM related fields of this project.

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University of Florida
United States
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