Joint Analysis of Neuroimaging Data: High-Dimensional Problems, Spatiotemporal Models and Computation

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Collaborative Research Team Project: 2016-2019

Uncovering the mysteries of the brain, including its function and structure, is a key challenge of modern science. Advances in the speed and accuracy of data acquisition through modern neuroimaging tools such as functional Magnetic Resonance Imaging (fMRI), Diffusion Tensor Imaging (DTI), Electroencephalography (EEG), and Magnetoencephalography (MEG) allow for unprecedented opportunities to understand brain neuroanatomy and function in health and disease. When neuroimaging was a fledgling field, the emphasis of most studies was on the detection of activated regions of the brain based on data collected from experiments involving only a single neuroimaging modality. Recently, researchers have turned their attention to more complicated problems that involve integrating complimentary sources of information, such as those that arise from studies that collect data using multiple neuroimaging modalities simultaneously, or studies that aim to combine brain imaging with genomics.

The development of statistical methods for these problems has fallen seriously behind the technological advances that allow us to collect the data. This project will thus bring together researchers in statistical science, computer science, biomedical engineering, neuroscience and molecular medicine to develop, test, apply, and propagate new methods with emphasis on joint analysis of neuroimaging data. Researchers across several sites will work on three goals: (1) develop sparse projection regression for high-dimensional analysis of combined neuroimaging and genomic (SNP) data; (2) develop computationally feasible solutions for the neuroelectromagnetic inverse problem based on combined MEG and EEG data; (3) investigate the physiological connection between the gastrointestinal tract and the brain by modeling the relationship between neural outcomes and the human intestinal microbiome using metagenomics data.

The team co-leaders are Farouk Nathoo from the University of Victoria and Linglong Kong from the University of Alberta, and collaborators are Peter Kim from the University of Guelph, Christine Lee from McMaster Univeristy, Timothy Johnson from the University of Michigan, Hongtu Zhu from the University of North Carolina Chapel Hill, and researchers at the University of Victoria, the University of Alberta, the University of Toronto, and Oregon State University.

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