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Abstract:
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Multi-modal magnetic resonance imaging modalities quantify different, yet complimentary, properties of the brain and its activity. When studied jointly, multi-modal imaging data may improve our understanding of the brain. Unfortunately, the vast number of imaging studies evaluate data from each modality separately and do not consider information encoded in the relationships between imaging types. We aim to study the complex relationships between multiple imaging modalities and map how these relationships vary spatially across different anatomical regions of the brain. Given a particular voxel location in the brain, we regress an outcome image modality on the remaining modalities using all voxels in a local neighborhood of the target voxel. Using simulations, we compare the performance of three estimation frameworks that account for the spatial dependence among voxels in a neighborhood: generalized linear models (GEE), linear mixed effects models with varying random effect structures, and weighted least squares. We then apply our framework to a large imaging study of neurodevelopment to study the relationship between local functional connectivity and cerebral blood flow.
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