Abstract:
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By combining various types of neuroimaging data, multimodal imaging analyses enable us to study the relationship between brain structure and function, and investigate the connectivity disruption pathways that characterize certain brain diseases. We develop a novel measure, sSC, to quantify the strength of structural connectivity (SC) underlying functional networks identified using data-driven methods such as independent component analysis (ICA). The sSC statistic can be defined on both the voxel- or region-level using diffusion tensor tractography. We provide a framework to conduct statistical inference for sSC, which overcomes many computational challenges due to spatial correlations within the data and the estimation of a large variance-covariance matrix. Simulation studies are conducted to evaluate the performance of the proposed sSC statistic. We also illustrate the method using an fMRI dataset.
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