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The number of neuroimaging data sets publicly available is growing at fastrate. The increase in availability and resolution of neuroimaging data requiresmodern approaches to signal processing for data analysis and resultsvalidation. We introduce the application of sparse multiway decompositionmethods (Caiafa and Cichocki, 2012) to linearized neuroimaging models. We showthat decomposed models are more compact but as accurate as full models and canbe successfully used for fast data analysis. We focus as example on a recentmodel for the evaluation of white matter connectomes (Pestilli et al, 2014). Weshow that the multiway decomposed model achieves accuracy comparable to thefull model, while requiring only a small fraction of the memory and computetime. The approach has implications for a majority of neuroimaging methodsusing linear approximations to measured signals.

Sparse multiway decomposition for analysis and modeling of diffusion imaging and tractography