  The number of neuroimaging data sets publicly available is growing at fast
rate. The increase in availability and resolution of neuroimaging data requires
modern approaches to signal processing for data analysis and results
validation. We introduce the application of sparse multiway decomposition
methods (Caiafa and Cichocki, 2012) to linearized neuroimaging models. We show
that decomposed models are more compact but as accurate as full models and can
be successfully used for fast data analysis. We focus as example on a recent
model for the evaluation of white matter connectomes (Pestilli et al, 2014). We
show that the multiway decomposed model achieves accuracy comparable to the
full model, while requiring only a small fraction of the memory and compute
time. The approach has implications for a majority of neuroimaging methods
using linear approximations to measured signals.

Quantitative Methods;Numerical Analysis;Methodology