Meeting Banner
Abstract #1723

Shape Invariant Modelling of Trial Based FMRI Data

Kristoffer Hougaard Madsen1,2, Lars Kai Hansen2, Morten Mrup2

1Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Hvidovre, Greater Copenhagen, Denmark; 2DTU Informatics, Technical University of Denmark, Kgs. Lyngby, Greater Copenhagen, Denmark

To overcome poor signal-to-noise ratios in fMRI, data sets are often acquired over repeated trials that form a three-way array of space time trials. As fMRI data contain multiple inter-mixed signal components blind signal separation and decomposition methods are frequently invoked for exploratory analysis and as a preprocessing step for signal detection. Here we extend multi-linear decomposition to account for general temporal modelling within a convolutional representation. We demonstrate how this alleviates degeneracy and helps to extract physiologically plausible components. The resulting convolutive multi-linear decomposition can model realistic trial variability as demonstrated in fMRI data.