Higher-Order Block Term Decomposition for Spatially Folded fMRI Data

TL;DR

This paper introduces a higher-order Block Term Decomposition approach to improve the analysis of spatially folded fMRI data, especially under challenging noise and overlap conditions, demonstrating its effectiveness through simulations.

Contribution

It is the first to apply higher-order BTD to fMRI data, enhancing tensor modeling of spatial dimensions for better neuroimaging analysis.

Findings

Higher-order BTD improves modeling accuracy in noisy conditions

Enhanced detection of overlapping brain activation regions

Simulation results confirm the method's effectiveness

Abstract

The growing use of neuroimaging technologies generates a massive amount of biomedical data that exhibit high dimensionality. Tensor-based analysis of brain imaging data has been proved quite effective in exploiting their multiway nature. The advantages of tensorial methods over matrix-based approaches have also been demonstrated in the characterization of functional magnetic resonance imaging (fMRI) data, where the spatial (voxel) dimensions are commonly grouped (unfolded) as a single way/mode of the 3-rd order array, the other two ways corresponding to time and subjects. However, such methods are known to be ineffective in more demanding scenarios, such as the ones with strong noise and/or significant overlapping of activated regions. This paper aims at investigating the possible gains from a better exploitation of the spatial dimension, through a higher- (4 or 5) order tensor modeling of the fMRI signal. In this context, and in order to increase the degrees of freedom of the modeling process, a higher-order Block Term Decomposition (BTD) is applied, for the first time in fMRI analysis. Its effectiveness is demonstrated via extensive simulation results.