Beyond Tensor Probabilistic Independent Component Analysis -- Putting Block-Term Decomposition and Independent Vector Analysis Together

2026-07-05

AI Summary

Traditional tensor probabilistic independent component analysis (TPICA) exhibits limited robustness in separating spatially highly overlapping fMRI source signals due to its reliance on strict statistical independence assumptions, which fail to capture the within-group correlation structures commonly present in real data. To address this limitation, this work proposes a novel framework—TPIVA—that integrates block term decomposition (BTD) with independent vector analysis (IVA). By jointly exploiting multidimensional tensor structure and subspace-level statistical independence, TPIVA overcomes the constraints of conventional CP decomposition and ICA. The method replaces the collinearity constraints in BTD with statistical independence, yielding a more flexible and expressive generalized model. Experimental results demonstrate that TPIVA significantly enhances source separation performance in fMRI data, providing preliminary validation of its theoretical advantages and feasibility for handling spatially overlapping sources.

Abstract

Tensor probabilistic independent component analysis (TPICA) is a popular approach to analyzing functional magnetic resonance imaging (fMRI) data, which draws its popularity from its ability to enrich the advantages of the statistics-based ICA with the awareness of the multi-way nature of these data, brought about and exploited via a deterministic 3-way (time $\times$ space $\times$ subjects) tensor decomposition (Canonical Polyadic Decomposition (CPD)) model. It has, however, received critique concerning its robustness in realistic fMRI unmixing scenarios, notably those involving sources that are strongly overlapped in space. Such cases may not meet the assumption of statistical independence required in ICA. They can instead be better described as independent vectors (or subspaces) of dependent components, pointing to the adoption of alternative statistical approaches, notably independent vector analysis (IVA). On the other hand, on the deterministic side, CPD is often restrictive and is outperformed by the more flexible block-term decomposition (BTD) model, also in the fMRI source unmixing context. Given the above, plus strong evidence of links between IVA and BTD, it is deemed worthwhile to consider the possibilities of generalizing TPICA to a BTD-based ``TPIVA" extension, which would more successfully combine the power of statistics and tensor decomposition. This could also entail a generalization of the BTD model, where (non)collinearity would be replaced by statistical (in)dependence. This note aims to outline the state-of-the-art and the above ideas in more detail, serving as a preliminary, motivating step in this research direction.

Problem

• fMRI
• source unmixing
• statistical independence
• tensor decomposition
• spatial overlap

Innovation

• Block-Term Decomposition
• Independent Vector Analysis
• Tensor Probabilistic ICA
• fMRI source unmixing
• Statistical dependence