Performance assessment of IVA-based subgroup identification methods and their application in experimental fUS data

Abstract

Using independent vector analysis (IVA) to analyze and find subgroups in functional magnetic resonance imaging (fMRI) and functional ultrasound (fUS) data requires a lot of manual labour. Recently methods like subgroup identification using IVA (SI-IVA) and IVA for common subspace identification (IVA-CS) have tried to reduce this labour through automation. However, both methods did not test for accuracy. This thesis shows through simulations that these proposed methods are not accurate or robust enough to be trusted and that spectral clustering is a better alternative for automatic subgroup identification. Spectral clustering is then incorporated into the analysis of experimental fUS data of two groups of mice to try and identify these automatically. In this analysis, adaptive constrained IVA (acIVA) was used to incorporate references, further improving the interpretability of the results as components are directly linked to prior constraints.
However, applying subgroup analysis showed that the mice could not be clustered based on their response to the stimuli. Still, spectral clustering is more accurate in the simulations making it a promising alternative for automatic subgroup identification. Furthermore, combining spectral clustering with acIVA makes the results more interpretable due to constrained components not being subject to permutation ambiguity.