Independent Vector Analysis (IVA) is a widely used technique for multi-subject fMRI analysis. To guide the decomposition and increase the interpretability of the extracted components, constrained IVA methods incorporate prior information, also known as references, into the model to guide the solution towards those. In this context, threshold-free constrained IVA (tf-cIVA) avoids the difficult issue of threshold selection by leveraging the structure of IVA and using a regularization term to encourage correlation with the corresponding component and penalize all cross-component correlations. Although it is shown to provide desirable performance, the strategy in tf-cIVA can suppress correlations between brain networks within the same functional domain, contrary to the expected neurobiological behavior. In this work, we propose a domain-informed tf-cIVA (ditf-cIVA) that defines a regularization term to selectively preserve the correlation within brain domains. We compare our method with tf-cIVA on a resting-state fMRI dataset of 58 healthy controls and 58 schizophrenia patients. Our findings demonstrate that ditfcIVA produces a more modular spatial functional network connectivity structure, yields more consistent component estimations as measured by higher one-sample t-values, and shows increased sensitivity in detecting significant group differences between the two cohorts.
Gois et al. (Fri,) studied this question.