EiDA: A lossless approach for dynamic functional connectivity; application to fMRI data of a model of ageing.

ABSTRACT: Dynamic Functional Connectivity (dFC) is the study of the dynamic patterns of interaction that characterise brain function. Numerous numerical methods are available to compute and analyse dFC from high-dimensional data. In fMRI, a number of them rely on the computation of the instantaneous Phase Alignment (iPA) matrix (also known as instantaneous Phase Locking). Their limitations are the high computational cost and the concomitant need to introduce approximations with ensuing information loss. Here, we introduce the analytical decomposition of the iPA. This has two advantages. Firstly, we achieve an up to 1000-fold reduction in computing time without information loss. Secondly, we can formally introduce two alternative approaches to the analysis of the resulting time-varying instantaneous

SUBMITTER: de Alteriis G 

PROVIDER: S-EPMC11801787 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature