Data and model considerations for estimating time-varying functional connectivity in fMRI.

ABSTRACT: Functional connectivity (FC) in the brain has been shown to exhibit subtle but reliable modulations within a session. One way of estimating time-varying FC is by using state-based models that describe fMRI time series as temporal sequences of states, each with an associated, characteristic pattern of FC. However, the estimation of these models from data sometimes fails to capture changes in a meaningful way, such that the model estimation assigns entire sessions (or the largest part of them) to a single state, therefore failing to capture within-session state modulations effectively; we refer to this phenomenon as the model becoming static, or model stasis. Here, we aim to quantify how the nature of the data and the choice of model parameters affect the model's ability to detect temporal c

SUBMITTER: Ahrends C 

PROVIDER: S-EPMC9361391 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature