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 changes in FC using both simulated fMRI time courses and resting state fMRI data. We show that large between-subject FC differences can overwhelm subtler within-session modulations, causing the model to become static. Further, the choice of parcellation can also affect the model’s ability to detect temporal changes. We finally show that the model often becomes static when the number of free parameters that need to be estimated is high and the number of observations available for this estimation is low in comparison. Based on these findings, we derive a set of practical recommendations for time-varying FC studies, in terms of preprocessing, parcellation and complexity of the model. Highlights Time-varying FC models sometimes fail to detect temporal changes in fMRI data Between- and within-subject FC variability affect model stasis The choice of parcellation affects model stasis in real fMRI data The number of observations and free parameters critically affect model stasis Keywords: fMRI; time-varying FC; Hidden Markov Model (HMM); resting state