ArticleNeuroImage2022
Data and model considerations for estimating time-varying functional connectivity in fMRI.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A comprehensive framework for statistical testing of brain dynamics.Nature protocols · 2026Review
- Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity.bioRxiv : the preprint server for biology · 2025Article
- TR(Acking) Individuals Down: Exploring the Effect of Temporal Resolution in Resting-State Functional MRI Fingerprinting.Human brain mapping · 2025Article
- Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel.eLife · 2025Article
- The Gaussian-linear hidden Markov model: A Python package.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Altered brain dynamics in post-stroke cognitive and motor dysfunction.Frontiers in aging neuroscience · 2025Article
- Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach.Translational psychiatry · 2024Article
- Stacking models of brain dynamics to improve prediction of subject traits in fMRI.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Brain state dynamics differ between eyes open and eyes closed rest.Human brain mapping · 2024Article
- Study of dynamic brain function in irritable bowel syndrome via Hidden Markov Modeling.Frontiers in neuroscience · 2024Article
- Time-varying functional connectivity as Wishart processes.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Dynamic functional connectivity: Why the controversy?Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Article
- High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model.PLoS computational biology · 2022Article
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Authors and funding
7 authors.
Funding
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 per state 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.
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