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Related Experiment Video

Updated: Oct 2, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

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

C Ahrends¹, A Stevner², U Pervaiz³

¹Department of Clinical Medicine, Center for Music in the Brain, Aarhus University & Royal Academy of Music Aarhus/Aalborg, Universitetsbyen 3, Aarhus C 8000, Denmark.

|February 26, 2022

Summary

State-based models can fail to detect dynamic functional connectivity (FC) changes in fMRI data, leading to model stasis. Data characteristics, model parameters, and parcellation choices significantly impact the ability to capture within-session FC modulations.

Keywords:

Hidden Markov Model (HMM)Resting state Time-varying FC fMRI

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Last Updated: Oct 2, 2025

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Published on: October 13, 2023

Area of Science:

Neuroimaging
Computational Neuroscience
Data Science

Background:

Brain functional connectivity (FC) shows dynamic, within-session modulations.
State-based models analyze time-varying FC from fMRI data.
Model stasis, where models fail to capture dynamic changes, is a common challenge.

Purpose of the Study:

Quantify how data characteristics and model parameters influence the detection of temporal FC changes.
Investigate the phenomenon of model stasis in time-varying FC analysis.
Provide practical recommendations for improving dynamic FC studies.

Main Methods:

Utilized simulated fMRI time courses and resting-state fMRI data.
Employed state-based models to estimate time-varying functional connectivity.
Analyzed the impact of between-subject FC differences, parcellation choices, and model complexity on model performance.

Main Results:

Large between-subject FC differences can obscure subtler within-session modulations, causing model stasis.
The choice of brain parcellation significantly affects the detection of temporal FC changes.
Model stasis is more likely when the number of estimated parameters per state is high relative to the number of available observations.

Conclusions:

Model stasis in time-varying FC analysis is influenced by data properties and model choices.
Recommendations are provided for optimizing preprocessing, parcellation, and model complexity.
Improved methodologies can enhance the accurate capture of dynamic brain connectivity.