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Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal
Hannes Almgren1, Frederik Van de Steen1, Simone Kühn2
1Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium.
Neuroimage
|August 31, 2018
Summary
Spectral Dynamic Causal Modelling (DCM) reliably estimates brain connectivity within subjects. Accounting for hemispheric asymmetry and using Bayesian model reduction enhances consistency for clinical applications.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Systems Neuroscience
Background:
- Spectral Dynamic Causal Modelling (DCM) is a key method for inferring effective connectivity in resting-state functional MRI (fMRI).
- Current applications primarily focus on group-averaged connectivity, with limited understanding of subject- and session-specific reliability.
- Reliability is essential for clinical use, such as employing neurophysiological phenotypes for disease progression.
Purpose of the Study:
- To evaluate the consistency and reliability of spectral DCM effective connectivity estimates within and between subjects.
- To identify sources of variability, including hemispheric asymmetry and data processing choices.
- To assess the impact of standard fMRI processing steps on connectivity estimate robustness.
Main Methods:
- Applied Dynamic Causal Modelling (DCM) analyses to four longitudinal resting-state fMRI datasets.
- Utilized a large sample of 17 subjects and 589 total sessions for robust statistical power.
- Investigated effects of hemispheric asymmetry and standard data processing procedures (e.g., global signal regression, ROI size).
Main Results:
- Found systematic and reliable patterns of hemispheric asymmetry in effective connectivity.
- Demonstrated highly similar connectivity patterns across subjects when asymmetry was accounted for.
- Observed minimal impact of common processing choices (global signal regression, ROI size) on reliability for most subjects.
- Reported that Bayesian model reduction significantly improved the consistency of connectivity patterns.
Conclusions:
- Spectral DCM provides reliable and consistent subject-specific effective connectivity estimates, especially when accounting for hemispheric asymmetry.
- Standard data processing choices have limited influence on the reliability of these estimates.
- Bayesian model reduction is a valuable technique for enhancing the consistency of effective connectivity inference in resting-state fMRI.
- These findings support the clinical utility of spectral DCM for neurophysiological phenotyping.
Keywords:
Dynamic causal modellingEffective connectivityLongitudinal designsReliabilityResting stateVariabilityfMRIMore Related Videos
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