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Dynamic causal modelling of fluctuating connectivity in resting-state EEG
Frederik Van de Steen1, Hannes Almgren1, Adeel Razi2
1Ghent University, Ghent, Belgium.
Neuroimage
|January 29, 2019
Summary
Effective connectivity in the brain
Area of Science:
- Neuroscience
- Cognitive Science
Background:
- Functional and effective connectivity change over time.
- Changes are influenced by intrinsic neuronal coupling and external factors.
- Contextual effects can be subject-specific or universal.
Purpose of the Study:
- To characterize dynamic fluctuations in effective connectivity.
- To investigate changes within the default mode and saliency networks.
- To differentiate subject-specific and common connectivity patterns.
Main Methods:
- Dynamic Causal Modelling (DCM) of electroencephalogram (EEG) data.
- Analysis of 1-second windows over a 1-minute resting state recording.
- Hierarchical parametric empirical Bayes (PEB) for connectivity parameter assessment.
Main Results:
- Connectivity fluctuations in default mode and saliency networks show subject-specific and common components.
- Connections to higher-order regions monotonically increased over the 1-minute period.
- Systematic changes detected suggest predictive validity of dynamic connectivity estimates.
Conclusions:
- Brain network connectivity is dynamic and exhibits both individual and shared patterns.
- Network-specific differences exist in the contribution of contextual connectivity fluctuations.
- Methodology can aid in characterizing brain state transitions and their underpinnings.
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