Functional connectivity dynamics: modeling the switching behavior of the resting state
Enrique C A Hansen1, Demian Battaglia2, Andreas Spiegler1
1Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, 27Bd Jean Moulin, 13005 Marseille, France.
Neuroimage
|December 3, 2014
Summary
Resting state functional connectivity (FC) exhibits dynamic, non-stationary switching between discrete states. Enhancing network non-linearity in computational models reproduces these dynamics, offering a promising biomarker for brain activity and disease.
Area of Science:
- Computational neuroscience
- Neuroimaging analysis
- Brain dynamics
Background:
- Functional connectivity (FC) reveals brain region interactions, crucial for basic research and clinical applications like Alzheimer's disease and schizophrenia.
- Current whole-brain computational models accurately simulate average resting-state FC but fail to capture its significant non-stationarity and variability.
- This non-stationarity in resting-state FC hinders accurate individual predictability in clinical settings.
Purpose of the Study:
- To investigate the rich structure of resting-state FC non-stationarity and its underlying mechanisms.
- To evaluate computational models' ability to reproduce spontaneous FC state transitions.
- To explore how network non-linearity influences FC dynamics and their potential as biomarkers.
Main Methods:
- Simulated whole-brain mean-field computational models with realistic tractography-derived connectivity.
- Compared models optimized for time-averaged FC with linear stochastic models and enhanced non-linear models.
- Analyzed spontaneous state transitions and fluctuations in functional connectivity dynamics (FCD).
Main Results:
- Resting-state FC non-stationarity is characterized by rapid transitions between discrete FC states.
- Models optimized for time-averaged FC do not reproduce these spontaneous state transitions.
- Enhancing network node non-linearity generates diverse network behaviors and non-stationary switching, mimicking empirical FCD.
Conclusions:
- Functional connectivity dynamics (FCD) reveal a richer structure than time-averaged FC.
- Computational models require enhanced non-linearity to capture spontaneous FC state transitions.
- FCD show promise as a more accurate biomarker for resting-state neural activity and its pathological alterations.
Keywords:
Brain dynamicsFunctional connectivityFunctional connectivity dynamicsResting stateStructural connectivityWhole brain computational modelMore Related Videos
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