Contrasting topologies of synchronous and asynchronous functional brain networks
Biorxiv : the Preprint Server for Biology
|August 30, 2024
Summary
This study introduces asynchronous functional networks (aFNs) using optimal causation entropy (oCSE), revealing distinct network properties compared to synchronous functional networks (sFNs). Both network types provide unique insights into brain connectivity and how age influences them.
Area of Science:
- Neuroscience
- Network Science
- Computational Biology
Background:
- Synchronous functional networks (sFNs) derived from correlated fMRI data are standard in network neuroscience.
- Novel methods are needed to explore alternative functional network topologies.
Purpose of the Study:
- To introduce and characterize asynchronous functional networks (aFNs) using optimal causation entropy (oCSE).
- To compare the topological properties of aFNs with traditional sFNs.
- To investigate the influence of age on network efficiency and connection probabilities in both aFNs and sFNs.
Main Methods:
- Generation of aFNs and sFNs from fMRI data of 212 participants (NCANDA study).
- Application of optimal causation entropy (oCSE) for aFN generation.
- Multivariate mixed effects models to analyze age-network interactions.
Main Results:
- aFNs exhibit higher global efficiency and lower local efficiency than sFNs, after controlling for network density.
- Key nodes for outgoing global efficiency in aFNs are located in the brainstem and orbitofrontal cortex.
- Incoming global efficiency nodes in aFNs correspond to the Default Mode Network (DMN) in sFNs.
- Age interacts with global efficiency in aFNs and local efficiency in sFNs to affect connection probability.
Conclusions:
- Both aFNs and sFNs capture complementary information about functional brain connectivity.
- aFNs offer a novel perspective on brain network organization beyond synchronous correlations.
- Understanding age-related network dynamics requires considering both asynchronous and synchronous network properties.
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