Contrasting topologies of synchronous and asynchronous functional brain networks.
Clayton C McIntyre1,2, Mohsen Bahrami1,3, Heather M Shappell1,4
1Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Network Neuroscience (Cambridge, Mass.)
|December 30, 2024
Summary
This study introduces asynchronous functional networks (aFNs) and compares them to synchronous functional networks (sFNs) using fMRI data. aFNs reveal unique brain connectivity patterns, offering complementary insights to traditional sFN analysis.
Area of Science:
- Neuroscience
- Network Neuroscience
- Computational Neuroscience
Background:
- Synchronous functional networks (sFNs) are commonly used in network neuroscience.
- Novel methods are needed to explore alternative functional brain connectivity measures.
Purpose of the Study:
- To introduce and characterize asynchronous functional networks (aFNs) generated using optimal causation entropy.
- 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:
- Functional magnetic resonance imaging (fMRI) data from 212 participants were analyzed.
- Asynchronous functional networks (aFNs) were generated using optimal causation entropy.
- Synchronous functional networks (sFNs) were generated using correlation-based methods.
- Multivariate mixed effects models were employed to assess age-related interactions with network properties.
Main Results:
- After controlling for network density, aFNs exhibited higher global efficiency but lower local efficiency compared to sFNs.
- Key nodes for outgoing global efficiency in aFNs were identified in the brainstem and orbitofrontal cortex.
- Nodes with high incoming global efficiency in aFNs were associated with the default mode network in sFNs.
- Age interacted with global efficiency in aFNs and local efficiency in sFNs to affect connection probability.
Conclusions:
- Both aFNs and sFNs provide distinct and complementary information about functional brain connectivity.
- aFNs offer a novel perspective on brain network dynamics beyond synchronous correlations.
- The findings highlight the importance of considering different network metrics for a comprehensive understanding of brain function.
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