Contrasting topologies of synchronous and asynchronous functional brain networks

We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy and compared aFN topology with the correlation-based synchronous functional networks (sFNs), which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and Neurodevelopment in Adolescence study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the default mode network in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity that the other type of network does not.

In recent years, the network neuroscience field has increasingly grown toward the study of “dynamic” networks, which depict second-to-second changes in functional connectivity. However, it is unclear how the brain is able to self-direct shifts between distinct states of connectivity. Prior investigation of information dynamics in complex systems suggests that these shifts may be governed by underlying asynchronous relationships between brain regions. For the first time, this work applies a novel methodology to generate asynchronous functional brain networks from fMRI data. The topology of these asynchronous networks is contrasted with the topology of the synchronous correlation-based networks, which currently dominate the network neuroscience literature. Finally, asynchronous networks are shown to yield novel information about brain connectivity that is not captured by synchronous networks alone.