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.
View Article and Find Full Text PDFHuang et al. have conducted a thorough examination of methodologies used for identifying and analyzing functional connectivity using task-based fMRI. Their review adeptly describes current approaches without bias or preference.
View Article and Find Full Text PDFWe generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy (oCSE) and compared aFN topology to 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 (NCANDA) 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.
View Article and Find Full Text PDFBackground: Cardiorespiratory fitness (CRF) supports cognition, though it is unclear what mechanisms underly this relationship. Insulin resistance adversely affects cognition but can be reduced with habitual exercise.
Objective: We investigated whether insulin resistance statistically mediates the relationship between CRF and cognition.