Most work on functional connectivity (FC) in neuroimaging data prefers longer scan sessions or greater subject count to improve reliability of brain–behavior relationships or predictive models. Here, we investigate whether systematically isolating moments in time can improve brain–behavior relationships and outperform full scan data. We assess how behavioral relationships vary over time points that are less visible in full FC based on co-fluctuation amplitude. Additionally, we perform optimizations using a temporal filtering strategy to identify time points that improve brain–behavior relationships. Analyses were performed on resting-state fMRI data of 352 healthy subjects from the Human Connectome Project and across 58 different behavioral measures. Templates were created to select time points with similar patterns of brain activity and optimized for each behavior to maximize brain–behavior relationships from reconstructed functional networks. With 10% of scan data, optimized templates of select behavioral measures achieved greater strength of brain–behavior correlations and greater transfer of behavioral associations between groups of subjects than full FC across multiple cross-validation splits of the dataset. Therefore, selectively filtering time points may allow for development of more targeted FC analyses and increased understanding of how specific moments in time contribute to behavioral prediction.

The social environment has a critical influence on human development, cognition, and health. Research in health psychology and social neuroscience indicate an urgent need to understand how social relationships are associated with brain function and organization. To address this, we apply multilayer modeling and modularity maximization—both established tools in network neuroscience—to jointly cluster patterns of brain-behavior associations for seven social support measures. By using network approaches to map and analyze the connectivity between all pairs of brain regions simultaneously, we can clarify how relationships between brain regions (e.g. connectivity) change as a function of social relationships. This multilayer approach enables direct comparison of brain-behavior associations across social contexts for all brain regions and builds on both ecological and developmental neuroscientific findings and network neuroscientific approaches. In particular, we find that subcortical and control systems are especially sensitive to different constructs of perceived social support. Network nodes in these systems are highly flexible; their community affiliations, which reflect groups of nodes with similar patterns of brain-behavior associations, differ across social support measures. Additionally, our application of multilayer modeling to patterns of brain-behavior correlations, as opposed to just functional connectivity, represents an innovation in how multilayer models are used in human neuroscience. More than that, it offers a generalizable technique for studying the stability and variation of brain-behavior associations.

Recent studies have shown that functional connectivity can be decomposed into its exact frame-wise contributions, revealing short-lived, infrequent, and high-amplitude time points referred to as “events.” Events contribute disproportionately to the time-averaged connectivity pattern, improve identifiability and brain-behavior associations, and differences in their expression have been linked to endogenous hormonal fluctuations and autism. Here, we explore the characteristics of events while subjects watch movies. Using two independently-acquired imaging datasets in which participants passively watched movies, we find that events synchronize across individuals and based on the level of synchronization, can be categorized into three distinct classes: those that synchronize at the boundaries between movies, those that synchronize during movies, and those that do not synchronize at all. We find that boundary events, compared to the other categories, exhibit greater amplitude, distinct co-fluctuation patterns, and temporal propagation. We show that underlying boundary events 1 is a specific mode of co-fluctuation involving the activation of control and salience systems alongside the deactivation of visual systems. Events that synchronize during the movie, on the other hand, display a pattern of co-fluctuation that is time-locked to the movie stimulus. Finally, we found that subjects’ time-varying brain networks are most similar to one another during these synchronous events.