Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the where and when) and then allow for empirical testing of alternative network models of brain function that link information to behavior (the how). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach-dynamic activity flow modeling-then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory-motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

Automated summary

Understanding how cognitive task behavior is generated by brain network interactions is a central question in neuroscience. This study presents a novel network modeling approach using noninvasive functional neuroimaging data to capture neural signatures of task information with high spatial and temporal precision. The approach combines MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA) to dynamically decode task information in the human brain. The modeling approach, called dynamic activity flow modeling, then simulates the flow of task-evoked activity over resting-state functional connections, providing insights into the network processes underlying sensory-motor information flow in the brain.