PT  - JOURNAL ARTICLE
AU  - Ravi D. Mill
AU  - Julia L. Hamilton
AU  - Emily C. Winfield
AU  - Nicole Lalta
AU  - Richard H. Chen
AU  - Michael W. Cole
TI  - Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior
AID  - 10.1101/2021.01.26.428276
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2021.01.26.428276
4099  - http://biorxiv.org/content/early/2022/05/20/2021.01.26.428276.short
4100  - http://biorxiv.org/content/early/2022/05/20/2021.01.26.428276.full
AB  - 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 non-invasive 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 with multivariate pattern analysis. 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 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 towards 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.Competing Interest StatementThe authors have declared no competing interest.