State-dependent relationship between lower and higher order networks in fMRI brain dynamics

Resting-state functional magnetic resonance imaging (fMRI) is a powerful tool for exploring the brain's functional organization. Functional connectivity (FC) is a commonly studied feature of fMRI data defined as the temporal correlation between activity patterns in pairs of brain regions. A major discovery of the past two decades of FC research has been the identification of consistent modular groupings in brain region time series correlations, commonly known as resting-state networks (RSNs). A second major discovery is the observation that RSNs in cortex are organized spatially along a functional/anatomical gradient. At one end of this gradient are 'lower order' networks (LONs), predominantly specialized for unimodal information processing. At the other end are 'higher order' networks (HONs), responsible for integrating multimodal information. Unlike the stable structural connectivity (SC) based on fixed anatomical links, FC fluctuates over time, and varies across brain regions. FC coordination within RSNs depends on SC, forming interconnected networks that regulate cognition, emotion, and behavior. The aim of the present study was to understand better how RSNs interact and communicate, based on their underlying SC. We used a whole-brain connectome-based neural mass modelling approach to study resting-state and task-based fMRI FC data. Following virtual SC lesions in the model, we characterized the FC changes within and between RSNs, and observed how these changes varied across different cognitive states. Our findings reveal how FC dynamics depend on underlying SC, highlighting the flexibility of these interactions across different brain states. LON lesions generally decrease FC within and between other LONs, and vice-versa for HON lesions. At rest, we observed a mutual antagonism between LONs and HONs, which was reversed during task conditions, with certain tasks increasing coordination between LONs and HONs. These results highlight the dynamic nature of brain network interactions, influenced by brain states and task demands. Our findings also have implications for clinical practice, offering insights into conditions such as brain tumors and stroke.