State-dependent relationship between lower and higher order networks in fMRI brain dynamics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".