Spatial and temporal functional connectivity changes between resting and attentive states
Bibliographic record
Abstract
Remote brain regions show correlated spontaneous activity at rest within well described intrinsic connectivity networks (ICNs). Meta-analytic coactivation studies have uncovered networks similar to resting ICNs, suggesting that in task states connectivity modulations may occur principally within ICNs. However, it has also been suggested that specific "hub" regions dynamically link networks under different task conditions. Here, we used functional magnetic resonance imaging at rest and a continuous visual attention task in 16 participants to investigate whether a shift from rest to attention was reflected by within-network connectivity modulation, or changes in network topography. Our analyses revealed evidence for both modulation of connectivity within the default-mode (DMN) and dorsal attention networks (DAN) between conditions, and identified a set of regions including the temporoparietal junction (TPJ) and posterior middle frontal gyrus (MFG) that switched between the DMN and DAN depending on the task. We further investigated the temporal nonstationarity of flexible (TPJ and MFG) regions during both attention and rest. This showed that moment-to-moment differences in connectivity at rest mirrored the variation in connectivity between tasks. Task-dependent changes in functional connectivity of flexible regions may, therefore, be understood as shifts in the proportion of time specific connections are engaged, rather than a switch between networks per se. This ability of specific regions to dynamically link ICNs under different task conditions may play an important role in behavioral flexibility.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".