Anticorrelation between default and dorsal attention networks varies across default subsystems and cognitive states
Bibliographic record
Abstract
Summary Anticorrelation between the default network (DN) and dorsal attention network (DAN) is thought to be an intrinsic aspect of functional brain organization reflecting competing functions. However, the stability of anticorrelations across distinct DN subsystems, different contexts, and time, remains unexplored. Here we examine DN-DAN functional connectivity across six different cognitive states. We show that:(i) the DAN is anticorrelated with the DN core subsystem, but not with the two DN subsystems involved in mentalizing and mnemonic functions, respectively; (ii) DN-DAN interactions vary significantly across cognitive states; (iii) DN-DAN connectivity fluctuates across time between periods of anticorrelation and periods of positive correlation; and (iv) coupling between the frontoparietal control network (FPCN) and DAN predicts variation in the strength of DN-DAN anticorrelation across time. These findings reveal substantial variability in DN-DAN interactions, suggesting that these networks are not strictly competitive, and that the FPCN may act to modulate their anticorrelation strength.
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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 | 0.000 |
| 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) | 0.002 | 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".