Default mode network anti-correlation as a transdiagnostic biomarker of cognitive function
Why this work is in the frame
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Bibliographic record
Abstract
The default mode network (DMN) is intricately linked with processes such as self-referential thinking, episodic memory recall, self-projection, and understanding the mindset of others. Over recent years, there has been a surge in examining its functional connectivity, particularly its antagonistic relationship with frontoparietal networks (FPN) involved in top-down attention, executive function, and cognitive control. Notably, the DMN demonstrates an anti-correlated connection with FPN and Dorsal Attention Network (DAN), leading to its deactivation when one's attention is turned towards the external environment. The fluidity in switching between these internal and external modes of processing—highlighted by this anti-correlated functional connectivity—has been proposed as an indicator of cognitive health and mediated by salience networks (SAL). Due to the ease of the estimation of functional connectivity-based measures through resting state fMRI paradigms, there is now a wealth of large-scale datasets, paving the way for standardized connectivity benchmarks. This review delves into the promising role of DMN connectivity metrics as potential biomarkers of cognitive state across attention, mind wandering and meditation states, and investigating deviations in clinical conditions such as anxiety, depression, ADHD, PTSD and others. Additionally, we tackle the issue of reliability of network estimation and functional connectivity and share recommendations for using connectivity measures as a biomarker of cognitive health.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it