Resting-State Network Functional Connectivity Patterns Associated with the Mindful Attention Awareness Scale
Bibliographic record
Abstract
Mindfulness refers to attending to moment-to-moment experiences with acceptance and no judgment. Several scales have been developed to quantify different components of mindfulness. The Mindful Attention Awareness Scale (MAAS) is particularly sensitive to trait mindfulness and is proposed to measure the attentional component of mindfulness. The purpose of this study was to identify the neural correlates of the MAAS in four resting-state networks related to attention-the default mode network (DMN), the salience network (SN), and the left and right central executive network (CEN). Thirty-two university students naive to mindfulness completed the MAAS and later underwent a resting-state functional magnetic resonance imaging scan. Resting-state data were analyzed using an independent component analysis; the scores from the MAAS were covaried to the connectivity maps in an analysis of covariance. The results indicate that variations in MAAS scores correlated with variations in functional connectivity patterns in resting-state networks. Specifically, within the SN and CEN, the MAAS was negatively correlated with functional connectivity in the precuneus, even though the precuneus is a key component of the DMN. Negative correlations in the DMN between the MAAS and the insula and negative correlations in the SN between the MAAS and the posterior cingulate cortex were also observed. These results suggest that MAAS scores (1) are correlated with the functional connectivity of several brain structures related to attention and (2) involve cross-network functional connectivity.
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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 | 0.003 |
| 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 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".