Girls’ attentive traits associate with cerebellar to dorsal attention and default mode network connectivity
Bibliographic record
Abstract
ABSTRACT Attention traits are a cornerstone to the healthy development of children’s performance in the classroom, their interactions with peers, and in predicting future success and problems. The cerebellum is increasingly appreciated as a region involved in complex cognition and behavior, and moreover makes important connections to key brain networks known to support attention: the dorsal attention and default mode networks (DAN; DMN). The cerebellum has also been implicated in childhood disorders affecting attention, namely autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), suggesting that attention networks extending to the cerebellum may be important to consider in relation to attentive traits. Yet, direct investigations into the association between cerebellar FC and attentive traits are lacking. Therefore, in this study we examined attentive traits, assessed using parent reports of ADHD and ASD symptoms, in a community sample of 52 girls aged 4-7 years, i.e. around the time of school entry, and their association with cerebellar connections with the DAN and DMN. We found that cortico-cerebellar functional connectivity (FC) jointly and differentially correlated with attentive traits, through a combination of weaker and stronger FC across anterior and posterior DAN and DMN nodes. These findings suggest that cortico-cerebellar integration may play an important role in the manifestation of attentive traits.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".