Behavioral Heterogeneity in Relation with Brain Functional Networks in Young Children
Bibliographic record
Abstract
This study aimed to identify distinct behavioral profiles in a population-based sample of 654 4-year-old children and characterize their relationships with brain functional networks using resting-state functional magnetic resonance imaging data. Young children showed 7 behavioral profiles, including a super healthy behavioral profile with the lowest scores across all Child Behavior CheckList (CBCL) subscales (G1) and other 6 behavioral profiles, respectively with pronounced withdrawal (G2), somatic complaints (G3), anxiety and withdrawal (G4), somatic complaints and withdrawal (G5), the mixture of emotion, withdrawal, and aggression (G6), and attention (G7) problems. Compared with children in G1, children with withdrawal shared abnormal functional connectivities among the sensorimotor networks. Children in emotionally relevant problems shared the common pattern among the attentional and frontal networks. Nevertheless, children in sole withdrawal problems showed a unique pattern of connectivity alterations among the sensorimotor, cerebellar, and salience networks. Children with somatic complaints showed abnormal functional connectivities between the attentional and subcortical networks, and between the language and posterior default mode networks. This study provides novel evidence on the existence of behavioral heterogeneity in early childhood and its associations with specific functional networks that are clinically relevant phenotypes for mental illness and are apparent from early childhood.
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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 | 0.001 |
| 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".