Functional connectivity analysis of childhood depressive symptoms
Bibliographic record
Abstract
BACKGROUND: Childhood depression is a highly distinct and prevalent condition with an unknown neurobiological basis. We wish to explore the resting state fMRI data in children for potential associations between neural connectivity and childhood depressive symptoms. METHODS: A longitudinal birth cohort study with neuroimaging data obtained at 4.5, 6.0 and 7.5 years of age and the Children Depression Inventory 2 (CDI) administered between 8.5 and 10.5 years was used. The CDI score was used as the dependent variable and tested for correlation, both simple Pearson and network based statistic, with the functional connectivity values obtained from the resting state fMRI. Cross-validated permutation testing with a general linear model was used to validate that the identified functional connections were indeed implicated in childhood depression. RESULTS: Ten functional connections and four brain regions (Somatomotor Area B, Temporoparietal Junction, Orbitofrontal Cortex and Insula) were identified as significantly associated with childhood depressive symptoms for girls at 6.0 and 7.5 years. No significant functional connections were found in girls at 4.5 years or for boys at any timepoint. Network based statistic and permutation testing confirmed these findings. CONCLUSIONS: This study revealed significant sex-dependent associations of neural connectivity and childhood depressive symptoms. The regions identified are implicated in speech/language, social cognition and information integration and suggest unique pathways to childhood depressive symptoms.
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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".