Rate of functional network maturity and the role of environmental factors
Bibliographic record
Abstract
The developmental period from childhood to adolescence is marked by significant changes to the functional properties of the brain that support various aspects of higher-level cognition. Environmental factors such as socioeconomic status and adversity can have an outsized influence on neurocognitive development. However, not all environmental factors have the same influence on cognitive and brain development. In the current study, we examined the differential influences of SES (i.e., parental education and neighbourhood safety) and adversity on the maturation rate of functional networks in children and adolescents. Using resting-state fMRI data, independent component analysis with dual regression was computed to identify six networks (Default Mode Network (DMN), Left Executive Control Network (ECN), Right ECN, Hippocampal (HPC), Salience, and Sensorimotor networks) of interest in children and adolescents aged 7 to 15 (N=216, acquired from the Healthy Brain Network). A neural maturity index was generated based on the degree of similarity between the spatial configuration of the six networks in each youth brain to that of an adult template (1243 total, with a mean age of 26; independently by sex). Regression analyses were used to determine the association between neural maturity, social-cognitive abilities and environmental factors such as parental education, neighbourhood safety and number of negative life events (adversity). We found one sensory (sensorimotor) and two association (default mode and executive control) networks matured faster than other networks. Only the rate of maturity of the DMN and HPC were associated with environmental factors. Maturity of the DMN was associated with less adversity and better social cognitive ability, whereas maturity of the HPC network was associated with younger participants with higher IQs. Moreover, these effects were stronger in females than males. Our results highlight the importance of examining the unique contributions of distinct dimensions of childhood environments on neurocognitive development.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| 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".