Prenatal exposure to air pollution and infant cognitive development using an eye-tracking visual paired-comparison task
Bibliographic record
Abstract
Although the prenatal life is a critical period for brain development, very few studies have focused on prenatal exposure to air pollution in relation to infant cognition, and most studies have relied on carer-reported outcome assessment. We examined the association between prenatal exposure to air pollution and objective measurement of infant cognitive development using an eye-tracking methodology. The study was based on data from a subset of 168 mother-child pairs participating in the Barcelona Life Study Cohort (BiSC), Spain (2018-2023). Total exposure to nitrogen dioxide (NO 2 ), black carbon (BC), particulate matter (PM 2.5 ), and PM 2.5 copper (Cu) and iron (Fe) content during pregnancy were assessed through integrating estimates of land-use regression (LUR) models with data on time spent at home, workplace, and during commuting. Cognitive performance was assessed longitudinally at 6 months ( n =156) and 18 months ( n =62) of age through an eye-tracking Visual Paired-Comparison (VPC) task that measured short-term recognition memory for faces. Linear mixed models were applied to explore the association between prenatal air pollution exposure and the percentage looking time at novel compared to familiar stimuli on the VPC task, adjusting for potential confounders. Results showed worse cognitive performance with increasing air pollution exposure: For each interquartile range increase of NO 2 , BC, PM 2.5 , Cu and Fe, novelty preference decreased with -2.0 (95% CI: -4.7, 0.6), -2.5 (-4.6, -0.5), -3.9 (-7.0, -0.9), -2.1 (-4.0, -0.3), and -1.4 (-3.2, 0.3), respectively. There were suggestions for potentially stronger associations in boys. The findings highlight the pregnancy period as a window of vulnerability for the impact of air pollution on the developing brain, and values eye-tracking as an objective non-invasive tool for early detection of such impact.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".