Developmental exposure to legacy environmental contaminants, medial temporal lobe volumes and spatial navigation memory in late adolescents
Bibliographic record
Abstract
Exposure to lead, mercury, and polychlorinated biphenyls (PCBs) has been causally linked to spatial memory deficits and hippocampal changes in animal models. The Inuit community in Northern Canada is exposed to higher concentrations of these contaminants compared to the general population. This study aimed to 1) investigate associations between prenatal and current contaminant exposures and medial temporal brain volumes in Inuit late adolescents; 2) examine the relationship between these brain structures and spatial memory; and 3) assess the mediating role of brain structures in the association between contaminant exposure and spatial memory. Prenatal and current exposures were assessed from blood samples collected at birth and at the time of testing in 71 participants aged 16-22. Volumetric measurements of the hippocampi, entorhinal, and parahippocampal cortices from T1-weighted images were obtained using the Automatic Segmentation of Hippocampal Subfields method. Spatial navigation memory was evaluated using a computerized Morris Water Maze task. Prenatal lead exposure was associated with a smaller left hippocampal volume (β = -0.30, 95% CI = -0.56, -0.05), while current mercury (β = -0.34, 95% CI = -0.62, -0.06) and PCB-153 (β = -0.36, 95% CI = -0.70, -0.01) exposures were linked to a smaller left entorhinal cortex. The volume of the left entorhinal cortex positively correlated with spatial navigation memory performance (β = 0.26, 95% CI = 0.01, 0.51). These findings suggest specific windows of brain vulnerability to these contaminants, with the entorhinal cortex playing a key role in spatial navigation memory and potentially mediating the effects of exposure.
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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 | 0.000 | 0.000 |
| 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".