Diffusion tensor imaging of white matter and correlates to eye movement control and psychometric testing in children with prenatal alcohol exposure
Bibliographic record
Abstract
Prenatal alcohol exposure (PAE) can cause central nervous system dysfunction and widespread structural anomalies as detected by magnetic resonance imaging (MRI). This study focused on diffusion tensor imaging (DTI) of white matter in a large sample of PAE participants that allowed us to examine correlations with behavioral outcomes. Participants were confirmed PAE (n = 69, mean age = 12.5 ± 3.2 years) or typically developing control children (n = 67, mean age = 12.1 ± 3.2 years) who underwent brain MRI, eye movement tasks, and psychometric tests. A semi-automated tractography method extracted fractional anisotropy (FA) and mean diffusivity (MD) values from 15 white matter tracts. The PAE group displayed decreased FA compared with controls in multiple tracts including 3 corpus callosum regions, right corticospinal tract, and 3 left hemisphere tracts connecting to the frontal lobe (cingulum, uncinate fasciculus, and superior longitudinal fasciculus). Significant group by sex interactions were found for the genu, left superior longitudinal fasciculus, and the left uncinate, with females in the PAE group exhibiting lower FA compared with control females. Correlations were found between DTI and eye movement measures in the control group, but these same relationships were absent in the PAE group. In contrast, no correlations were found between DTI and any of the psychometric tests used in this study. These findings support the hypothesis that measures of eye movement control may be valuable functional biomarkers of the brain injury induced by PAE as these tasks reveal group differences that appear to be linked to deficits in white matter integrity in the brain. Hum Brain Mapp 38:444-456, 2017. © 2016 Wiley Periodicals, Inc.
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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 | 0.002 |
| 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".