Emerging atypical connectivity networks for processing angry and fearful faces in very preterm born children
Bibliographic record
Abstract
Very preterm born (VPT) children are those born before 32/40 weeks' gestational age and comprise 10% of the 15 million babies born prematurely worldwide each year. Due to advancements in neonatal medicine, the survival rate of VPT birth has increased, but few studies have investigated the nonmedical, social-cognitive morbidities that affect these children. In this study, we examined emotional face processing networks in VPT compared to age and sex matched full-term born (FT) children. Magnetoencephalography (MEG) was used to test VPT and FT born children at 6 years (n = 78) and 8 years (n = 83). Children were assessed using an implicit emotion face-processing task. Happy, fearful, and angry faces were presented for 150 ms, but children were asked to respond by button press to the location of a control pixelated image of the face displayed on the side of the screen opposite to the face. Children rated the valence of the images on a five-point scale. Group differences showed that VPT children rated angry faces more positively than their FT peers. VPT children had reduced connectivity for angry and fearful faces at 8 years in networks including regions such as the bilateral amygdala, superior temporal sulci, and anterior cingulate gyrus. Interventions should target both emotion recognition, as well as higher cognitive processes related to emotional control and thinking about one's own emotions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".