Neonatal Amygdala Mean Diffusivity: A Potential Predictor of Emotional Face Perception
Bibliographic record
Abstract
Abstract The ability to differentiate between different facial expressions is an important part of human social and emotional development that begins in infancy. Studies have shown that within the first year of life, infants develop a distinctive attentional bias towards fearful facial expressions. Investigations into the neural basis for this bias have highlighted the significance of the amygdala. The amygdala’s role in directing attention towards fearful facial expressions underscores its importance in early emotional development, significantly influencing how infants interpret and react to facial expressions. To date, no studies have been conducted to investigate the associations between the amygdala microstructure and infants’ perception of emotional faces. This study aimed to elucidate this relationship while also investigating whether this association is sex specific. We measured the amygdala microstructural properties using diffusion tensor imaging mean diffusivity (MD) measurements in 40 healthy infants aged 2 to 5 weeks. Eye tracking was used to assess attention disengagement from fearful vs. non-fearful (happy and neutral) facial expressions as well as scrambled non-face control picture at 8 months. Generally, infants were age-typically less likely to disengage from fearful faces than from non-fearful faces towards salient distractors. A significant negative association was observed between the right amygdala MD measures and disengagement probability from fearful faces in the overall sample. Moreover, there was a positive association between the bilateral amygdala MD measures and the disengagement probability from scrambled non-face control picture in girls. These results indicate that the amygdala MD is associated with attention disengagement processes already in infancy, both in fear processing and in non-emotional conditions. Specifically, these findings highlight the role of the amygdala microstructure in modulating attentional processes, which may have implications for emotional regulation and susceptibility to emotional dysregulation later in life.
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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 | 0.000 | 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) | 0.002 | 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".