Oxytocin enhances basolateral amygdala activation and functional connectivity while processing emotional faces: preliminary findings in autistic vs non-autistic women
Bibliographic record
Abstract
Oxytocin is hypothesized to promote social interactions by enhancing the salience of social stimuli. While previous neuroimaging studies have reported that oxytocin enhances amygdala activation to face stimuli in autistic men, effects in autistic women remain unclear. In this study, the influence of intranasal oxytocin on activation and functional connectivity of the basolateral amygdala-the brain's 'salience detector'-while processing emotional faces vs shapes was tested in 16 autistic and 21 non-autistic women by functional magnetic resonance imaging in a placebo-controlled, within-subject, cross-over design. In the placebo condition, minimal activation differences were observed between autistic and non-autistic women. However, significant drug × group interactions were observed for both basolateral amygdala activation and functional connectivity. Oxytocin increased left basolateral amygdala activation among autistic women (35-voxel cluster, Montreal Neurological Institute (MNI) coordinates of peak voxel = -22 -10 -28; mean change = +0.079%, t = 3.159, PTukey = 0.0166) but not among non-autistic women (mean change = +0.003%, t = 0.153, PTukey = 0.999). Furthermore, oxytocin increased functional connectivity of the right basolateral amygdala with brain regions associated with socio-emotional information processing in autistic women, but not in non-autistic women, attenuating group differences in the placebo condition. Taken together, these findings extend evidence of oxytocin's effects on the amygdala to specifically include autistic women and specify the subregion of the effect.
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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 | 0.001 |
| 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".