Neural sensitivity in adults with autism spectrum disorder to an oxytocin trial: A proof of principle study
Bibliographic record
Abstract
The core characteristics of autism spectrum disorder (ASD) are restrictive and repetitive behaviours and interests and social and communication difficulties.A potential intervention for these difficulties is oxytocin, which has shown some success in ameliorating symptoms, but little is understood about the effects of oxytocin on neural processing.We recorded magnetoencephalography (MEG) during a social (emotional faces) and a cognitive (working memory) task in adults with ASD (n=14) undergoing a 12week randomized double blind intra-nasal oxytocin (IN-OXT) trial, pre-and post-trial.We compared the MEG metrics of brain connectivity in the two tasks both between groups (Placebo and IN-OXT) and between pre-and post-trial (Time 1 and Time 2).Although there were no behavioural differences in task performance, there were significant MEG differences in functional brain networks.In the faces task, we found a significant increase in beta-band connectivity only in the IN-OXT group at Time 2 compared to Time 1, in a network strongly linked with face processing.This suggests that the IN-OXT contributed to greater efficiency within the face processing network post-treatment.In the working memory task, the IN-OXT group also showed increased connectivity at Time 2, but in the theta band and in a frontal-parietal network known to be involved in working memory.Thus, network and frequency specific changes were seen to increase with IN-OXT administration post-trial, suggesting that oxytocin impacts neural functioning associated with these task-specific networks and may have positive effects that are worth pursuing in a larger clinical study.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".