Atypical development of emotional face processing networks in autism spectrum disorder from childhood through to adulthood
Bibliographic record
Abstract
Impairments in social functioning are hallmarks of autism spectrum disorder (ASD) and atypical functional connectivity may underlie these difficulties. Emotion processing networks typically undergo protracted maturational changes, however, those with ASD show either hyper- or hypo-connectivity with little consensus on the functional connectivity underpinning emotion processing. Magnetoencephalography was used to investigate age-related changes in whole-brain functional connectivity of eight regions of interest during happy and angry face processing in 190 children, adolescents and adults (6-39 years) with and without ASD. Findings revealed age-related changes from child- through to mid-adulthood in functional connectivity in controls and in ASD in theta, as well as age-related between-group differences across emotions, with connectivity decreasing in ASD, but increasing for controls, in gamma. Greater connectivity to angry faces was observed across groups in gamma. Emotion-specific age-related between-group differences in beta were also found, that showed opposite trends with age for happy and angry in ASD. Our results establish altered, frequency-specific developmental trajectories of functional connectivity in ASD, across distributed networks and a broad age range, which may finally help explain the heterogeneity in the literature.
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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 | 0.001 | 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) | 0.001 | 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".