Cortico-Cerebellar Neurodynamics during Social Interaction in Autism Spectrum Disorder
Bibliographic record
Abstract
ABSTRACT Background Exploring neural network dynamics during social interaction could help to identify biomarkers of Autism Spectrum Disorders (ASD). Recently, the cerebellum, a brain structure that plays a key role in social cognition, has attracted growing interest. Here, we investigated the electrophysiological activity of the cortico-cerebrum network during real-time social interaction in ASD. We focused our analysis on theta oscillations (3-8 Hz), which have been associated with large-scale coordination of distant brain areas and might contribute to interoception, motor control, and social event anticipation, all skills known to be altered in ASD. Methods We combined the Human Dynamic Clamp, a paradigm for studying realistic social interactions using a virtual avatar, with high-density electroencephalography (HD-EEG). Using source reconstruction, we investigated power in the cortex and the cerebellum, along with coherence between the cerebellum and three cortical areas, and compared our findings in a sample of participants with ASD and with typical development (TD) (n = 140). We developed an open-source pipeline to analyse neural dynamics at the source level from HD-EEG data. Results Individuals with ASD showed a significant increase in theta band power during social interaction compared to resting state, unlike individuals with TD. In particular, we observed a higher theta power over the cerebellum and the frontal and temporal cortices in the ASD group compared to the TD group, alongside bilateral connectivity alterations between the cerebellum and the sensorimotor and parietal cortices. Conclusions This study uncovered ASD-specific alterations in the theta dynamics, especially in a network between the cerebellum and social-associated cortical networks.
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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) | 0.002 | 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".