Atypical resting synchrony in autism spectrum disorder
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is increasingly understood to be associated with aberrant functional brain connectivity. Few studies, however, have described such atypical neural synchrony among specific brain regions. Here, we used magnetoencephalography (MEG) to characterize alterations in functional connectivity in adolescents with ASD through source space analysis of phase synchrony. Resting-state MEG data were collected from 16 adolescents with ASD and 15 age- and sex-matched typically developing (TD) adolescents. Atlas-guided reconstruction of neural activity at various cortical and subcortical regions was performed and inter-regional phase synchrony was calculated in physiologically relevant frequency bands. Using a multilevel approach, we characterized atypical resting-state synchrony within specific anatomically defined networks as well as altered network topologies at both regional and whole-network scales. Adolescents with ASD demonstrated frequency-dependent alterations in inter-regional functional connectivity. Hyperconnectivity was observed among the frontal, temporal, and subcortical regions in beta and gamma frequency ranges. In contrast, parietal and occipital regions were hypoconnected to widespread brain regions in theta and alpha bands in ASD. Furthermore, we isolated a hyperconnected network in the gamma band in adolescents with ASD which encompassed orbitofrontal, subcortical, and temporal regions implicated in social cognition. Results from graph analyses confirmed that frequency-dependent alterations of network topologies exist at both global and local levels. We present the first source-space investigation of oscillatory phase synchrony in resting-state MEG in ASD. This work provides evidence of atypical connectivity at physiologically relevant time scales and indicates that alterations of functional connectivity in adolescents with ASD are frequency dependent and region dependent.
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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 | 0.001 | 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".