Developmental changes in neuromagnetic rhythms and network synchrony in autism
Bibliographic record
Abstract
OBJECTIVE: There is gathering consensus that altered connectivity is a hallmark of the autistic brain. This includes atypical neural oscillations and their coordination across brain regions, which are understood to mediate information processing and integration. It remains unclear whether and how connectivity in various neurophysiological frequency ranges develops atypically in autism spectrum disorder (ASD). METHODS: To address this in a cross-sectional sample, we recorded resting-state magnetoencephalography from 134 children and adolescents with and without ASD, and calculated resting spectral power and inter-regional synchrony (functional connectivity). RESULTS: Although no overall group differences were observed, significant alterations in linear and nonlinear age-related changes in resting oscillatory power and network synchrony were found. These differences were frequency- and region-specific and implicated brain systems thought to play a prominent role in ASD, such as the frontal cortex and cerebellum. We also found correlations between Autism Diagnostic Observation Schedule scores and the degree to which connectivity in cerebellar networks is "idiosyncratic" in an individual with autism. INTERPRETATION: We provide the first evidence that it is the curvatures of maturational changes in neurophysiological oscillations and synchrony, rather than disturbances in a particular direction, that characterize the brain function in individuals with ASD. Moreover, the patterns of idiosyncratic distortions of network synchrony relative to the group curve are associated with behavioral symptoms of ASD. Ann Neurol 2017;81:199-211.
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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".