Rhythm and timing as vulnerabilities in neurodevelopmental disorders
Bibliographic record
Abstract
Millions of children are impacted by neurodevelopmental disorders (NDDs), which unfold early in life, have varying genetic etiologies and can involve a variety of specific or generalized impairments in social, cognitive and motor functioning requiring potentially lifelong specialized supports. While specific disorders vary in their domain of primary deficit (e.g. autism spectrum disorder (social), attention-deficit/hyperactivity disorder (attention), developmental coordination disorder (motor) and developmental language disorder (language)), comorbidities between NDDs are common. Intriguingly, many NDDs are associated with difficulties in skills related to rhythm, timing and synchrony though specific profiles of rhythm/timing impairments vary across disorders. Impairments in rhythm/timing may instantiate vulnerabilities for a variety of NDDs and may contribute to both the primary symptoms of each disorder as well as the high levels of comorbidities across disorders. Drawing upon genetic, neural, behavioural and interpersonal constructs across disorders, we consider how disrupted rhythm and timing skills early in life may contribute to atypical developmental cascades that involve overlapping symptoms within the context of a disorder's primary deficits. Consideration of the developmental context, as well as common and unique aspects of the phenotypes of different NDDs, will inform experimental designs to test this hypothesis including via potential mechanistic intervention approaches. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".