Loss of skills and onset patterns in neurodevelopmental disorders: Understanding the neurobiological mechanisms
Bibliographic record
Abstract
Patterns of onset in Autism Spectrum Disorder, including a pattern that includes loss of previously acquired skills, have been identified since the first reports of the disorder. However, attempts to study such "regression" have been limited to clinical studies, that until recently mostly involved retrospective reports. The current report reflects discussion that occurred at an NIMH convened meeting in 2016 with the purpose of bridging clinical autism research with basic and translational work in this area. This summary describes the state of the field with respect to clinical studies, describing gaps in knowledge based on limited methods and prospective data collected. Biological mechanisms that have been shown to account for regression early in development in specific conditions are discussed, as well as potential mechanisms that have not yet been explored. Suggestions include use of model systems during the developmental period and cutting-edge methods, including non-invasive imaging that may afford opportunities for a better understanding of the neurobiological pathways that result in loss of previously-attained skills. Autism Res 2018, 11: 212-222. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Loss of previously acquired skills, or regression, has been reported in Autism Spectrum Disorder since Kanner's reports in the 1950's. The current report reflects discussion from an NIMH convened meeting in 2016 with the purpose of bridging clinical autism research with basic and translational work in this area. This summary describes the state of the field regarding clinical studies and suggests use of model systems during the developmental period and cutting-edge methods, for a better understanding of the neurobiological pathways that result in loss of previously-attained skills.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".