Visual motion and rapid auditory processing are solid endophenotypes of developmental dyslexia
Bibliographic record
Abstract
Although a genetic component is known to have an important role in the etiology of developmental dyslexia (DD), we are far from understanding the molecular etiopathogenetic pathways. Reduced measures of neurobiological functioning related to reading (dis)ability, i.e. endophenotypes (EPs), are promising targets for gene finding and the elucidation of the underlying mechanisms. In a sample of 100 nuclear families with DD (229 offspring) and 83 unrelated typical readers, we tested whether a set of well-established, cognitive phenotypes related to DD [i.e. rapid auditory processing (RAP), rapid automatized naming (RAN), multisensory nonspatial attention and visual motion processing] fulfilled the criteria of the EP construct. Visual motion and RAP satisfied all testable criteria (i.e. they are heritable, associate with the disorder, co-segregate with the disorder within a family and represent reproducible measures) and are therefore solid EPs of DD. Multisensory nonspatial attention satisfied three of four criteria (i.e. it associates with the disorder, co-segregates with the disorder within a family and represents a reproducible measure) and is therefore a potential EP for DD. Rapid automatized naming is heritable but does not meet other criteria of the EP construct. We provide the first evidence of a methodologically and statistically sound approach for identifying EPs for DD to be exploited as a solid alternative basis to clinical phenotypes in neuroscience.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".