Consequences of Variations in Genes that affect Dopamine in Prefrontal Cortex
Bibliographic record
Abstract
Patricia Goldman-Rakic played a groundbreaking role in investigating the cognitive functions subserved by dorsolateral prefrontal cortex and the key role of dopamine in that. The work discussed here builds on that including: 1) Studies of children predicted to have lower levels of prefrontal dopamine but otherwise basically normal brains (children treated for phenylketonuria [PKU]). Those studies changed medical guidelines, improving the children's lives. 2) Studies of visual impairments (in contrast sensitivity and motion perception) in PKU children due to reduced retinal dopamine and due to excessive phenylalanine during the first postnatal weeks. Those studies, too, changed medical guidelines. 3) Studies of working memory and inhibitory control differences in typically developing children due to differences in catechol-O-methyltransferase (COMT) genotype, which selectively affect prefrontal dopamine levels. 4) Studies of gender differences in the effect of COMT genotype on cognitive performance in older adults. 5) A hypothesis about fundamental differences between attention deficit hyperactivity disorder (ADHD) that includes hyperactivity and ADHD of the inattentive type. Those disorders are hypothesized to differ in the affected neural system, underlying genetics, responsiveness to medication, comorbidities, and cognitive and behavioral profiles. These sound quite disparate but they all grew systematically out the base laid down by Patricia Goldman-Rakic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".