Effect of Dopamine Transporter Genotype on Intrinsic Functional Connectivity Depends on Cognitive State
Bibliographic record
Abstract
Functional connectivity between brain regions can define large-scale neural networks and provide information about relationships between those networks. We examined how relationships within and across intrinsic connectivity networks were 1) sensitive to individual differences in dopaminergic function, 2) modulated by cognitive state, and 3) associated with executive behavioral traits. We found that regardless of cognitive state, connections between frontal, parietal, and striatal nodes of Task-Positive networks (TPNs) and Task-Negative networks (TNNs) showed higher functional connectivity in 10/10 homozygotes of the dopamine transporter gene, a polymorphism influencing synaptic dopamine, than in 9/10 heterozygotes. However, performance of a working memory task (a state requiring dopamine release) modulated genotype differences selectively, such that cross-network connectivity between TPNs and TNNs was higher in 10/10 than 9/10 subjects during working memory but not during rest. This increased cross-network connectivity was associated with increased self-reported measures of impulsivity and inattention traits. By linking a gene regulating synaptic dopamine to a phenotype characterized by inefficient executive function, these findings validate cross-network connectivity as an endophenotype of executive dysfunction.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".