Neural Activation in Reward Network Associated with Inattentive Symptoms of Attention Deficit Hyperactivity Disorder
Bibliographic record
Abstract
Attention-deficit/hyperactivity disorder (ADHD) and impulsivity have been linked to the functioning of the brain’s reward network. However, many of these studies focus on the anticipatory phase of reward processing and are limited by small sample sizes. In the current study, a community sample of 1081 adults (mean age = 28.8, SD = 3.7) completed a computerized functional magnetic resonance imaging task examining reward outcome. Out-of-scanner participants completed self-report measures of ADHD symptoms and impulsivity. A voxelwise t-test of activation during reward outcomes indicated activation in the left and right ventral striatum, the ventromedial prefrontal cortex, and the posterior cingulate, as well as greater activation in sensory and motor areas. In voxelwise regression analyses, neural response to reward in the left striatum, insula, dorsolateral prefrontal cortex, and lateral temporal cortex, as well as bilaterally in the occipital cortex, was inversely associated with inattentive symptoms of ADHD. No associations were found between neural response to reward and hyperactive symptoms of ADHD or impulsivity. Results were generally consistent in follow-up region of interest analyses. These findings suggest activation in reward network regions is linked to inattentive, but not hyperactive symptoms of ADHD, even in those without a diagnosis.
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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".