Attention Deficit Hyperactivity Disorder (ADHD): A Comprehensive Overview of the Mechanistic Insights from Human Studies to Animal Models
Bibliographic record
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by persistent inattention, hyperactivity, and impulsivity. Despite its considerable global prevalence, key gaps remain in our understanding of the structural and molecular changes underlying ADHD which complicate adult diagnosis, as symptoms present differently from those observed during childhood ADHD. On the other hand, while psychostimulants effectively mitigate some symptoms, significant controversy surrounds their long term effects on cognition, learning and memory, and day to day living. Moreover, our understanding of how various medications given to alleviate ADHD symptoms during pregnancy impact the developing fetal brain also remains largely unexplored. Here, we discuss the subtle differences between ADHD in children and adults and how these symptoms alter brain development and maturation. We further examine changes in monoamine signaling in ADHD and how psychostimulant and non pharmacological treatments modulate these neural networks. We evaluate and discuss findings as they pertain to the long term use of ADHD medications, including in utero exposure, on cognitive outcomes and contextualize these findings with mechanistic insights from animal models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".