Casting a wider fish net on animal models in neuropsychiatric research
Bibliographic record
Abstract
Neuropsychiatric disorders, such as schizophrenia, are associated with abnormal brain development. In this review, we discuss how studying dimensional components of these disorders, or endophenotypes, in a wider range of animal models will deepen our understanding of how interactions between biological and environmental factors alter the trajectory of neurodevelopment leading to aberrant behavior. In particular, we discuss some of the advantages of incorporating studies of brain and behavior using a range of teleost fish species into current neuropsychiatric research. From the perspective of comparative neurobiology, teleosts share a fundamental pattern of neurodevelopment and functional brain organization with other vertebrates, including humans. These shared features provide a basis for experimentally probing the mechanisms of disease-associated brain abnormalities. Moreover, incorporating information about how behaviors have been shaped by evolution will allow us to better understand the relevance of behavioral variation to determine their physiological underpinnings. We believe that exploiting the conservation in brain development across vertebrate species, and the rich diversity of fish behavior in lab and natural populations will lead to significant new insights and a holistic understanding of the neurobiological systems implicated in neuropsychiatric disorders.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".