Behavioral and neuroanatomical approaches in models of neurodevelopmental disorders: opportunities for translation
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review highlights the invaluable contribution of in-vivo rodent models in dissecting the underlying neurobiology for numerous neurodevelopmental disorders. Currently, models are routinely generated with precision genomics and characterized for research on neurodevelopmental disorders. In order to impact translation, outcome measures that are translationally relevant are essential. This review emphasizes the importance of accurate neurobehavioral and anatomical analyses. RECENT FINDINGS: Numerous well validated assays for testing alterations across behavioral domains with sensitivity and throughput have become important tools for studying the effects of genetic mutations on neurodevelopment. Recent work has highlighted relationships and links between behavioral outcomes and various anatomical metrics from neuroimaging via magnetic resonance. These readouts are biological markers and outcome measures for translational research and will be have important roles for genetic or pharmacologic intervention strategies. SUMMARY: Combinatorial approaches that leverage translationally relevant behavior and neuroanatomy can be used to develop a platform for assessment of cutting edge preclinical models. Reliable, robust behavioral phenotypes in preclinical model systems, with clustering of brain disease will lead to well informed, precise biochemical mechanistic hypotheses. Ultimately, these steadfast workhorse techniques will accelerate the progress of developing and testing targeted treatments for multiple neurodevelopmental disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".