The pros and cons of motor, memory, and emotion-related behavioral tests in the mouse traumatic brain injury model
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a medical emergency with high morbidity and mortality. Motor, memory, and emotion-related deficits are common symptoms following TBI, yet treatment is very limited. To develop new drugs and find new therapeutic avenues, a wide variety of TBI models have been established to mimic the heterogeneity of TBI. In this regard, along with histologic measures, behavioral functional outcomes provide valuable insight into the underlying neuropathology and guide neurorehabilitation efforts for neuropsychiatric impairment after TBI. Development, characterization, and application of behavioral tests that can assess functional neurologic deficits are essential to the development of translational therapies. This comprehensive review aims to summarize 19 common behavioral tests from three aspects (motor, memory, and emotion-related) that are associated with TBI pathology. Discussion covers the apparatus, the test steps, the evaluation indexes, data collection and analysis, animal performance and applications, advantages and disadvantages as well as precautions to eliminate bias wherever possible. We discussed recent studies on TBI-related preconditioning, biomarkers, and optimized behavioral protocols. The neuropsychologic tests employed in clinics were correlated with those used in mouse TBI models. In summary, this review provides a comprehensive, up-to-date reference for TBI researchers to choose the right neurobehavioral protocol according to the research objectives of their translational investigation.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".