Developing Brain-Strain-Based Scaling to Inform the Clinical Relevance of Mouse Models of Concussion Induced by Rotation
Bibliographic record
Abstract
Abstract Background Laboratory animal experiments are an invaluable tool for studying mild traumatic brain injury (mTBI)/concussion. Among them, rodent neurotrauma experiments have been most widely used, as transgenic and gene targeting technologies in mice allow us to test the roles of different genes in recovery from brain injury. Furthermore, the clinical relevance of rodent concussion studies can be improved by using these technologies to study concussions in animals that carry the human versions of genes known to play a role in neurological disease. However, delivering concussion injuries to the mice that are relevant to real-world human head impacts is challenging, as the mouse and human heads are dramatically different in shape and size. In the vast majority of mouse concussion experiments, the pathological and behavioral consequences of the injuries are evaluated without considering whether the injury model produces brain stretches (maximum principal strains) of the same magnitude as those experienced by human brains undergoing similar impacts. Methods We conducted a total of 201 computational simulations to understand both human and mouse brain strains that are directly linked to neuronal damage during closed-head concussive impacts. To represent real-world human head impacts we simulated mouse head impacts with durations of 1.5 ms (Type 1 scaling), followed by simulations with durations between 1 and 2 ms (Type 2), and finally, simulations with durations from 0.75 to 4.5 ms (Type 3) to develop scaling between human and mouse, as well as to reveal the predicted effects of small and large changes in impact durations on brain strain. Results Guided by these simulations we calculated that peak rotational velocities in mice could be achieved by scaling human peak rotational velocities with factors of 5.8, 4.6, and 6.8, for flexion/extension, lateral bending, and axial rotation, respectively, to reach equal brain strains between human and mouse. The effects of impact durations on scaling were also calculated and longer-duration mouse head impacts needed larger scaling factors to reach equal strain. Conclusions The scaling method will help us to create brain injury in the mouse with brain strain loading equivalent to those experienced in real-world human head impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".