Investigating the Regional, Directional, and Rate-Dependent Mechanical Response of Fixed Human Brain Tissue Under Compression
Bibliographic record
Abstract
Assuming homogeneous mechanical properties for all brain tissue in computational simulations may lead to inaccurate predictions of response, affecting conclusions about brain injury mechanisms, prevention, and treatment. This study investigated the effect of tissue location, loading direction, and strain rate on the mechanical properties of brain tissue. Digital Image Correlation (DIC) analysis was used to quantify the stress response, Poisson's Ratio (PR), and volume ratio of human brain tissue under uniaxial compression. The directional, regional, and strain rate dependent properties of white matter from the corpus callosum and gray matter from the temporal lobe cortex were investigated. Higher strain rate and compression magnitude increased the tissue stress response across all brain regions and loading directions. The PR of all tissues varied with compression magnitude. The temporal lobe exhibited isotropic deformation behavior, aligning with homogeneous incompressible material behavior. In the corpus callosum, directionally dependent PR suggested transverse isotropy. For both tissue locations, the volume ratio investigation showed deviations from incompressibility as strain rate and compressive strain magnitude increased; these deviations can result in large stored-energy penalties due to the high bulk modulus of brain tissue. Integrating region- and direction-specific mechanical properties into brain tissue models could improve insights into complex load transfer mechanisms within the brain, potentially refining clinical strategies for brain injury intervention and prevention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".