Viscoelastic mapping of mouse brain tissue: Relation to structure and age
Bibliographic record
Abstract
There is growing evidence that mechanical factors affect brain functioning. However, brain components responsible for regulating the physiological mechanical environment are not completely understood. To determine the relationship between structure and stiffness of brain tissue, we performed high-resolution viscoelastic mapping by dynamic indentation of the hippocampus and the cerebellum of juvenile mice brains, and quantified relative area covered by neurons (NeuN-staining), axons (neurofilament NN18-staining), astrocytes (GFAP-staining), myelin (MBP-staining) and nuclei (Hoechst-staining) of juvenile and adult mouse brain slices. Results show that brain subregions have distinct viscoelastic parameters. In gray matter (GM) regions, the storage modulus correlates negatively with the relative area of nuclei and neurons, and positively with astrocytes. The storage modulus also correlates negatively with the relative area of myelin and axons (high cell density regions are excluded). Furthermore, adult brain regions are ∼ 20%-150% stiffer than the comparable juvenile regions which coincide with increase in astrocyte GFAP-staining. Several linear regression models are examined to predict the mechanical properties of the brain tissue based on (immuno)histochemical stainings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".