Cerebral stiffness changes during visual stimulation: Differential physiological mechanisms characterized by opposing mechanical effects
Bibliographic record
Abstract
We performed functional intrinsic Magnetic Resonance Elastography (fiMRE) as well as Time of Flight angiography and BOLD fMRI on 7 healthy human subjects to monitor shear stiffness and arterial dilation during periods of prolonged visual stimulation. FiMRE activation with increased stiffness was observed to occur almost equally within brain white and gray matter (49.7±14.0% and 40.9±12.2%, respectively), while activation with decreased stiffness was significantly (p=0.018) more likely to occur in white matter than gray matter (50.8±11.4% and 37.0±4.5%, respectively). At the low mechanical activation and block design frequencies used in this intrinsic MRE (iMRE) approach, the aggregate stiffness change across the entire BOLD activation region was not significant. However, we observed significant reduction in shear stiffness (1.40 ± 0.15 to 0.68 ± 0.22 [kPa], p < 0.001) in areas adjacent to the Posterior Cerebral Artery, where vasodilation is evident, in the V1 region. In addition, we observed significant shear stiffness increase (1.29 ± 0.12 to 0.62 ± 0.16 [kPa], p < 0.001) in areas adjacent to the Middle Temporal or V5 region of the visual cortex. These results show that iMRE can measure intrinsic cerebro-mechanical reactions due to visual stimulation as well as the differential physiological response detected in distinct regions of the visual cortex.
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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 | 0.001 |
| 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".