Regional Ischemic Vulnerability of the Brain to Hypoperfusion
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To characterize the spatial pattern of cerebral ischemic vulnerability to hypoperfusion in stroke patients. METHODS: We included 90 patients who underwent admission CT perfusion and MRI within 12 hours of ischemic stroke onset. Infarcted brain lesions ("core") were segmented from admission diffusion-weighted imaging and, along with the CT perfusion parameter maps, coregistered onto MNI-152 brain space, which was parcellated into 125 mirror cortical and subcortical regions per hemisphere. We tested the hypothesis that the percent infarction increment per unit of relative cerebral blood flow (rCBF) reduction differs statistically between regions using regression analysis to assess the interaction between regional rCBF and region variables. Next, for each patient, a "vulnerability index" map was constructed with voxel values equaling the product of that voxel's rCBF and infarction probability (derived from the MNI-152-transformed, binary, segmented, diffusion-weighted imaging lesions). Voxel-based rCBF threshold for core was determined within the upper 20th percentile of vulnerability index map voxel values. RESULTS: Different regions had different percent infarction increase per unit rCBF reduction (P=0.001). The caudate body, putamen, insular ribbon, paracentral lobule, and precentral, middle, and inferior frontal gyri had the highest ischemic vulnerability to hypoperfusion. A voxel-based rCBF threshold of <0.42 optimally distinguished infarct core in the highly-vulnerable regions, whereas rCBF<0.16 distinguished core in the remainder of the brain. CONCLUSIONS: We demonstrated regional ischemic vulnerability of the brain to hypoperfusion in acute stroke patients. Location-specific, rather than whole-brain, rCBF thresholds may provide a more accurate metric for estimating infarct core using CT perfusion maps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".