Abstract TP168: Quantitative Cerebrovascular Reactivity Atlas In Children
Bibliographic record
Abstract
Introduction: Cerebrovascular reactivity (CVR) can be measured by inducing carbon dioxide (CO 2 ) changes in the circulation and measuring subsequent changes in blood flow with functional MRI blood oxygen level dependent sequences (BOLD). In clinical practice, abnormalities in CVR imaging can serve as a biomarker of ischemic risk. To characterize pathological aberrations in CVR, it is essential to have quantitative reference data as to what constitutes normal CVR across brain regions at various stages of development. Methods: Prospective enrollment of healthy children ages 6 to 18. Target N of 40. Two datasets are being generated within the atlas using two different methods of administering the vasoactive stimulus (CO 2 ): a breath-hold CVR dataset (endogenous stimulus) and a RespirAct TM CVR dataset (exogenous). A test of normality is performed on CVR values for each voxel across subjects, with mean and standard deviation calculations. This will allow for generation of a Z-score for each CVR value per voxel in any clinical CVR study. Voxel-wide validation of breath-hold CVR values against the gold standard RespirAct TM CVR values will be performed using within subject t-test and between group comparisons. Subgroup analysis based on gender and age will also be performed. Results: Preliminary CVR data for 8 healthy right-handed children is presented (4 female, median age 13, range 9 -14 years). All participants tolerated CVR studies well and no side effects were reported. Group datasets acquired to date passed tests of normality with no significant differences in CVR values between the two methods found. Conclusions: This study will generate the first atlas of quantitative CVR-fMRI data in healthy children. Normative physiological data on CVR changes throughout childhood, and associations between CVR, blood pressure, age, and gender will be obtained. CVR imaging is performed clinically in patients with a risk of recurrent arterial ischemic stroke such as those with intracranial arteriopathies, sickle cell disease and other genetic and metabolic disorders. Our atlas can provide clinicians with objective measures regarding extent and distribution of CVR abnormalities, allow for stroke risk stratification, and aid in determining the optimal time for intervention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".