Iterative analysis of cerebrovascular reactivity dynamic response by temporal decomposition
Bibliographic record
Abstract
Abstract Objective To improve quantitative cerebrovascular reactivity ( CVR ) measurements and CO 2 arrival times, we present an iterative analysis capable of decomposing different temporal components of the dynamic carbon dioxide‐ Blood Oxygen‐Level Dependent ( CO 2 ‐ BOLD ) relationship. Experimental Design Decomposition of the dynamic parameters included a redefinition of the voxel‐wise CO 2 arrival time, and a separation from the vascular response to a stepwise increase in CO 2 (Delay to signal Plateau – DTP ) and a decrease in CO 2 (Delay to signal Baseline – DTB ). Twenty‐five (normal) datasets, obtained from BOLD MRI combined with a standardized pseudo‐square wave CO 2 change, were co‐registered to generate reference atlases for the aforementioned dynamic processes to score the voxel‐by‐voxel deviation probability from normal range. This analysis is further illustrated in two subjects with unilateral carotid artery occlusion using these reference atlases. Principal Observations We have found that our redefined CO 2 arrival time resulted in the best data fit. Additionally, excluding both dynamic BOLD phases ( DTP and DTB ) resulted in a static CVR , that is maximal response, defined as CVR calculated only over a normocapnic and hypercapnic calibrated plateau. Conclusion Decomposition and novel iterative modeling of different temporal components of the dynamic CO 2 ‐ BOLD relationship improves quantitative CVR measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".