Impact of abnormal cerebrovascular reactivity on <scp>BOLD fMRI</scp>: a preliminary investigation of moyamoya disease
Bibliographic record
Abstract
Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies of patients with cerebrovascular disease have largely ignored the confounds associated with abnormal cerebral blood flow, vascular reactivity and neurovascular coupling. We studied BOLD fMRI activation and cerebrovascular reactivity in moyamoya disease. To characterize the impact of remote vascular demands on BOLD fMRI measurements, we varied the vascular territories engaged by manipulating the experimental task performed by the participants. Vascular territories affected by disease were identified using BOLD cerebrovascular reactivity. Preliminary evidence from two patients pre- and postrevascularization surgery and four controls indicates that neurovascular coupling in affected brain regions can be modulated by the task-related vascular demands in unaffected regions. In one patient studied, we observed that brain regions with improved cerebrovascular reactivity after surgery demonstrated normalized neurovascular coupling, that is the degree to which neurovascular coupling was modulated by task-related vascular demands was decreased. We propose that variations in task-dependent neurovascular coupling in patients with moyamoya disease are likely related to vascular steal. While preliminary, our findings are a proof of concept of the limitations of BOLD fMRI in cerebrovascular disease and suggest a role for assessment of cerebrovascular reactivity to improve interpretation of task-related BOLD fMRI activation.
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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".