Impaired Cerebrovascular Reactivity With Steal Phenomenon Is Associated With Increased Diffusion in White Matter of Patients With Moyamoya Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Reduced cerebrovascular reactivity (CVR) with steal phenomenon is an independent predictor for stroke and may indicate tissue exposed to episodic low-grade ischemia. The apparent diffusion coefficient (ADC) calculated using diffusion-weighted MRI is effective in characterizing focal brain ischemia and subtle structural changes in normal-appearing white matter (WM). We hypothesized that regions of steal phenomenon are associated with increased ADC in normal-appearing WM of patients with Moyamoya disease. METHODS: Twenty-two patients with unilateral CVR impairment secondary to Moyamoya disease and 12 healthy control subjects underwent diffusion-weighted MRI and functional MRI mapping of the cerebrovascular response to hypercapnia. Parametric maps of ADC and CVR were calculated, coregistered, and segmented using automated image processing methods. ADC of normal-appearing WM was compared between hemispheres, and between WM with negative CVR (ie, steal phenomenon) and WM with positive CVR. RESULTS: In patients, ADC of normal-appearing WM was elevated in the hemisphere ipsilateral to the CVR impairment compared with the contralateral hemisphere (P<0.005) and in WM with negative CVR compared with WM with positive CVR (P<0.001). WM in regions of steal phenomenon within the affected hemisphere had higher ADC than homologous contralateral WM (P<0.005). In control subjects, negative CVR in WM was not associated with elevated ADC. CONCLUSIONS: Regions of steal phenomenon are spatially correlated with elevated ADC in normal-appearing WM of patients with Moyamoya disease. This structural abnormality may reflect low-grade ischemic injury after exhaustion of the cerebrovascular reserve capacity.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".