Early Diffusion Magnetic Resonance Imaging Changes in Normal-Appearing Brain in Pediatric Moyamoya Disease
Bibliographic record
Abstract
BACKGROUND: Moyamoya disease often leads to ischemic strokes visible on diffusion-weighted imaging (DWI) and T2-weighted magnetic resonance imaging (MRI) with subsequent cognitive impairment. In adults with moyamoya, apparent diffusion coefficient (ADC) is correlated with regions of steal phenomenon and executive dysfunction prior to white matter changes. OBJECTIVE: To investigate quantitative global diffusion changes in pediatric moyamoya patients prior to explicit structural ischemic damage. METHODS: We retrospectively reviewed children (<20 yr old) with moyamoya disease and syndrome who underwent bypass surgery at our institution. We identified 29 children with normal structural preoperative MRI and without findings of cortical infarction or chronic white matter ischemic changes. DWI datasets were used to calculate ADC maps for each subject as well as for 60 age-matched healthy controls. Using an atlas-based approach, the cerebral white matter, cerebral cortex, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, nucleus accumbens, and brainstem were segmented in each DWI dataset and used to calculate regional volumes and ADC values. RESULTS: Multivariate analysis of covariance using the regional ADC and volume values as dependent variables and age and gender as covariates revealed a significant difference between the groups (P < .001). Post hoc analysis demonstrated significantly elevated ADC values for children with moyamoya in the cerebral cortex, white matter, caudate, putamen, and nucleus accumbens. No significant volume differences were found. CONCLUSION: Prior to having bypass surgery, and in the absence of imaging evidence of ischemic stroke, children with moyamoya exhibit cerebral diffusion changes. These findings could reflect microstructural changes stemming from exhaustion of cerebrovascular reserve.
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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 | 0.001 | 0.001 |
| 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".