NFB-04. EXAMINING DIFFUSION, ARTERIAL SPIN-LABELED PERFUSION, AND VOLUMETRIC CHANGES IN THE NEUROFIBROMATOSIS TYPE 1 BRAIN USING AN ATLAS-BASED, MULTI-PARAMETRIC APPROACH
Bibliographic record
Abstract
Abstract BACKGROUND Neurofibromatosis Type 1 (NF1) is a multisystem disorder with wide ranging clinical implications. Patients may present with macrocephaly, stroke, and cognitive deficits, all of which may impede normal neural development. We applied atlas-based, multi-parametric MRI analysis of regional brain to evaluate diffusion, arterial spin-labeled (ASL) perfusion, and volumetric changes in children with NF1. METHODS Children evaluated for NF1 from 2009 to 2018 at Stanford University (n=78) were retrospectively reviewed and compared to healthy controls (n=100). All patients underwent diffusion-weighted (DWI) magnetic resonance imaging at 3T, and children with brain tumors were excluded. Using atlas-based DWI analyses, we assessed volume, median apparent diffusion coefficient (ADC), and cerebral blood flow in the cerebral cortex, thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, nucleus accumbens, brain stem, and cerebral white matter. We also measured volume of the lateral ventricles. Multivariate analysis of covariance was used to test for differences between controls and NF1 patients, controlling for gender and age at time of imaging. RESULTS Comparing NF1 to controls, we detected increased volume and decreased ASL cerebral blood flow in white matter and all subcortical and cortical structures except for brainstem volume. Median ADC was also increased in the thalamus, pallidum, hippocampus, and brainstem. CONCLUSIONS Using a multi-parametric approach, we demonstrate quantitative measures of microstructural and physiologic changes of the NF1 brain. Atlas-based, quantitative MRI brain signatures may serve as biomarkers of neural development and further provide insight into associated cognitive dysfunction or risks for vasculopathy-related strokes in children with NF1.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".