Gray matter blood flow and volume are reduced in association with white matter hyperintensity lesion burden: a cross-sectional MRI study
Bibliographic record
Abstract
Cerebral White Matter Hyperintensities (WMH) are associated with vascular risk factors and age-related cognitive decline. WMH have primarily been associated with global white matter and gray matter (GM) changes and less is known about regional effects in GM. The purpose of this study was to test for an association between WMH and two GM imaging measures: cerebral blood flow (CBF) and voxel-based morphometry (VBM). Twenty-six elderly adults with mild to severe WMH participated in this cross-sectional 3 Tesla magnetic resonance imaging (MRI) study. MRI measures of GM CBF and VBM were derived from arterial spin labeling (ASL) and T1-weighted images, respectively. Fluid-attenuated inversion recovery (FLAIR) images were used to quantify the WMH lesion burden (mL). GM CBF and VBM data were used as dependent variables. WMH lesion burden, age and sex were used in a regression model. Visual rating of WMH with the Fazekas method was used to compare the WMH lesion volume regression approach. WMH volume was normally distributed for this group (mean volume of 22.7 mL, range: 2.2-70.6 mL). CBF analysis revealed negative associations between WMH volume and CBF in the left anterior putamen, subcallosal, accumbens, anterior caudate, orbital frontal, anterior insula, and frontal pole (corrected p < 0.05). VBM analysis revealed negative associations between WMH and GM volume in lingual gyrus, intracalcarine, and bilateral hippocampus (corrected p < 0.05). The visual rating scale corroborated the regression findings (corrected p < 0.05). WMH lesion volume was associated with intra-group GM CBF and structural differences in this cohort of WMH adults with mild to severe lesion burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".