In vivo myelin imaging and tissue microstructure in white matter hyperintensities and perilesional white matter
Bibliographic record
Abstract
Abstract Introduction White Matter hyperintensities (WMHs) relate to cognitive decline in aging, due to their deleterious effect on white matter structure. Diffusion tensor imaging (DTI) detects changes to white matter microstructure, both within the WMH and extending in a penumbra-like pattern in surrounding (perilesional) normal appearing white matter (NAWM). However, DTI markers are not specific to tissue components, complicating interpretation of previous DTI findings. Myelin water imaging is a novel imaging technique that provides specific markers of myelin content (myelin water fraction: MWF) and interstitial fluid (geometric mean T2: GMT2). Here we combined DTI and myelin water imaging to examine tissue characteristics in WMHs and perilesional NAWM. Our goal was to establish a multimodal neuroimaging approach to track WMH progression. Methods 80 individuals (47 older adults and 33 individuals with chronic stroke) underwent neuroimaging and tissue segmentation. To measure perilesional NAWM, WMH masks were dilated in 2mm segments up to 10mm in distance from the WMH to measure perilesional NAWM. Fractional anisotropy (FA), mean diffusivity (MD), MWF, and GMT2 were extracted from WMHs and perilesional NAWM. We examined whether white matter metrics showed a spatial gradient of effects in perilesional NAWM, as a function of Distance from the WMH, and Group. We tested whether white matter metrics in the WMH lesion related to severity of cerebrovascular disease across the whole sample, indexed by whole brain WMH volume. Results We observed a spatial gradient of higher MD and GMT2, and lower FA, in perilesional NAWM and the WMH. In the chronic stroke group, MWF was reduced in the WMH lesion but did not show a spatial gradient in perilesional NAWM. Across the whole sample, white matter metrics within the WMH lesion related to whole-brain WMH volume; MD and GMT2 increased, and MWF decreased, with increasing WMH volume. Conclusions NAWM adjacent to WMHs exhibits characteristics of a transitional stage between healthy NAWM tissue and WMH lesions. This was observed in markers sensitive to interstitial fluid, but not in the specific marker of myelin concentration (MWF). Within the WMH, interstitial fluid was higher and myelin concentration was lower in individuals with more severe cerebrovascular disease. Our data suggests that fluid-sensitive imaging metrics (such as DTI) can identify NAWM at high risk of conversion to a WMH. In contrast, specific markers of myelin concentration (e.g., MWF) can be used to measure levels of demyelination in the WMH itself, which may be a useful marker for disease staging of advanced WMHs.
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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 | 0.000 |
| 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) | 0.001 | 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".