Modelling the microstructural effects of white matter hyperintensities
Bibliographic record
Abstract
Leukoaraiosis, or white matter hyperintensities (WMH), are lesions within white matter (WM) characterized by altered tissue microstructure, manifesting as changes in magnetic resonance imaging (MRI) metrics of water diffusion.Conventional MRI research compares metrics in WMH to those of all normal-appearing white matter (NAWM), ignoring potential distance-dependent effects of WMH.However, histopathological evidence suggests that the presence of WMH influences the microstructure of surrounding NAWM, with conflicting interpretations attributing these changes to inflammatory or degenerative processes.The present thesis aimed to characterize distinct patterns of microstructural change occurring in perilesional voxels and in WM tracts containing WMH.We leveraged the neurite orientation dispersion index (NODDI) model in large-scale, diffusion MRI data (N = 408) to differentiate changes in intraand extracellular water fractions (ICVF, ISOVF), as well as orientation dispersion (OD).To assess spatial variation, we generated a spatial gradient expanding from the WMH and fit Linear Mixed-Effects Models (LMMs) using diffusion parameters as continuous outcomes predicted by the distance from the WMH.We observed increases in ICVF and OD as the distance increased, suggesting that tissue microstructure was more disorganized in perilesional voxels.Utilizing probabilistic tractography to reconstruct WM regions, we identified microstructural abnormalities in regions containing WMH compared to contralateral, non-lesion regions.We further demonstrated that the proportion of WMH within a region predicted parameter changes, notably in ICVF and OD.When investigating the association of WMH proportion with parameter change, we demonstrate that even small lesions (<100mm 3 ) were significantly correlated with changes in the ICVF and OD.Our complementary analyses suggest a
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".