Post-mortem 7 Tesla MRI detection of white matter hyperintensities: A multidisciplinary voxel-wise comparison of imaging and histological correlates
Bibliographic record
Abstract
White matter hyperintensities (WMH) occur in normal aging and across diagnostic categories of neurodegeneration. Ultra-high field imaging (UHF) MRI machines offer the potential to improve our understanding of WMH. Post-mortem imaging using UHF magnetic resonance imaging (MRI) is a useful way of assessing WMH, however, the responsiveness of UHF-MRI to pathological changes within the white matter has not been characterized. In this study we report post-mortem MRI sequences of white matter hyperintensities in normal aging, Alzheimer's disease, and cerebrovascular disease. Seven Tesla post-mortem MRI reliably detected periventricular WMH using both FLAIR and T2 sequences and reflects underlying pathology of myelin and axon density despite prolonged fixation time. Co-registration of histological images to MRI allowed for direct voxel- wise comparison of imaging findings and pathological changes. Myelin content and cerebrovascular pathology were the most significant predictors of MRI white matter intensity as revealed by linear mixed models. Future work investigating the utility of UHF- MRI in studying cell-specific changes within WMH is required to better understand radio-pathologic correlations.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".