Post‐mortem MR imaging of tau pathology – a pilot study
Bibliographic record
Abstract
Abstract Background In‐vivo detection of neuropathology is critical for early diagnosis of neurodegenerative diseases. Post‐mortem brain magnetic resonance imaging (MRI) of pathological protein inclusions could further our ability to detect them in vivo and correlate MRI parameters to histopathological substrates. In this post‐mortem study, we aimed to identify MRI correlates of neurodegenerative disease pathology in a brain with various forms of proteinopathies. Method One large cortical section containing the temporal cortex, hippocampus and thalamus was used from an 82‐year‐old male patient, with chronic traumatic encephalopathy (CTE), Alzheimer’s disease (AD), TDP‐43, aging related tau astrogliosis (ARTAG) and cerebral amyloid angiopathy (CAA) pathologies. The post‐mortem tissue sample was scanned using high‐resolution Rapid Acquisition with Relaxation Enhancement (RARE) and multi‐gradient echo (MGE) sequences on an 11.7 Tesla MRI (Bruker, Germany). RARE scan parameters were as follows: field of view: 97.2 x 86.4 x 28.8 mm, rep time: 2 s, RARE factor: 8, echo time: 48 ms, echo spacing: 12 ms, 225 μm isotropic resolution. Tissue samples were subsequently sectioned (0.004 mm thick slices) and stained using AT8 – for Tau, GFAP – for astrogliosis and H&E/Luxol – for distinguishing gray (GM) and white matter (WM) and PERLS – for extracellular iron. Result Post‐mortem MRI allowed for clear visualization of WM and GM structures. Decreased cell density in WM as visualised on H&E stained slices was associated with hyperintensities on T2w images and decreased measured R2*. AT8 immunoreactivity in GM and WM was associated with hyperintensities on T2w images and decreased measured R2*. Conclusion High‐resolution MRI of post‐mortem tissue allowed for visualization of fine WM‐GM details, as well as hyperintensities in areas that potentially correspond to increased densities of tau pathology and decreased WM density. Further studies are needed to investigate the potential for high‐resolution post‐mortem MRI to visualize the pathological process in neurodegenerative diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".