Altered Superficial White Matter on Tractography MRI in Alzheimer's Disease
Bibliographic record
Abstract
BACKGROUND/AIMS: Superficial white matter provides extensive cortico-cortical connections. This tractography study aimed to assess the diffusion characteristics of superficial white matter tracts in Alzheimer's disease. METHODS: Diffusion tensor 3T magnetic resonance imaging scans were acquired in 24 controls and 16 participants with Alzheimer's disease. Neuropsychological test scores were available in some participants. Tractography was performed by the Fiber Assignment by Continuous Tracking (FACT) method. The superficial white matter was manually segmented and divided into frontal, parietal, temporal and occipital lobes. The mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AxD) and fractional anisotropy (FA) of these tracts were compared between controls and participants with Alzheimer's disease and correlated with available cognitive tests while adjusting for age and white matter hyperintensity volume. RESULTS: Alzheimer's disease was associated with increased MD (p = 0.0011), increased RD (p = 0.0019) and increased AxD (p = 0.0017) in temporal superficial white matter. In controls, superficial white matter was associated with the performance on the Montreal Cognitive Assessment, Stroop and Trail Making Test B tests, whereas in Alzheimer's disease patients, it was not associated with the performance on cognitive tests. CONCLUSION: Temporal lobe superficial white matter appears to be disrupted in Alzheimer's disease.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".