P3‐238: Associations Between Quantitative Tractography at 3T MRI and Cognitive Function in Alzheimer’s Disease
Bibliographic record
Abstract
This tractography study aimed to assess the diffusion characteristics of white matter tracts in Alzheimer’s disease and their cognitive correlates. Diffusion tensor 3T MRI scans were acquired in twenty-four cognitively normal controls and sixteen participants with Alzheimer’s disease. Participants completed neuropsychological testing including the Montreal Cognitive Assessment, Mini-Mental State Exam, Stroop test, Trail Making Test B, Letter Number Sequencing and Wechsler Memory Scale-III Longest span forward and Longest span backward. Tractography was performed by the Fiber Assignment by Continuous Tracking method. The superficial white matter, corpus callosum, cingulum, long association fibers, corticospinal/bulbar tracts, thalamic fibers, and cerebellar fibers were manually segmented. The fractional anisotropy (FA) and mean diffusivity (MD) of these tracts were quantified and compared between cognitively normal controls and participants with Alzheimer’s disease. In participants with Alzheimer’s disease we correlated cognitive test scores and the MD and FA of tracts. Alzheimer’s disease was associated with greater MD in the superficial white matter tracts (AD: 0.001168±0.000218, controls: 0.001018±0.000150, p=0.011), cingulum (AD: 0.000848±0.000098, controls: 0.000794± 0.000072, p= 0.045) and association fibers (AD: 0.000824± 0.000052, controls: 0.000774±0.000049, p=0.003) and decreased FA in the corpus callosum (AD: 0.560±0.043, controls: 0.593±0.048, p=0.031). In the cingulum, increased MD was associated with worse performance on Trail Making Test B (p=0.034) and Longest span backward (p=0.021) and decreased FA was associated with worse performance on the Mini-Mental State Exam (p=0.042). In the corpus callosum, increased MD was associated with worse performance on Longest span forward (p=0.013). In Alzheimer’s disease, quantitative tractography can detect abnormalities in superficial white matter, cingulum, corpus callosum and association fibers and its measures can relate to cognitive function.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".