P2‐406: INVESTIGATING THE SENSITIVITY OF FREE‐WATER IMAGING IN DETECTING WHITE‐MATTER ABNORMALITIES WITHIN PATIENTS WITH ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Free-water imaging (FWI) is an analytic technique for diffusion-weighted magnetic resonance imaging (dMRI) and has been proposed as a marker for neuroinflammation in neurodegenerative diseases. However, whether FWI could be used as a sensitive technique in understanding white-matter (WM) abnormalities within patients with Alzheimer's disease (AD) who are stable (AD-Stable) and those deteriorating rapidly in their cognitive abilities within a year (AD-Converters) is currently unknown. Data was collected from 9 AD-Stable and 8 AD-Converters participating in a longitudinal study of aging. Multi-shell dMRI with 71 directions each at b-values of 500s/mm, 1000s/mm, and 2500s/mm and isotropic 1.5mm resolution were acquired for each participant. Both conventional single-tensor (ST) fitting and single-tensor fitting with FW estimates utilizing the data from the three-shell simultaneously were performed in-house. Tract-based Spatial Statistics (TBSS) was used to extract the WM skeleton from ST-fractional anisotropy (FA), and various diffusion-derived metrics such as FA, axial diffusivity (AxD), mean diffusivity (MD), and radial diffusivity (RD) along with FW within this skeleton were compared between the groups, and also tested for association with the Montreal Cognitive Assessment (MoCA) using non-parametric statistics. Significance was established at familywise error corrected to p<0.05. No significant difference in any diffusion-derived metrics was observed without FW-correction. Significantly higher MD was observed for AD-Stable after FW-correction (MDFW), notably, in the regions of superior corona radiata, superior longitudinal fasciculus, and splenium of the corpus callosum. ADFW showed a positive correlation with the MoCA within AD-Stable participants (Fig.1) in the same WM tracts encompassing the anterior thalamic radiation, corticospinal tract, and corpus callosum.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".