MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer’s Disease
Bibliographic record
Abstract
Characterizing Alzheimer’s disease (AD) progression remains a significant clinical challenge. The initial stages of AD are marked by the accumulation of amyloid-beta plaques and Tau tangles, with cognitive functions often appearing normal, and clinical symptoms may not manifest until up to 20 years after the prodromal period begins. Comprehensive longitudinal studies analyzing brain-wide structural connectomics in the early stages of AD, especially those with large sample sizes, are scarce. In this study, we investigated a longitudinal diffusion-weighted imaging dataset of 264 subjects to assess the predictive potential of diffusion data for AD. Our findings indicate the potential of a simple prognostic biomarker for disease progression based on the hemispheric lateralization of mean tract volume for tracts originating from the supramarginal and paracentral regions, achieving an accuracy of 86%, a sensitivity of 86%, and a specificity of 93% when combined with other clinical indicators. However, diffusion-weighted imaging measurements alone did not provide strong predictive accuracy for clinical variables, disease classification, or disease conversion. By conducting a comprehensive tract-by-tract analysis of diffusion-weighted characteristics contributing to the characterization of AD and its progression, our research elucidates the potential of diffusion MRI as a tool for the early detection and monitoring of neurodegenerative diseases and emphasizes the importance of integrating multi-modal data for enhanced predictive analytics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".