Diffusion MRI-based measures of neurite microstructure associate with future risk of Alzheimer’s Disease
Bibliographic record
Abstract
ABSTRACT INTRODUCTION Early detection of Alzheimer’s disease (AD) is crucial for intervention, but traditional MRI and cognitive assessments may miss pre-symptomatic changes. Advanced diffusion MRI (dMRI) methods, such as Neurite Orientation Dispersion and Density Imaging (NODDI), show promise in identifying early brain changes. METHODS We analyzed 65 cognitively unimpaired older adults (25 APOE-e4 carriers, 40 non-carriers) from the ADNI3 dataset. NODDI’s neurite density index (NDI) and orientation dispersion index (ODI), volumetric MRI and cognitive performance (MoCA) were analyzed in key brain regions like the hippocampus, fusiform gyrus, and entorhinal cortex. Statistical analyses included linear regression and t-tests, with FDR correction. RESULTS NDI differed significantly between carriers and non-carriers and correlated with MoCA scores. ODI differed only in the CA1 hippocampal subfield. Volumetric MRI measures showed no group differences. DISCUSSION NODDI metrics, particularly NDI, could help detect early APOE-e4-related microstructural changes, while traditional volumetric MRI measures remain uninformative at early stages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".