Uncovering patterns of white matter degeneration in normal aging: Links between morphometry and microstructure
Bibliographic record
Abstract
While tract-wise differences in volume and microstructure are common targets of investigation in age-related changes in the white matter (WM), there has been relatively little exploration into other attributes of tract morphometry or its relation to microstructure in vivo, and limited understanding on how they jointly inform the understanding of the WM aging trajectory. This study examines 10 WM tracts for tract-wise differences in morphometry (i.e., volume, length, and volume-to-length ratio) and microstructural integrity (i.e., fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) using diffusion MRI data from the Human Connectome Project in Aging (HCP-A) with the goal of laying the foundation for a more comprehensive model of age-related WM microstructure-morphometry trajectories with a special focus on age-shifted correlations and sex differences. Results indicated that degeneration in microstructure was detectable at younger ages than changes in morphometry, with widely heterogeneous patterns of interrelation and morphometry-microstructural associations in aging both across tracts and between sexes. Multi-parametric signatures of decline suggest differing stages or mechanisms of degeneration across tracts, with female subjects exhibiting a higher proportion of tracts in later stages of decline than males. This work highlights the value of integrating microstructural and morphometric measures of WM health, and encourages the integration of yet more modalities in improving our mechanistic understanding of WM aging.
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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 | 0.001 |
| 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".