Shape-related characteristics of age-related differences in subcortical structures
Bibliographic record
Abstract
Abstract OBJECTIVES With an increasing aging population, it is important to gain a better understanding of biological markers of aging. Subcortical volume is known to differ with age; additionally considering shape-related characteristics may provide a better index of age-related differences in subcortical structure. Recently fractal dimensionality has been shown to be more sensitive to age-related differences, but this measure is borne out of mathematical principles, rather than quantifying a neurobiologically relevant characteristic directly. We considered four distinct measures of shape and how they relate to aging and fractal dimensionality: surface-to-volume ratio, sphericity, long-axis curvature, and surface texture. METHODS Structural MRIs from two samples, with a combined sample size of over 600 healthy adults across the adult lifespan, were used to measure age-related differences in the structure of the thalamus, putamen, caudate, and hippocampus. For each structure, volume and fractal dimensionality were calculated, as well as each of the four distinct shape measures. These measures were then examined in their utility in explaining age-related variability in brain structure. RESULTS The four shape measures were able to account for 80-90% of the variance in fractal dimensionality, indicating that these measures were sensitive to the same shape characteristics. Of the distinct shape measures, surface-to-volume ratio was the most sensitive aging biomarker. CONCLUSION Though volume is often used to characterize inter-individual differences in subcortical structures, our results demonstrate that additional measures can be useful complements to volumetry. Our results indicate that shape characteristics of subcortical structures are useful biological markers of healthy 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 | 0.003 |
| 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) | 0.001 | 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".