Testing retrogenesis and physiological explanations for tract-wise white matter aging: links to developmental order, fibre calibre, and vascularization
Bibliographic record
Abstract
To understand the consistently observed spatial distribution of white-matter (WM) aging, developmentally driven theories termed "retrogenesis" have gained traction, positing that the order of WM tract development predicts the order of declines. Regions that develop first are expected to deteriorate the last, i.e. "last-in-first-out". Alternatively, regions which develop most rapidly may also decline most rapidly in aging, or "gains-predict-loss". The validity of such theories remains uncertain, in part due to lack of clarity on the definition of developmental order. Importantly, our recent findings suggest that WM aging is also associated with physiological parameters such as perfusion, which may be linked to fibre metabolic need, which in turn varies with fibre size. Here we address the extent to which the degree of WM aging is determined by development trajectory (i.e. retrogenesis) and/or by physiological state. We obtained microstructural and perfusion measures using data from the Human Connectome Project in Aging (HCP-A), complemented by a meta-analysis involving maps of fibre calibre and macrovascular volume. Our results suggest that (1) while tracts that appear last or finish myelinating first in development display the slowest aging, the pattern of aging is not fully explained by retrogenesis; in fact, time courses of tract emergence and myelination give rise to opposite associations with WM decline; (2) tracts that appear earlier also have higher mean axon calibre and are also associated with lower degrees of WM microstructural aging; (3) such tracts also tend to exhibit relatively sustained CBF with a higher rate of lengthening of the arterial transit times (ATT), suggestive of collateral blood supply. These findings were also sex dependent in a tract-specific manner. Future work will investigate whether these are ultimately influenced by each tract's metabolic demand and the role of macrovascular collateral flow.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".