Quantitative MRI of the hippocampus reveals microstructural trajectories of aging and Alzheimer’s disease pathology
Bibliographic record
Abstract
Hippocampal degeneration is a feature of both normal aging and Alzheimer's disease (AD). Prior to macroscopic degeneration, microstructural changes occur such as demyelination, iron deposition, or subtle atrophy, which can be characterized in vivo using MRI. We topographically mapped measures of microstructure and macrostructure across the unfolded surface of the hippocampus in 224 healthy older adults at risk for AD (aged 57 to 87) and 37 younger adults (aged 18 to 37). We describe three spatial regions of unique structural covariance between four parameters sensitive to microstructural tissue properties (R1, MTsat, R2*, PD) and macrostructure (surface thickness), with high convergence with previous spatial segmentations. We demonstrate both cross-sectional and longitudinal associations of microstructure with healthy aging across the lifespan, AD pathological hallmarks, genetic risk, and cognition. These associations had subtle variations across different spatial areas of the hippocampus. We report associations between age and qMRI measures sensitive to macromolecular concentration (R1, MTsat) and paramagnetic susceptibility (R2*), consistent with mechanisms of demyelination and increased iron deposition as key hallmarks of the aging hippocampus and in presymptomatic stages of AD. qMRI measures did not explain more variance in delayed recall than global PET measures, suggesting variation in cognitive performance in aging and incipient AD is influenced by factors beyond hippocampal microstructure. We demonstrate the utility of quantitative MRI to provide greater insight into hippocampal health compared to typical macrostructural measures through "in vivo histology," opening a window to understanding neuropathological mechanisms in the earliest stages of age- and disease-related neurodegeneration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".