High resolution diffusion tensor imaging of the hippocampus across the healthy lifespan
Bibliographic record
Abstract
The human hippocampus is difficult to image given its small size, location, shape, and complex internal architecture. Structural magnetic resonance imaging (MRI) has shown age-related hippocampal volume changes that vary along the anterior-posterior axis. Diffusion tensor imaging (DTI) provides complementary measures related to microstructure, but there are few hippocampus DTI studies investigating change with age in healthy participants, and all have been limited by low spatial resolution. The current study uses high resolution 1 mm isotropic DTI of 153 healthy volunteers aged 5-74 years to investigate diffusion and volume trajectories of the hippocampus (whole, head, body, and tail) and correlations with memory. Hippocampal volume showed age-related changes that differed between head (peaking at midlife), body (no changes), and tail (decreasing across the age span). Fractional anisotropy (FA) and mean, axial, and radial diffusivities (MD, AD, RD) yielded peaks or minima, respectively, at ~30-35 years in all three subregions of the hippocampus. Greater magnitude changes were observed during development than in aging. Age trajectories for both volume and DTI were similar between males and females. Correlations between tests of memory and FA and/or volume were significant in younger subjects (5-17 years), but not in 18-49 year olds or 50-74 year olds. MD was significantly correlated with memory performance in 18-49 year olds, but not in other age groups. Given the diffusion-weighted image contrast and resolution, head digitations could be examined revealing that the majority of subjects had 3-4 (48%) or 2 (32%) bilaterally with no effect of age. One millimeter isotropic DTI yielded high quality diffusion-weighted maps of the human hippocampus that showed regionally specific age effects and cognitive correlations along the anterior-posterior axis from 5 to 74 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".