Multivariate morphometry statistics reveal the morphological change pattern of hippocampus during normal aging
Bibliographic record
Abstract
There have been numerous studies focusing on normal aging in previous decades which is accompanied by the structural and functional decline in the hippocampus, while the pattern of hippocampal alteration with age remains unclear. Figuring out the mechanism of hippocampal changes precisely is beneficial for a better understanding of the aging process. In this study, we included a total of 451 T1 MRI scans of subjects of age 50-90 who were labeled as normal in the Alzheimer's Disease Neuroimaging Initiative. Taking 10 years of age as an age band, we divided the subjects into four groups (denoted as HC1, HC2, HC3, and HC4, respectively), with the youngest being 50-60 and the oldest 81-90. Then the Multivariate Morphometry Statistics (MMS) of the hippocampus segmented from the four groups were extracted by surface reconstruction, mesh generation, and surface registration. Finally, the significant differences between the youngest group and the other three were statistically analyzed. Results showed that the earliest deformation region of the left hippocampus located in the frontal subiculum and the dorsal CA1 of the tail part and gradually expanded with aging, while the right hippocampal deformation mainly concentrated in the dorsal CA1 and spread to the posterior CA2-3, which occurred later than that of the left. All the results illustrated that the hippocampus is truly a vulnerable structure in the course of aging, and the MMS are sensitive metrics for detecting the changes in the subcortical convex structure.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".