Morphometric and Histologic Substrates of Cingulate Integrity in Elders with Exceptional Memory Capacity
Bibliographic record
Abstract
This human study is based on an established cohort of "SuperAgers," 80+-year-old individuals with episodic memory function at a level equal to, or better than, individuals 20-30 years younger. A preliminary investigation using structural brain imaging revealed a region of anterior cingulate cortex that was thicker in SuperAgers compared with healthy 50- to 65-year-olds. Here, we investigated the in vivo structural features of cingulate cortex in a larger sample of SuperAgers and conducted a histologic analysis of this region in postmortem specimens. A region-of-interest MRI structural analysis found cingulate cortex to be thinner in cognitively average 80+ year olds (n = 21) than in the healthy middle-aged group (n = 18). A region of the anterior cingulate cortex in the right hemisphere displayed greater thickness in SuperAgers (n = 31) compared with cognitively average 80+ year olds and also to the much younger healthy 50-60 year olds (p < 0.01). Postmortem investigations were conducted in the cingulate cortex in five SuperAgers, five cognitively average elderly individuals, and five individuals with amnestic mild cognitive impairment. Compared with other subject groups, SuperAgers showed a lower frequency of Alzheimer-type neurofibrillary tangles (p < 0.05). There were no differences in total neuronal size or count between subject groups. Interestingly, relative to total neuronal packing density, there was a higher density of von Economo neurons (p < 0.05), particularly in anterior cingulate regions of SuperAgers. These findings suggest that reduced vulnerability to the age-related emergence of Alzheimer pathology and higher von Economo neuron density in anterior cingulate cortex may represent biological correlates of high memory capacity in advanced old age.
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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) | 0.001 | 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".