Sulcal morphology differences between mild cognitive impairment patients and normal elderly subjects
Bibliographic record
Abstract
Mild cognitive impairment (MCI) is an intermediate cognitive state between normal aging and dementia. Previous studies have found the atrophy of the gray matter and white matter in MCI compared to normal aging. However, the relatively few reports focused on the sulcal morphology in MCI subjects. Here, we investigated the changes of sulcal morphology in MCI and normal controls using quantitative surface-based method. We computed three dimensional gyrification indexes (3D-GI) of both cerebral hemispheres and four morphological metrics (the bottom length, top length, average depth and maximum depth) in nine prominent sulci per hemisphere, as well as the asymmetry index (AI) of these metrics. The relationships among those metrics and Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) scores in MCI patients were also investigated. We found that 3D-GI was not significantly different between MCI subjects and normal controls. Interestingly, we observed that the lengths and depths of the left superior and inferior frontal sulci in MCI subjects showed significant differences compared to the normal people. And the AI differences existed in the superior frontal, inferior frontal, and post-central, intra-parietal sulci. Taken together, our results showed sulcal morphology changes in MCI patients and therefore provided insights into the cognitive decline process.
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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".