The Role of MRI Biomarkers and Their Interactions with Cognitive Status and APOE ε4 in Nondemented Elderly Subjects
Bibliographic record
Abstract
PURPOSE: (1) To investigate atrophy patterns of hippocampal subfield volume and Alzheimer's disease (AD)-signature cortical thickness in mild cognitive impairment (MCI) patients; (2) to explore the association between the neuropsychological (NP) and the brain structure in the MCI and older normal cognition group; (3) to determine whether these associations were modified by the apolipoprotein E (APOE) ε4 gene and cognitive status. METHODS: The FreeSurfer software was used for automated segmentation of hippocampal subfields and AD-signature cortical thickness for 22 MCI patients and 23 cognitive normal controls (NC). The volume, cortical thickness, and the neuropsychological scale were compared with two-sample t tests. Linear regression models were used to determine the association between the NP and the brain structure. RESULTS: Compared with the NC group, MCI patients showed a decreased volume of the left presubiculum, subiculum and right CA2_3 and CA4_DG (p < 0.05, FDR corrected). The volume of these regions was positively correlated with NP scores. Of note, these associations depended on the cognitive status but not on the APOE ε4 status. The left subiculum and presubiculum volume were positively correlated with the Montreal Cognitive Assessment (MoCA) scores only in the MCI patients. CONCLUSION: Atrophy of the hippocampal subfields may be a powerful biomarker for MCI in the Chinese population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".