APOE ε4 allele modified the correlation between deep grey matter volume and cognitive performance in non-demented elders
Bibliographic record
Abstract
Abstract APOE ε4 allele is the strongest predictor of Alzheimer’s disease (AD) risk, but its role in the association between the deep grey matter volume and cognitive impairment is still unclear. This study investigated the effects of APOE ε4 allele on this association in non-demented elders. We enrolled 24 patients with mild cognitive impairment (MCI) and 28 normal controls (NC), who underwent the whole brain 3DTIW MRI scanning, an APOE genotype test, and neuropsychological tests. The right thalamus (p = 0.026), the left pallidum (p = 0.026), and the bilateral amygdala (left p = 0.042, right p = 0.048) atrophied in MCI, and their volume were positively correlated with the cognitive scores (MoCA) (p < 0.05). Furthermore, the general liner regression model suggested that the correlation between the right thalamus and the putamen volume with MoCA scores was different in the APOE ε4 carriers and non- carriers. Compared with the non APOEε4 carriers, the right thalamus atrophied more rapidly when the cognition decline in APOE ε4 carriers, while the right putamen compensatory expansion to slow the rate of cognitive decline although failed. This suggested that the right putamen showed stronger compensation by increasing the volume at the early stage of cognitive impairments in the APOE ε4 carriers, while this compensatory change had been disappeared in the right thalamus. In conclusion, APOE ε4 allele modifies the correlation between the right thalamus, the right putamen, and MoCA scores, and it has a potential selective effect on the relationship between cognition and brain structures to some extent in non-demented elders.
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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 | 0.000 | 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) | 0.002 | 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".