Mapping the Effect of the Apolipoprotein E Genotype on 4-Year Atrophy Rates in an Alzheimer Disease–related Brain Network
Bibliographic record
Abstract
PURPOSE: To determine the effect of the apolipoprotein E (APOE) genotype on atrophy rates of specific brain gray matter regions hypothesized to be key components of cognitive networks disrupted in Alzheimer disease. MATERIALS AND METHODS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was approved by the institutional review boards of all participating sites. All subjects and their legal representatives gave written informed consent prior to data collection. The authors analyzed data from 237 subjects (mean age, 79.9 years; 40% female) with mild cognitive impairment (MCI) in the ADNI database and assessed the effect of the APOE ε4 and ε2 alleles on regional brain atrophy rates over a 12-48-month period. Brain regions were selected a priori: 15 experimental and five control regions were included. Regional atrophy rates were derived by using a fully automated algorithm applied to T1-weighted magnetic resonance (MR) imaging data. Analysis consisted of mixed-effects linear regression with repeated measures; results were adjusted for multiple testing with Bonferroni correction. RESULTS: Thirteen of 15 experimental regions showed a significant effect of ε4 for higher atrophy rates (P < .001 for all). Cohen d values ranged from 0.26 to 0.42, with the largest effects seen in the amygdalae and hippocampi. The transverse temporal cortex showed a trend (P = .02, but did not survive Bonferroni correction) for a protective effect (Cohen d value = 0.15) of ε2. No control region showed an APOE effect. CONCLUSION: The APOE ε4 allele is associated with accelerated rates of atrophy in 13 distinct brain regions in limbic and neocortical areas. This suggests the possibility of a genotype-specific network of related brain regions that undergo faster atrophy in MCI and potentially contribute to cognitive decline. Online supplemental material is available for this article.
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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 | 0.002 | 0.005 |
| 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".