White matter microstructure and cognitive functioning across healthy older adults with different APOE alleles
Bibliographic record
Abstract
Abstract Background APOE alleles play an important neurobiological role in the development of Alzheimer’s Disease (AD), including modifying structure of white matter in the brain and cognitive decline (Gold et al., 2012; Reas et al., 2019). However, certain alleles (i.e., APOE‐e4) have be studied more than others (i.e., APOE‐e2 & e3), and have been primarily studied in individuals already diagnosed with AD, rather than in healthy older adults. While APOE‐e4 is well establish risk factor for AD, APOE‐e2 may play a role in protective mechanisms against AD, and APOE‐e3 may be neutral, yet their underlying neural mechanisms and association with cognitive functioning in the healthy aging brain are equivocal (Suri et al., 2013). Thus, the current study aimed to investigate the influence of different APOE alleles on white matter microstructure and cognition in healthy older adults. Method White matter microstructure, memory, and executive functioning (EF) were compared between cognitively normal older adults with either APOE‐e2+ (n= 16, mean age= 76), APOE e3e3 (n= 64, mean age= 74), or APOE‐e4+ (n=35, mean age=73). White matter microstructure was compared using Diffusion Tensor Imaging (DTI) Track‐based spatial statistics (TBSS) methods to derive fractional anisotropy (FA). Whole‐brain mean FA was compared first between e3e3 and e2+ and then between e3e3 and e4+ groups. Memory and executive functioning composite scores were compared, including covariates of age, sex, and education levels, using multivariate analysis of covariance (MANCOVA). All data were retrieved from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Result No significant differences in white matter microstructure (FA; p> 0.05, corrected) were observed. Further, groups were not significantly different in memory, EF, age, sex, or education levels (p=0.59). Conclusion These results indicate that white matter microstructure, memory, and EF changes are not evident in the healthy brain as a function of APOE allele presence. DTI may not be sensitive to APOE‐related changes in the healthy brain; nonetheless, future investigations should evaluate whether other structural (e.g., grey matter volume) and functional (e.g., fMRI) differences are evident across different APOE alleles in this population, as well as examine other cognitive domains.
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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 | 0.001 | 0.001 |
| 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".