Neurostructural and neurocognitive correlates of <i>APOE</i> ε4 in youth bipolar disorder
Bibliographic record
Abstract
Background: Bipolar disorder (BD) is a clinical risk factor for Alzheimer’s disease (AD). Apolipoprotein E ε4 ( APOE ε4), a genetic risk factor for AD, has been associated with brain structure and neurocognition in healthy youth. Aims: We evaluated whether there was an association between APOE ε4 with neurostructure and neurocognition in youth with BD. Methods: Participants included 150 youth (78 BD:19 ε4-carriers, 72 controls:17 ε4-carriers). 3T-magnetic resonance imaging yielded measures of cortical thickness, surface area, and volume. Regions-of-interest (ROI) and vertex-wise analyses of the cortex were conducted. Neurocognitive tests of attention and working memory were examined. Results: Vertex-wise analyses revealed clusters with a diagnosis-by- APOE ε4 interaction effect for surface area ( p = 0.002) and volume ( p = 0.046) in pars triangularis (BD ε4 -carriers > BD noncarriers), and surface area ( p = 0.03) in superior frontal gyrus (controls ε4 -carriers > other groups). ROI analyses were not significant. A significant interaction effect for working memory ( p = 0.001) appeared to be driven by nominally poorer performance in BD ε4 -carriers but not control ε4 -carriers; however, post hoc contrasts were not significant. Conclusions: APOE ε4 was associated with larger neurostructural metrics in BD and controls, however, the regional association of APOE ε4 with neurostructure differed between groups. The role of APOE ε4 on neurodevelopmental processes is a plausible explanation for the observed differences. Future studies should evaluate the association of APOE ε4 with pars triangularis and its neurofunctional implications among youth with BD.
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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 | 0.000 |
| 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".