Multivariate white matter differences links to cognition in individuals with family history of Alzheimer’s disease and APOE4 genetic risk
Bibliographic record
Abstract
Abstract Background Growing evidence suggests Alzheimer’s disease (AD) develops from a complex cascade of events that vary between individuals. Multivariate approaches have the potential to capture the complexity and heterogeneity of pathologies underlying AD in a more holistic manner compared to univariate approaches. Method The MRI data of 105 older adults with a family history of AD from the PREVENT‐AD dataset were analysed. Forty six of these individuals had the APOE4‐3 genotype (greater risk of developing AD), while 59 subjects had the APOE3‐3 genotype (normal risk). Diffusion‐weighted imaging and multi‐echo magnetization transfer, proton density and T1‐weighted data were used to compute several WM metrics (Fig1 b‐c). We computed the voxel‐wise Mahalanobis distance (D2) in WM between APOE4‐3 individuals and a reference group (APOE3‐3) (Fig1). D2 is a multivariate measure that combines several MRI metrics, accounting for their covariance, and yielding a score indicative of the degree of abnormality at each WM voxel (Fig2). Associations between WM D2 and cognitive performance were then investigated (Fig3). Independent component analysis (ICA) was performed on spatial dimensions and linear regression analyses were conducted between D2 in ICA components and scores on items of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). We then extracted the contribution of each MRI feature to D2 in the regions where significant associations were found. Result A significant negative association (R = ‐0.36, p = 0.014) was found between D2 in a component corresponding to the splenium of the corpus callosum (CC) and scores on RBANS‐immediate memory (Fig3), indicating a link between a greater degree of WM abnormality and poorer performance. Proton density (MPM_PD) and longitudinal relaxation rate (MPM_R1), potentially indicative of WM loss, contributed most to D2 in this region. Conclusion Our findings suggest that microstructural alterations in the posterior part of the CC may play a role in memory deficit in older adults who are at greater risk of developing AD. This is in line with other studies that showed a link between CC degeneration and memory impairments in early AD stages.1 1. Qiu et al. (2016). Inter‐hemispheric dysconnectivity in AD and aMCI. Sci.Rep.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".