Predicting cognitive decline in a low‐dimensional representation of brain morphology
Bibliographic record
Abstract
Abstract Background Identifying early signs of neurodegeneration due to Alzheimer’s disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, cortical decline in aging and high inter‐individual variability complicates the determination of AD‐related neurodegeneration on trajectories. Further, the high‐dimensional nature of these trajectories necessitate the use of a transformation into lower‐dimensional spaces in order to perform comparative statistical analyses, model decline, and identify criteria differentiating pathological from normal trajectories. Method In this project, we computed trajectories in a 2D representation of a 62‐dimensional manifold of individual cortical thickness measures. To compute this representation, we used a nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We first created a UMAP embedding on measurements from 6,237 cognitively healthy participants (3,556 women) aged 18 to 100 years old from the NOMIS database. We then projected longitudinal data from 537 mild cognitively impaired (MCI) subjects and 340 AD subjects from ADNI into this embedding. Each participant had multiple visits (n ≥ 2), one year apart. Finally, differences in trajectories were analysed by clustering the reduced data and comparing the positional variations through time between MCI and AD subjects. The validity of the approach was verified through cross‐validation. Result First, the embedding was shown to be positively associated (r = 0.65) with participants' age (see figure 1) in the NOMIS data. When projected in this space, differences between ADNI’s MCI and AD distributions were found (see figure 2). Average trajectories between clusters were shown to be significantly different between MCI and AD subjects (see figure 3). Moreover, we showed that some clusters and trajectories between clusters were more prone to host AD subjects. Finally, cross‐validation showed an accuracy of up to 72% at predicting cognitive decline in MCI participants over 2,000 bootstrap iterations. Conclusion A 2D low‐dimensional cortical thickness representation embeds sufficient discriminatory information as to predict decline to AD in MCI participants with strong accuracy.
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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 | 0.000 |
| 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) | 0.001 | 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".