Distinctive age‐related longitudinal dementia progression patterns using a machine‐learning‐based MRI biomarker
Bibliographic record
Abstract
Abstract Background Dementia of Alzheimer’s type (DAT) is an age‐related neurodegenerative syndrome caused by Alzheimer’s disease. The risk of developing dementia increases with age, but also depends on disease trajectory. In this study, we aim to evaluate the effect of normal aging on the MRI‐derived dementia risk score and investigate the distinctive age‐related longitudinal dementia progression patterns among individuals with different dementia trajectories. Method A total of 1609 participants with multi‐timepoint neuroimaging tests and diagnoses were recruited from the ADNI. Based on their dementia trajectories, the subjects were stratified into four groups: stable normal cognition (sNC=423), stable/progressive mild cognitive impairment (sMCI=535/pMCI=321), and stable dementia of Alzheimer’s type (sDAT=330). Brain structures were automatically segmented and harmonized to control the confounding effect of sex and scanner, and feed into an extensively‐validated kernel‐based ensemble classifier to derive the score, which we termed longitudinal MR‐DAT‐Score (MRDATS). The CSF‐based biomarker was derived as the ratio between the total tau and the amyloid beta (t‐tau/Aβ1‐42). We analyzed the effect of aging on the biomarkers’ distribution of each stratified group in terms of their empirical cumulative distribution function (ECDF). We also evaluated the difference in longitudinal MRDATS progression patterns among different groups. Result Compared to the CSF biomarker, the MRDATS revealed a more distinctive age‐related risk increase among all stratified groups (Figure 1). In addition, the effect of aging persists across both the non‐progression groups (sNC and sMCI) and the progression groups (pMCI) except for the DAT group, where the age‐related effect is only observable for the 80‐90 years’ group. Distinctive longitudinal dementia risk progression patterns (in terms of MRDATS) were also observed for each stratified group (Figure 2). Specifically, for patients with mild cognitive impairment (MCI), distinctive patterns were observed between sMCI and pMCI across subjects across different aged populations. Conclusion Our study demonstrated the differential aging effect pattern as well as distinctive longitudinal patterns between AD‐progressive and non‐progressive subjects across different age groups. The results of this study emphasize the importance of disentangling the effect of aging with the disease‐driven brain atrophy patterns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".