Comprehensive evaluation of AT(N) imaging biomarkers for predicting cognition
Bibliographic record
Abstract
Background and Objectives: quantification of amyloid, tau, and neurogenerative pathologies that develop in Alzheimer's Disease (AD). Interest in imaging biomarkers has led to a wide variety of biomarker definitions, some of which potentially offer less predictive value than others. We aimed to assess how different operationalizations of AD imaging biomarkers affect prediction of cognition. Methods: F]-Flortaucipir), and volumetric MRI imaging. We compiled a large collection of imaging biomarker definitions (42 in total) spanning different pathologies (amyloid, tau, neurodegeneration) and variable types (continuous, binary, non-binary categorical). Using cross-validation, we trained regression models to predict neuropsychological performance, both globally and across different subdomains (Phenotype Harmonization Consortium composites), using different combinations of biomarkers. We also compared these biomarker models to support vector machines (SVMs) trained to predict cognition directly from imaging regions of interest. In a subsample of individuals with CSF biomarker readouts, we repeated experiments comparing the accuracy of models using imaging and fluid biomarkers. Additional analyses tested the predictive strength of imaging biomarkers when limited to specific clinical stages of disease (cognitive unimpaired vs. impaired) and when modeling longitudinal cognitive change. Results: Our sample included 490 people (247 female) with a mix of no impairment (n=288), mild impairment (n=163), and dementia (n=39). While almost all biomarkers tested were predictive of cognitive performance, we observed substantial variability in accuracy, even for measures of the same pathology. Tau biomarkers were the single most accurate single predictors, though combination of biomarkers spanning multiple pathologies were more accurate overall. SVM models were generally more accurate than models using traditional biomarkers. Incorporating continuous or non-binary categorical biomarkers was beneficial only for tau and neurodegeneration, but not amyloid. Patterns of results were largely consistent when considering different clinical stages of disease, neuropsychological domains, and longitudinal cognition. In the CSF subsample (n=246), imaging biomarkers strongly outperformed CSF versions for cognitive prediction. Discussion: We demonstrated that different imaging biomarker definitions can lead to variability in downstream predictive tasks. Researchers should consider how their biomarker operationalizations may help or hinder the assessment of disease severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".