Joint analysis of imaging and genomic data to identify associations related to cognitive impairment
Bibliographic record
Abstract
Both genetic variants and brain region abnormalities are recognized to play a role in cognitive decline.In this project, we explore the relationship between genome-wide variation and region-specific rates of decline in brain structure, as measured by magnetic resonance imaging.The correspondence between rates of decline in brain regions and single nucleotide polymorphisms (SNPs) is investigated using data from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1), a study of Alzheimer's disease and mild cognitive impairment.In these data, the number of SNP and imaging biomarkers greatly exceeds the number of study subjects.To explore these data, we therefore look to modern multivariate statistical techniques that find sparse linear combinations of the two datasets having maximum correlation.These methods are particularly appealing because they greatly reduce the dimensions of the data, providing a low-dimensional representation of the data to explore.Regularization of the correlation structure through a "sparse" singular value decomposition makes multivariate analysis on a large set of biomarkers possible.Using sparse linear combinations of the two datasets also incorporates variable selection into the analysis, providing insight into which genetic variants are associated with cognitive decline.Resampling techniques are used to examine the validity of the results by exploring their reproducibility in independent test sets, and by assessing the stability of the variable selection.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".