Deep causal feature extraction and inference with neuroimaging genetic data
Bibliographic record
Abstract
Alzheimer's disease (AD) is a severe public health issue in the world. Magnetic Resonance Imaging (MRI) offers a way to study brain differences between AD patients and healthy individuals through feature extraction and comparison. However, in most previous works, the extracted features were not aimed to be causal, hindering biological understanding and interpretation. In order to extract causal features, we propose using instrumental variable (IV) regression with genetic variants as IVs. Specifically, we propose Deep Feature Extraction via Instrumental Variable Regression (DeepFEIVR), which uses a nonlinear neural network to extract causal features from three-dimensional neuroimages to predict an outcome (eg, AD status in our application) while maintaining a linear relationship between the extracted features and IVs. DeepFEIVR not only can handle high dimensional individual-level data for model building, but also is applicable to GWAS summary data to test associations of the extracted features with the outcome in subsequent analysis. In addition, we propose an extension of DeepFEIVR, called DeepFEIVR-CA, for covariate adjustment (CA). We apply DeepFEIVR and DeepFEIVR-CA to the Alzheimer's Disease Neuroimaging Initiative (ADNI) individual-level data as training data for model building, then apply to the UK Biobank neuroimaging and the International Genomics of Alzheimer's Project (IGAP) AD GWAS summary data, showcasing how the extracted causal features are related to AD and various brain endophenotypes.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".