Predicting Alcohol Dependence from Multi-Site Brain Structural Measures
Bibliographic record
Abstract
Abstract Background The search for neuroimaging biomarkers of alcohol use disorder (AUD) has primarily been restricted to significance testing in small datasets of low diversity. To identify neurobiological markers beyond individual differences, it may be useful to develop classification models for AUD. The ever-increasing quantity of neuroimaging data demands methods that are robust to the complexities of multi-site designs and are generalizable to data from new scanners. Methods This study represents a mega-analysis of previously published datasets from 2,034 AUD and comparison participants spanning 27 sites, coordinated by the ENIGMA Addiction Working Group. Data were grouped into a training set including 1,652 participants (692 AUD, 24 sites), and test set with 382 participants (146 AUD, 3 sites). A battery of machine learning classifiers was evaluated using repeated random cross-validation (CV) and leave-site-out CV. Area under the receiver operating characteristic curve (AUC) was our base metric of performance. Results Multi-objective evolutionary search was conducted to identify sparse, generalizable, and high performing subsets of brain measurements. Cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume, appeared most frequently across searches. Restricting a regularized logistic regression model to these four features yielded a test-set AUC of .768. Conclusions Developing classification models on multi-site data with varied underlying class distributions poses unique challenges. Supplementing datasets with controls from new sites and performing feature selection increases generalizability. Four features identified by evolutionary search may serve as specific biomarkers for individuals with current AUD.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".