Spatial-extent inference for testing variance components in reliability and heritability studies
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Clusterwise inference is a popular approach in neuroimaging to increase sensitivity, but most existing methods are currently restricted to the General Linear Model (GLM) for testing mean parameters. Statistical methods for testing variance components, which are critical in neuroimaging studies that involve estimation of narrow-sense heritability or test-retest reliability, are underdeveloped due to methodological and computational challenges, which would potentially lead to low power. We propose a fast and powerful test for variance components called CLEAN-V (CLEAN for testing Variance components). CLEAN-V models the global spatial dependence structure of imaging data and computes a locally powerful variance component test statistic by data-adaptively pooling neighborhood information. Correction for multiple comparisons is achieved by permutations to control family-wise error rate (FWER). Through analysis of task-functional magnetic resonance imaging (fMRI) data from the Human Connectome Project across five tasks and comprehensive data-driven simulations, we show that CLEAN-V outperforms existing methods in detecting test-retest reliability and narrow-sense heritability with significantly improved power, with the detected areas aligning with activation maps. The computational efficiency of CLEAN-V also speaks of its practical utility, and it is available as an R package.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it