Using Propensity Score Matching to Control for MRI Scan Quality
Bibliographic record
Abstract
Movement during MRI scanning complicates distinguishing between the different tissues in the brain (e.g., grey and white matter). Standard practice excludes scans based on researcher-determined visual quality thresholds. Unfortunately, children, elderly, and clinical populations are shown to move more, resulting in higher exclusion rates. This disproportionate exclusion creates systematic bias in the literature and makes research findings less generalizable. Furthermore, the artifacts caused by motion are demonstrated to continue to confound data, even after visual quality control has occurred. We aimed to minimize the confounding factor of systematic group differences in movement. To achieve this, we used a post-scanning statistical technique called propensity score matching (PSM) that matches control and patient populations on scan quality metrics, leading to more comparable groups, greater inclusion, and more generalizable results. We found that PSM can attenuate significant differences in scan quality between groups while allowing for greater sample diversity than standard exclusion protocols. Crucially, using PSM can also alter the results of neuroimaging-based analyses. Using three datasets (total n = 1536), we compared voxel based morphometry analyses based on different quality control protocols. In particular, we observed discrepant results between PSM and strict threshold exclusion, with PSM magnifying some regional group differences and diminishing others. Overall, PSM is a customizable way to mitigate the impact of confounds in neuroimaging research and a powerful method to help distinguish true effects from artifacts.
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How this classification was reachedexpand
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".