Statistical properties of functional connectivity MRI enrichment analysis in school-age autism research
Bibliographic record
Abstract
Mass univariate testing on functional connectivity MRI (fcMRI) data is limited by difficulties achieving experiment-wide significance. Recent work addressing this problem has used enrichment analysis, which aggregates univariate screening statistics for a set of variables into a single enrichment statistic. There have been promising results using this method to explore fcMRI-behavior associations. However, there has not yet been a rigorous examination of the statistical properties of enrichment analysis when applied to fcMRI data. Establishing power for fcMRI enrichment analysis will be important for future neuropsychiatric and cognitive neuroscience study designs that plan to include this method. Here, we use realistic simulation methods, which mimic the covariance structure of fcMRI data, to examine the false positive rate and statistical power of one technique for enrichment analysis, over-representation analysis. We find it can attain high power even for moderate effects and sample sizes, and it strongly outperforms univariate analysis. The false positive rate associated with permutation testing is robust. • fcMRI enrichment analysis is a method for conducting brain-wide association studies. • Addresses problems with significance and power present in mass univariate analysis. • Can be well-powered for small effects ( R 2 < 0.01 ) and low sample sizes ( N = 50 ).
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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 | 0.001 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".