On statistical tests of functional connectome fingerprinting
Bibliographic record
Abstract
Abstract Fingerprinting of functional connectomes is an increasingly standard measure of reproducibility in functional magnetic resonance imaging connectomics. In such studies, one attempts to match a subject's first session image with their second, in a blinded fashion, in a group of subjects measured twice. The number or percentage of correct matches is usually reported as a statistic, which is then used in permutation tests. Despite the simplicity and increasing popularity of such procedures, the soundness of the statistical tests, the power, and the factors impacting the test are unstudied. In this article, we investigate the statistical tests of matching based on exchangeability assumption in the fingerprinting analysis. We show that a nearly universal Poisson(1) approximation applies for different matching schemes. We theoretically investigate the permutation tests and explore the issue that the test is overly sensitive to uninteresting directions in the alternative hypothesis, such as clustering due to familial status or demographics. We perform a numerical study on two functional magnetic resonance imaging (fMRI) resting‐state datasets, the Human Connectome Project (HCP) and the Baltimore Longitudinal Study of Aging (BLSA). These datasets are instructive, as the HCP includes technical replications of long scans and includes monozygotic and dizygotic twins, as well as non‐twin siblings. In contrast, the BLSA study incorporates more typical length resting‐state scans in a longitudinal study. Finally, a study of single regional connections is performed on the HCP data.
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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 | 0.147 | 0.632 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".