How much is “enough”? Considerations for functional connectivity reliability in pediatric naturalistic fMRI
Why this work is in the frame
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Bibliographic record
Abstract
Reliable functional connectivity (FC) measurements are important for robust neuroimaging findings, yet pediatric functional magnetic resonance imaging (fMRI) faces unique challenges due to head motion and bias toward shorter scans. Passive viewing conditions during fMRI offer advantages for scanning pediatric populations, but FC reliability under these conditions remains underexplored. Here, we used precision fMRI data collected across three passive viewing conditions to directly compare FC reliability profiles between 25 pre-adolescent children and 25 adults, with each participant providing over 2.8 hours of data over 4 sessions. We found that FC test-retest correlations increased asymptotically with scan length, with children requiring nearly twice the post-censored scan time (24.6 minutes) compared with adults (14.4 minutes) to achieve comparable reliability, and that this effect was only partly attributable to head motion. Reliability differences between lower-motion adults and higher-motion children were spatially non-uniform and largest in ventral anterior temporal and frontal regions. While averaging features within functional networks improved intraclass correlation coefficient (ICC) reliability, values for higher-motion children remained in the poor-to-fair ICC range even with 24 minutes of data. Of note, we observed substantial increases in edge-wise ICC between 24 and 54 minutes of data. Viewing conditions with greater engagement reduced head motion in children but had lower FC reliability than less engaging "low-demand" videos, suggesting complex state- or condition-related trade-offs. These findings have important implications for developmental neuroimaging study design, particularly for higher motion pediatric populations.
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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 | 0.001 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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