Comparing reliability-based measures of functional connectivity between movie and rest: An ROI-based approach
Bibliographic record
Abstract
Functional connectivity (FC) has shown promising utility in the field of precision psychiatry. However, to translate from research to clinical use, FC reliability and sensitivity to individual differences still require improvement. Movie watching as an acquisition state offers advantages at the whole-brain level that align with the requirements of FC for individualized measures. However, it is unclear whether these advantages hold in specific brain regions important for precision psychiatry. Here, we compared univariate and multivariate reliability-based measures of movie-watching and resting-state FC data in three psychiatrically relevant brain regions. We found that the reliability of movie-watching FC was comparable with resting-state FC in the dorsolateral prefrontal cortex and presupplementary motor area, and movie-watching FC was more discriminable than resting-state FC in the temporoparietal junction. Rest had higher reliabilities at lower data amounts (e.g., under 5 minutes of scan time). We then expanded this approach to all brain regions and showed that for image intraclass correlation coefficients (I2C2), no parcels were significantly different between movie and rest. For discriminability, 25% (94/379) of parcels were better for movie than for rest, and zero parcels were better for rest. For fingerprinting, 59 parcels were better for movie (mainly in visual and temporal regions, mean improvement in accuracy = 23%) and 4 parcels were better for rest. For researchers interested in cross-state differences in FC reliability, we provide an interactive visualization tool that displays the results for all measures and for all regions in both movie and rest. These findings suggest that movie watching as an acquisition state-even when using different movies across scans-may provide a useful alternative to resting state in research studies that require optimization of FC discriminability.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".