Inter-group Heterogeneity of Regional Homogeneity (REHO)
Bibliographic record
Abstract
Regional Homogeneity (REHO) measures the similarity between the time series of a given voxel and those of its neighbors. First discovered in a task-activation paradigm, REHO was considered as a complementary method to model-driven analysis of fMRI time series. With the increased popularity of resting-state paradigms, REHO has become a widely used method for inferring neural activity in the resting state. However, the neural/physiological processes that give rise to REHO are poorly understood. Differences in REHO across groups may not be indicative of differences in neuronal activity. Here, we investigate physiological contributions to REHO across 412 subjects in 9 separate datasets downloaded from OpenNeuro where both physiological signals (respiratory rate, heart rate, and motion) and resting state data are available. Overall, we find an inverse correlation between heart rate and REHO across subjects, an inverse correlation between respiratory rate and REHO across time, and differences in REHO across groups is driven primarily by FWHM of data and motion. We conclude that, due to REHO’s highly significant correlation with motion, heart rate, and respiratory rate, REHO should be used with caution to infer differences in neuronal activity across groups.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".