A Resting-State Connectivity Metric Independent of Temporal Signal-to-Noise Ratio and Signal Amplitude
Bibliographic record
Abstract
Temporal signal-to-noise ratio (tSNR) and the amplitude of low-frequency resting-state fluctuations (signal amplitude [SA]) can vary between magnetic resonance imaging sessions, thereby decreasing the reliability and reproducibility of measurements of resting-state connectivity between regions of interest (ROIs) in the human brain. In this study, a new metric for quantifying the strength of resting-state connections is introduced, which possesses low sensitivity to tSNR and SA but maintains high sensitivity to expected changes in connectivity magnitude or region volume caused by the presence of neurological disease, for example. This new metric is one that essentially divides the temporal cross-correlation of two ROIs by the temporal cross-correlation of one the ROIs with itself (i.e., a relative connectivity [RelCon]). The robustness of the new metric is demonstrated and compared with several existing metrics, using simulated datasets of varying tSNR and SA, as well as in data collected over multiple sessions from healthy subjects. For both simulated and real datasets, relative connectivity exhibited lower sensitivity to tSNR and SA compared with existing (i.e., absolute) connectivity metrics. Further, simulation suggests that for RelCon, it is better to calculate the correlation between all possible pairs of ROI voxel signals and then appropriately average the correlation coefficients, whereas for absolute connectivity it is better to average signals within the ROIs and then determine the correlation between the averaged signals. RelCon permits the comparison of connectivity across datasets acquired with different scanners or imaging parameters that potentially generate data with differing tSNR and SA.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".