Effect of analytical variability in estimating EEG-based functional connectivity
Bibliographic record
Abstract
Abstract The significant degree of variability and flexibility in neuroimaging analysis approaches has recently raised concerns. When running any neuroimaging study, the researcher is faced with a large number of methodological choices, often made arbitrarily. This can produce substantial variability in the results, ultimately hindering research replicability, and thus, robust conclusions. Here, we addressed the analytical variability in the EEG source connectivity pipeline and its effects on outcomes consistency. Like most neuroimaging analyses, the EEG source connectivity analysis involves the processing of high-dimensional data and is characterized by a complex workflow that leads to high analytical variability. In this study, we focused on source functional connectivity variability induced by three key factors along the analysis pipeline: 1) number of EEG electrodes, 2) inverse solution algorithms, and 3) functional connectivity metrics. Outcomes variability was assessed in terms of group-level consistency, inter-, and intra-subjects similarity, using resting-state EEG data (n = 88). As expected, our results showed that different choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure substantially affect group-level consistency, between-, and within-subjects similarity. We believe that the significant impact of such methodological variability represents a critical issue for neuroimaging studies that should be prioritized. Highlights The significant impact of methodological variability is a recognized critical priority issue for neuroimaging studies. Analytical variability related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure is a prominent issue in the EEG source connectivity analysis. Group-level consistency, between-, and within-subjects similarity are substantially affected by analytical variability in the EEG source connectivity analysis.
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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 | 0.036 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".