Unraveling high-order interactions in electrophysiological brain signals using elliptical distributions: moving beyond the Gaussian approximation
Bibliographic record
Abstract
Abstract The primary method for evaluating high-order brain interactions by information-theoretic measures such as (dual) total correlation and O-information involves the Gaussian approximation. Although this approximation is rather accurate for functional MRI signals, it is unclear how accurate it is for electroencephalography (EEG) and magnetoencephalography (MEG) signals. Here, we introduce the elliptical approximation, which is accurate for Gaussian data and a large family of non-Gaussian data. To illustrate its use, we applied both approximations to EEG and MEG signals and found that the Gaussian approximation to the (dual) total correlation and O-information is quite accurate for physiological resting-state oscillations, but is highly inaccurate for EEG data recorded during an absence seizure. In particular, for interactions of high-order ( > 10) the approximations do not always agree on whether the interactions are dominated by synergy or redundancy. Thus, our proposed method offers an opportunity to study high-order interactions in electrophysiological brain activity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".