Building blocks of functional connectivity measures for aperiodic electrophysiological brain signals
Bibliographic record
Abstract
A challenge in interpreting functional connectivity results in electroencephalography (EEG) data is volume conduction. A common way to mitigate spurious connectivity due to volume conduction is to use connectivity measures that are insensitive to volume conduction. Examples of such measures are the imaginary coherence, the lagged coherence, and the (weighted) phase-lag index. Their insensitivity to volume conduction stems from an invariant property and it is of both practical and theoretical interest to identify all measures with this property. In this study we derive a set of invariant connectivity measures that are fundamental in the sense that all others can be constructed from them by combination. These ”building blocks” of connectivity measures quantify the lack of invariance of multivariate EEG signals under permutation of the time-points. We use this result to construct a new connectivity measure for stationary aperiodic EEG signals, referred to as the temporal irreversibility index (TII) and illustrate its use by applying it to local field potentials recorded from primary visual cortex of a macaque monkey and to EEG data from comatose survivors of cardiac arrest. As far as we are aware, the TII is currently the only functional connectivity measure for aperiodic signals that is insensitive to volume conduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| 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".