Graph Theoretical Characteristics of EEG-Based Functional Brain Networks in Patients With Epilepsy: The Effect of Reference Choice and Volume Conduction
Bibliographic record
Abstract
<p> It is well-established that both volume conduction and the choice of recording reference</p>\n\n<p>(montage) affect the correlation measures obtained from scalp EEG, both in the time</p>\n\n<p>and frequency domains. As a result, a number of correlation measures have been</p>\n\n<p>proposed aiming to reduce these effects. In our previous work, we have showed that</p>\n\n<p>scalp-EEG based functional brain networks in patients with epilepsy exhibit clear periodic</p>\n\n<p>patterns at different time scales and that these patterns are strongly correlated to seizure</p>\n\n<p>onset, particularly at shorter time scales (around 3 and 5 h), which has important clinical</p>\n\n<p>implications. In the present work, we use the same long-duration clinical scalp EEG data</p>\n\n<p>(multiple days) to investigate the extent to which the aforementioned results are affected</p>\n\n<p>by the choice of reference choice and correlation measure, by considering several widely</p>\n\n<p>used montages as well as correlation metrics that are differentially sensitive to the</p>\n\n<p>effects of volume conduction. Specifically, we compare two standard and commonly</p>\n\n<p>used linear correlation measures, cross-correlation in the time domain, and coherence in</p>\n\n<p>the frequency domain, with measures that account for zero-lag correlations: corrected</p>\n\n<p>cross-correlation, imaginary coherence, phase lag index, and weighted phase lag index.</p>\n\n<p>We show that the graphs constructed with corrected cross-correlation and WPLI are</p>\n\n<p>more stable across different choices of reference. Also, we demonstrate that all the</p>\n\n<p>examined correlation measures revealed similar periodic patterns in the obtained graph</p>\n\n<p>measures when the bipolar and common reference (Cz) montage were used. This</p>\n\n<p>includes circadian-related periodicities (e.g., a clear increase in connectivity during sleep</p>\n\n<p>periods as compared to awake periods), as well as periodicities at shorter time scales</p>\n\n<p>(around 3 and 5 h). On the other hand, these results were affected to a large degree when</p>\n\n<p>the average referencemontage was used in combination with standard cross-correlation,</p>\n\n<p>coherence, imaginary coherence, and PLI, which is likely due to the low number of</p>\n\n<p>electrodes and inadequate electrode coverage of the scalp. Finally, we demonstrate that</p>\n\n<p>the correlation between seizure onset and the brain network periodicities is preserved </p>\n\n<p> when corrected cross-correlation and WPLI were used for all the examined montages.</p>\n\n<p>This suggests that, even in the standard clinical setting of EEG recording in epilepsy</p>\n\n<p>where only a limited number of scalp EEG measurements are available, graph-theoretic</p>\n\n<p>quantification of periodic patterns using appropriate montage, and correlation measures</p>\n\n<p>corrected for volume conduction provides useful insights into seizure onset.</p>
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".