Graph Theoretical Characteristics of EEG-Based Functional Brain Networks in Patients With Epilepsy: The Effect of Reference Choice and Volume Conduction
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it