P30. A novel application of the Phase-lag-Index in functional connectivity research. Issue 8 (August 2018)

• Record Type: Journal Article
• Authors: Bogaarts, J.G.; Chaturvedi, M.; Cozac, V.; Gschwandtner, U.; Hardmeier, M.; Hatz, F.; Meyer, A.; Fuhr, P.; Roth, V.
• Is Part Of: Clinical neurophysiology. Volume 129:Issue 8(2018:Aug.)
• Page Start: e79
• Publication Date: 2018-08
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2018.04.671
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : The phase-lag-index (PLI) (Stam et al., 2007 ) is a commonly used method to quantify functional connectivity (FC) in EEG/MEG data. When calculated for short epochs in the order of several seconds, PLI varies considerably from epoch to epoch. Using resting-state EEG data from 105 healthy subjects, we demonstrate that the pattern of correlations between PLI time-series is characteristic for an individual's brain. In addition to acting as an identifying fingerprint, this pattern is also highly gender-specific and bears characteristics that are shared among people and also between hemispheres. Furthermore, optimal performance is achieved using an epoch length of 250 ms which is in the same range as typical durations of phase-synchronisation (Varela et al., 2001 ). This observation hints at the timing-relation between phase-synchronisation events being the underlying source of information captured by correlation between PLI time-series. In conclusion, our work reveals a novel way of extracting meaningful information about the brain's functional organisation from EEG data.