OBJECTIVE: Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. APPROACH: The aim of this study was to provide a network approach insensitive to the effects that epoch length has on functional connectivity and network reconstruction. Two different measures, the phase lag index (PLI) and the amplitude envelope correlation (AEC) were applied to EEG resting-state recordings for a group of 18 healthy volunteers using non-overlapping epochs with variable length (1, 2, 4, 6, 8, 10, 12, 14 and 16 s). Weighted clustering coefficient (CCw), weighted characteristic path length (L w) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. MAIN RESULTS: Results from scalp analysis show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 s for PLI and 6 s for AEC. Moreover, CCw and L w show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 s versus 4-8 s for AEC). At the source-level the results were even more reliable, with stability already at 1 s duration for PLI-based MSTs. SIGNIFICANCE: The present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain network topology between different studies.
OBJECTIVE: Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. APPROACH: The aim of this study was to provide a network approach insensitive to the effects that epoch length has on functional connectivity and network reconstruction. Two different measures, the phase lag index (PLI) and the amplitude envelope correlation (AEC) were applied to EEG resting-state recordings for a group of 18 healthy volunteers using non-overlapping epochs with variable length (1, 2, 4, 6, 8, 10, 12, 14 and 16 s). Weighted clustering coefficient (CCw), weighted characteristic path length (L w) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. MAIN RESULTS: Results from scalp analysis show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 s for PLI and 6 s for AEC. Moreover, CCw and L w show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 s versus 4-8 s for AEC). At the source-level the results were even more reliable, with stability already at 1 s duration for PLI-based MSTs. SIGNIFICANCE: The present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain network topology between different studies.
Authors: Jan Cimbalnik; Petr Klimes; Vladimir Sladky; Petr Nejedly; Pavel Jurak; Martin Pail; Robert Roman; Pavel Daniel; Hari Guragain; Benjamin Brinkmann; Milan Brazdil; Greg Worrell Journal: Clin Neurophysiol Date: 2019-08-05 Impact factor: 3.708
Authors: Brigitta Tóth; Gábor Urbán; Gábor P Háden; Molnár Márk; Miklós Török; Cornelis Jan Stam; István Winkler Journal: Hum Brain Mapp Date: 2017-05-10 Impact factor: 5.038
Authors: Dagmar Nieboer; Linda Douw; Bob W van Dijk; Martijn W Heymans; Cornelis J Stam; Jos W R Twisk Journal: BMJ Open Date: 2016-12-15 Impact factor: 2.692
Authors: M Demuru; A A Gouw; A Hillebrand; C J Stam; B W van Dijk; P Scheltens; B M Tijms; E Konijnenberg; M Ten Kate; A den Braber; D J A Smit; D I Boomsma; P J Visser Journal: Sci Rep Date: 2017-08-29 Impact factor: 4.379
Authors: Yvonne Höller; Kevin Butz; Aljoscha Thomschewski; Elisabeth Schmid; Andreas Uhl; Arne C Bathke; Georg Zimmermann; Santino O Tomasi; Raffaele Nardone; Wolfgang Staffen; Peter Höller; Markus Leitinger; Julia Höfler; Gudrun Kalss; Alexandra C Taylor; Giorgi Kuchukhidze; Eugen Trinka Journal: Front Hum Neurosci Date: 2017-07-05 Impact factor: 3.169