The Thresholding Problem and Variability in the EEG Graph Network Parameters

Overview

Journal Sci Rep

Specialty Science

Date 2022 Nov 5

PMID 36333413

Authors

Timofey Adamovich

Ilya Zakharov

Anna Tabueva

Sergey Malykh

Affiliations

Soon will be listed here.

Abstract

Graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary, and the effect of the threshold choice is not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graph's global connectivity measures as a function of the chosen threshold which may influence the outcome of the study. The choice of the threshold could lead to different study conclusions; thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider the adoption of different analytic approaches. We also proposed some ways of improving the procedure of thresholding in functional connectivity research.

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References

Santarnecchi E, Galli G, Polizzotto N, Rossi A, Rossi S . Efficiency of weak brain connections support general cognitive functioning. Hum Brain Mapp. 2014; 35(9):4566-82. PMC: 6869093. DOI: 10.1002/hbm.22495. View

Steegen S, Tuerlinckx F, Gelman A, Vanpaemel W . Increasing Transparency Through a Multiverse Analysis. Perspect Psychol Sci. 2016; 11(5):702-712. DOI: 10.1177/1745691616658637. View

Botvinik-Nezer R, Holzmeister F, Camerer C, Dreber A, Huber J, Johannesson M . Variability in the analysis of a single neuroimaging dataset by many teams. Nature. 2020; 582(7810):84-88. PMC: 7771346. DOI: 10.1038/s41586-020-2314-9. View

Bastos A, Vezoli J, Fries P . Communication through coherence with inter-areal delays. Curr Opin Neurobiol. 2014; 31:173-80. DOI: 10.1016/j.conb.2014.11.001. View

Vinck M, Oostenveld R, van Wingerden M, Battaglia F, Pennartz C . An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage. 2011; 55(4):1548-65. DOI: 10.1016/j.neuroimage.2011.01.055. View

Sadaghiani S, Brookes M, Baillet S . Connectomics of human electrophysiology. Neuroimage. 2021; 247:118788. PMC: 8943906. DOI: 10.1016/j.neuroimage.2021.118788. View

Aczel B, Szaszi B, Nilsonne G, van den Akker O, Albers C, van Assen M . Consensus-based guidance for conducting and reporting multi-analyst studies. Elife. 2021; 10. PMC: 8626083. DOI: 10.7554/eLife.72185. View

Kang I, Galdo M, Turner B . Constraining functional coactivation with a cluster-based structural connectivity network. Netw Neurosci. 2024; 6(4):1032-1065. PMC: 11117093. DOI: 10.1162/netn_a_00242. View

Zakharov I, Tabueva A, Adamovich T, Kovas Y, Malykh S . Alpha Band Resting-State EEG Connectivity Is Associated With Non-verbal Intelligence. Front Hum Neurosci. 2020; 14:10. PMC: 7010914. DOI: 10.3389/fnhum.2020.00010. View

10.

Vasa F, Misic B . Null models in network neuroscience. Nat Rev Neurosci. 2022; 23(8):493-504. DOI: 10.1038/s41583-022-00601-9. View

11.

van den Heuvel M, Sporns O . A cross-disorder connectome landscape of brain dysconnectivity. Nat Rev Neurosci. 2019; 20(7):435-446. PMC: 8864539. DOI: 10.1038/s41583-019-0177-6. View

12.

Drakesmith M, Caeyenberghs K, Dutt A, Lewis G, David A, Jones D . Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data. Neuroimage. 2015; 118:313-33. PMC: 4558463. DOI: 10.1016/j.neuroimage.2015.05.011. View

13.

Bassett D, Nelson B, Mueller B, Camchong J, Lim K . Altered resting state complexity in schizophrenia. Neuroimage. 2011; 59(3):2196-207. PMC: 3254701. DOI: 10.1016/j.neuroimage.2011.10.002. View

14.

Nicolini C, Forcellini G, Minati L, Bifone A . Scale-resolved analysis of brain functional connectivity networks with spectral entropy. Neuroimage. 2020; 211:116603. DOI: 10.1016/j.neuroimage.2020.116603. View

15.

Harder J . The Multiverse of Methods: Extending the Multiverse Analysis to Address Data-Collection Decisions. Perspect Psychol Sci. 2020; 15(5):1158-1177. DOI: 10.1177/1745691620917678. View

16.

van Wijk B, Stam C, Daffertshofer A . Comparing brain networks of different size and connectivity density using graph theory. PLoS One. 2010; 5(10):e13701. PMC: 2965659. DOI: 10.1371/journal.pone.0013701. View

17.

Ma X, Gao L . Discovering protein complexes in protein interaction networks via exploring the weak ties effect. BMC Syst Biol. 2012; 6 Suppl 1:S6. PMC: 3403613. DOI: 10.1186/1752-0509-6-S1-S6. View

18.

Gramfort A, Luessi M, Larson E, Engemann D, Strohmeier D, Brodbeck C . MNE software for processing MEG and EEG data. Neuroimage. 2013; 86:446-60. PMC: 3930851. DOI: 10.1016/j.neuroimage.2013.10.027. View

19.

Hardwicke T, Ioannidis J . Mapping the universe of registered reports. Nat Hum Behav. 2019; 2(11):793-796. DOI: 10.1038/s41562-018-0444-y. View

20.

Palva S, Palva J . Functional roles of alpha-band phase synchronization in local and large-scale cortical networks. Front Psychol. 2011; 2:204. PMC: 3166799. DOI: 10.3389/fpsyg.2011.00204. View