Detecting connectivity in EEG: A comparative study of data-driven effective connectivity measures

Bakhshayesh, Hanieh; Fitzgibbon, Sean P.; Janani, Azin S.; Grummett, Tyler S.; Pope, Kenneth J.

doi:10.1016/j.compbiomed.2019.103329

Cited by 26 publications

(24 citation statements)

References 53 publications

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“…Despite the different concepts and the many time-seriesanalysis techniques, a discussion about their relative merit lasting for more than 15 years indicates that there is probably no single approach which is best suited to characterize properties of interactions between physiological systems (Smirnov and Andrzejak, 2005;Ansari-Asl et al, 2006;Kreuz et al, 2007;Paluš and Vejmelka, 2007;Smirnov et al, 2007;Osterhage et al, 2007aOsterhage et al, ,b, 2008Vejmelka and Paluš, 2008;Wendling et al, 2009;Florin et al, 2011;Wang et al, 2014;Zhou et al, 2014;Hirata et al, 2016;Stokes and Purdon, 2017;Xiong et al, 2017;Barnett et al, 2018;Beauchene et al, 2018;Dhamala et al, 2018;Krakovská et al, 2018;Bakhshayesh et al, 2019).…”

Section: Data-driven Assessment Of Pairwise Interaction Propertiesmentioning

confidence: 99%

The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges

2020

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The field of Network Physiology aims to advance our understanding of how physiological systems and sub-systems interact to generate a variety of behaviors and distinct physiological states, to optimize the organism's functioning, and to maintain health. Within this framework, which considers the human organism as an integrated network, vertices are associated with organs while edges represent time-varying interactions between vertices. Likewise, vertices may represent networks on smaller spatial scales leading to a complex mixture of interacting homogeneous and inhomogeneous networks of networks. Lacking adequate analytic tools and a theoretical framework to probe interactions within and among diverse physiological systems, current approaches focus on inferring properties of time-varying interactions—namely strength, direction, and functional form—from time-locked recordings of physiological observables. To this end, a variety of bivariate or, in general, multivariate time-series-analysis techniques, which are derived from diverse mathematical and physical concepts, are employed and the resulting time-dependent networks can then be further characterized with methods from network theory. Despite the many promising new developments, there are still problems that evade from a satisfactory solution. Here we address several important challenges that could aid in finding new perspectives and inspire the development of theoretic and analytical concepts to deal with these challenges and in studying the complex interactions between physiological systems.

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Section: Data-driven Assessment Of Pairwise Interaction Propertiesmentioning

confidence: 99%

The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges

2020

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“…Recent comparative studies have assessed causal effects with various causality measures, using also significance tests for each causal effect [ 14 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. Some studies concentrated on the comparison of direct and indirect causality measures [ 46 , 52 ], whereas other studies focused on specific types of causality measures, e.g., frequency domain measures [ 53 , 54 , 55 , 56 ], or different significance tests for a causality measure [ 57 , 58 , 59 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Granger Causality Measures for Constructing Networks from Multivariate Time Series

Siggiridou

Koutlis

Tsimpiris

et al. 2019

Entropy

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Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. These measures are in the time domain, such as model-based and information measures, the frequency domain and the phase domain. The study aims also to compare bivariate and multivariate measures, linear and nonlinear measures, as well as the use of dimension reduction in linear model-based measures and information measures. The latter is particular relevant in the study of high-dimensional time series. For the performance of the multivariate causality measures, low and high dimensional coupled dynamical systems are considered in discrete and continuous time, as well as deterministic and stochastic. The measures are evaluated and ranked according to their ability to provide causality networks that match the original coupling structure. The simulation study concludes that the Granger causality measures using dimension reduction are superior and should be preferred particularly in studies involving many observed variables, such as multi-channel electroencephalograms and financial markets.

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“…In the Fourier domain, spectral characteristics of the standard EEG frequency sub-bands, namely delta (δ) (0.5-4 Hz), theta (θ) (4-8 Hz), alpha (α) (8)(9)(10)(11)(12)(13) and beta (β) (13-30 Hz) have been extensively used as input features for the different interictal spike detection and classification algorithms such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) [14]- [20]. We note that in this study as well as others [21], the gamma sub-band (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48) was excluded due to the presence of evidence suggesting that the gamma frequency range in scalp EEG recordings may be strongly affected by muscle artifact [22]. In addition, [23] reported that the gamma sub-band yields low reliability graph metrics.…”

Section: Introductionmentioning

confidence: 88%

“…A wide range of available FC measures along with their varied application domains are well reviewed and presented in the literature [44]- [47]. FC methods between two time series, x and y, can be broadly categorized into time and frequency based methods.…”

Section: Methods a Mathematical Foundationmentioning

confidence: 99%

Penalized Functional Connectivity Maps for Patients With Focal Epilepsy

et al. 2021

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Objective: This study demonstrates how functional connectivity (FC) patterns are affected in direct relation to the lobe that is mostly affected by seizures. Methods: The novel idea of penalized FC (pFC) maps is compared against standard FC maps in the four fundamental EEG frequency sub-bands. The FC measure between any two specific electrodes is scaled depending on the probability of true FC between them, and their power content with respect to the two electrodes of maximum power within each frequency sub-band. The algorithm is automated and introduces adaptive power penalization based on the power distribution of the different sub-bands. Results: The pFC maps were found to be more effective at suppressing the local connectivity in the lobes that are less affected by the interictal epileptiform discharges (IEDs). More precisely, given the least amount of power penalization, pFC maps of the theta sub-band reveal statistical significance in terms of increased local connectivity margin of the affected region as compared to the standard FC maps. However, they cannot be solely relied upon as other sub-bands could alternatively show high local connectivity across different patients within the region of interest. Conclusion: penalized functional connectivity maps intrinsically provide more information regarding the whole brain network in context to regions of interest where the active lobe is determined by the neurologists to contain the focal source. Significance: Findings suggest that (1) the significant sub-band varies from patient to patient while remaining relatively consistent within the IED segments of a same patient, and (2) the pFC maps have an advanced capability in terms of pinpointing to a region of interest of the active lobe, and as such can play a critical role in providing insight as to a region of interest where the 3D source might be located when solving the ill-posed inverse problem.INDEX TERMS Focal epilepsy, Interictal Epileptiform Discharges (IEDs), penalized Functional Connectivity (pFC), penalized Weighted Phase Lag Index (pWPLI).

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Detecting connectivity in EEG: A comparative study of data-driven effective connectivity measures

Cited by 26 publications

References 53 publications

The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges

The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges

Evaluation of Granger Causality Measures for Constructing Networks from Multivariate Time Series

Penalized Functional Connectivity Maps for Patients With Focal Epilepsy

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