Graph Theoretical Characteristics of EEG-Based Functional Brain Networks in Patients With Epilepsy: The Effect of Reference Choice and Volume Conduction

It is well-established that both volume conduction and the choice of recording reference (montage) affect the correlation measures obtained from scalp EEG, both in the time and frequency domains. As a result, a number of correlation measures have been proposed aiming to reduce these effects. In our previous work, we have showed that scalp-EEG based functional brain networks in patients with epilepsy exhibit clear periodic patterns at different time scales and that these patterns are strongly correlated to seizure onset, particularly at shorter time scales (around 3 and 5 h), which has important clinical implications. In the present work, we use the same long-duration clinical scalp EEG data (multiple days) to investigate the extent to which the aforementioned results are affected by the choice of reference choice and correlation measure, by considering several widely used montages as well as correlation metrics that are differentially sensitive to the effects of volume conduction. Specifically, we compare two standard and commonly used linear correlation measures, cross-correlation in the time domain, and coherence in the frequency domain, with measures that account for zero-lag correlations: corrected cross-correlation, imaginary coherence, phase lag index, and weighted phase lag index. We show that the graphs constructed with corrected cross-correlation and WPLI are more stable across different choices of reference. Also, we demonstrate that all the examined correlation measures revealed similar periodic patterns in the obtained graph measures when the bipolar and common reference (Cz) montage were used. This includes circadian-related periodicities (e.g., a clear increase in connectivity during sleep periods as compared to awake periods), as well as periodicities at shorter time scales (around 3 and 5 h). On the other hand, these results were affected to a large degree when the average reference montage was used in combination with standard cross-correlation, coherence, imaginary coherence, and PLI, which is likely due to the low number of electrodes and inadequate electrode coverage of the scalp. Finally, we demonstrate that the correlation between seizure onset and the brain network periodicities is preserved when corrected cross-correlation and WPLI were used for all the examined montages. This suggests that, even in the standard clinical setting of EEG recording in epilepsy where only a limited number of scalp EEG measurements are available, graph-theoretic quantification of periodic patterns using appropriate montage, and correlation measures corrected for volume conduction provides useful insights into seizure onset.

[1]  R. Guevara,et al.  What Can We Really Say About Neuronal Synchrony? , 2005 .

[2]  Rodrigo Quian Quiroga,et al.  Nonlinear multivariate analysis of neurophysiological signals , 2005, Progress in Neurobiology.

[3]  José Luis Pérez Velazquez,et al.  Phase synchronization measurements using electroencephalographic recordings , 2007, Neuroinformatics.

[4]  Nicholas I. Fisher,et al.  Statistical Analysis of Circular Data , 1993 .

[5]  Paul L. Nunez,et al.  REST: A good idea but not the gold standard , 2010, Clinical Neurophysiology.

[6]  Jos J. Eggermont,et al.  Neural connectivity only accounts for a small part of neural correlation in auditory cortex , 1996, Experimental Brain Research.

[7]  Emily A. Mirro,et al.  Multi-day rhythms modulate seizure risk in epilepsy , 2018, Nature Communications.

[8]  Klaus Lehnertz,et al.  Evolving networks in the human epileptic brain , 2013, 1309.4039.

[9]  Motoaki Kawanabe,et al.  Modeling Sparse Connectivity Between Underlying Brain Sources for EEG/MEG , 2009, IEEE Transactions on Biomedical Engineering.

[10]  N. I. Fisher,et al.  Statistical Analysis of Circular Data , 1993 .

[11]  Filippo Zappasodi,et al.  Impact of the reference choice on scalp EEG connectivity estimation , 2016, Journal of neural engineering.

[12]  Gareth R. Barnes,et al.  Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required , 2012, Neuroscience Letters.

[13]  Robert W Thatcher,et al.  Coherence, Phase Differences, Phase Shift, and Phase Lock in EEG/ERP Analyses , 2012, Developmental neuropsychology.

[14]  Bin He,et al.  Graph analysis of epileptogenic networks in human partial epilepsy , 2011, Epilepsia.

[15]  Laura Tassi,et al.  Epileptogenic networks of type II focal cortical dysplasia: A stereo-EEG study , 2012, NeuroImage.

[16]  Guido Nolte,et al.  The use of standardized infinity reference in EEG coherency studies , 2007, NeuroImage.

[17]  N. Crone,et al.  Network dynamics of the brain and influence of the epileptic seizure onset zone , 2014, Proceedings of the National Academy of Sciences.

[18]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[19]  J. Fermaglich Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[20]  A. Hadjipapas,et al.  Multi-scale periodicities in the functional brain networks of patients with epilepsy and their effect on seizure detection , 2018, bioRxiv.

[21]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[22]  D. Tucker,et al.  EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. , 1997, Electroencephalography and clinical neurophysiology.

[23]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[24]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[25]  Dezhong Yao,et al.  How do reference montage and electrodes setup affect the measured scalp EEG potentials? , 2018, Journal of neural engineering.

[26]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[27]  J. Scargle Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data , 1982 .

[28]  B. H. Petty How Important is It , 1955 .

[29]  P. Nunez,et al.  Electric fields of the brain , 1981 .

[30]  Qian Wang,et al.  Revealing the Dysfunction of Schematic Facial-Expression Processing in Schizophrenia: A Comparative Study of Different References , 2017, Front. Neurosci..

[31]  Klaus Lehnertz,et al.  How important is the seizure onset zone for seizure dynamics? , 2014, Seizure.

[32]  Yu Ping Wang,et al.  Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference , 2014, Physiological measurement.

[33]  Pedro A. Valdes-Sosa,et al.  How do reference montage and electrodes setup affect the measured scalp EEG potentials? , 2018, Journal of neural engineering.

[34]  Slawomir J. Nasuto,et al.  Automatic Artefact Removal from Event-related Potentials via Clustering , 2007, J. VLSI Signal Process..

[35]  Dezhong Yao,et al.  Is the Surface Potential Integral of a Dipole in a Volume Conductor Always Zero? A Cloud Over the Average Reference of EEG and ERP , 2017, Brain Topography.

[36]  Peng Xu,et al.  A comparative study of different references for EEG default mode network: The use of the infinity reference , 2010, Clinical Neurophysiology.

[37]  Philipp Berens,et al.  CircStat: AMATLABToolbox for Circular Statistics , 2009, Journal of Statistical Software.

[38]  Uri T Eden,et al.  Emergence of Persistent Networks in Long-Term Intracranial EEG Recordings , 2011, The Journal of Neuroscience.

[39]  Georgios D. Mitsis,et al.  On the Effect of Volume Conduction on Graph Theoretic Measures of Brain Networks in Epilepsy , 2013 .

[40]  D. Tucker,et al.  EEG coherency II: experimental comparisons of multiple measures , 1999, Clinical Neurophysiology.

[41]  Robert Oostenveld,et al.  An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias , 2011, NeuroImage.

[42]  Claudio Pollo,et al.  Detecting Functional Hubs of Ictogenic Networks , 2014, Brain Topography.

[43]  Klaus Lehnertz,et al.  Long-term variability of global statistical properties of epileptic brain networks. , 2010, Chaos.

[44]  M. Kramer,et al.  Emergent network topology at seizure onset in humans , 2008, Epilepsy Research.

[45]  Peng Xu,et al.  MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG , 2017, Front. Neurosci..

[46]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[47]  D. Halliday,et al.  Volume conduction effects in brain network inference from electroencephalographic recordings using phase lag index , 2012, Journal of Neuroscience Methods.

[48]  A. Hadjipapas,et al.  Epileptic seizure onset correlates with long term EEG functional brain network properties , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[49]  D. Yao,et al.  A method to standardize a reference of scalp EEG recordings to a point at infinity , 2001, Physiological measurement.

[50]  Robert Oostenveld,et al.  A comparative study of different references for EEG spectral mapping: the issue of the neutral reference and the use of the infinity reference , 2005, Physiological measurement.