Literature DB >> 27140287

Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy.

Sandra Courtens1, Bruno Colombet1, Agnès Trébuchon1,2, Andrea Brovelli3, Fabrice Bartolomei1,2, Christian G Bénar1.   

Abstract

The reference electrophysiological pattern at seizure onset is the "rapid discharge," as visible on intracerebral electroencephalography (EEG). This discharge typically corresponds to a decrease of synchrony across brain areas. In contrast, the preictal period can exhibit patterns of increased synchrony, which can be quantified by network measures. Our objective was to compare preictal synchrony with a quantification of the rapid discharge as provided by the epileptogenicity index (EI). We investigated 24 seizures from 12 patients recorded by stereotaxic EEG (SEEG). Seizures were classified visually as containing preictal synchrony or not. We computed pairwise nonlinear correlation (h(2)) across channels in the 8 sec preceding the rapid discharge. The sum of ingoing and outgoing links (IN and OUT node strength), as well as the sum of all links (total strength, TOT) were computed for each region. We tested several filtering schemes, and quantified the capacity of each strength measure to serve as a detector of regions with high EI values using a receiver operating characteristic (ROC) analysis. We found that the best correspondence between node strength and EI was obtained for the OUT and TOT measures, for signals filtered in the 15-40 Hz band-that is, for the band corresponding to the spiky part of epileptic discharges. In agreement with these results, we also found that the ROC results were improved when considering only seizures with visible synchronous patterns in the preictal period. Our results suggest that measuring strength of preictal connectivity graphs can bring useful clinical information on the epileptogenic zone.

Entities:  

Keywords:  epilepsy; functional connectivity; graph theory; partial seizures; stereoelectroencephalography (SEEG)

Mesh:

Year:  2016        PMID: 27140287     DOI: 10.1089/brain.2015.0397

Source DB:  PubMed          Journal:  Brain Connect        ISSN: 2158-0014


  8 in total

1.  Electrophysiological Brain Connectivity: Theory and Implementation.

Authors:  Bin He; Laura Astolfi; Pedro A Valdes-Sosa; Daniele Marinazzo; Satu Palva; Christian G Benar; Christoph M Michel; Thomas Koenig
Journal:  IEEE Trans Biomed Eng       Date:  2019-05-07       Impact factor: 4.538

2.  Connectivity Alterations in Emotional and Cognitive Networks During a Manic State Induced by Direct Electrical Stimulation.

Authors:  Julia Scholly; Adrien Gras; Maxime Guye; Mathias Bilger; Maria Paola Valenti Hirsch; Edouard Hirsch; Alexander Timofeev; Pierre Vidailhet; Christian G Bénar; Fabrice Bartolomei
Journal:  Brain Topogr       Date:  2022-09-07       Impact factor: 4.275

3.  Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG.

Authors:  Pieter van Mierlo; Octavian Lie; Willeke Staljanssens; Ana Coito; Serge Vulliémoz
Journal:  Brain Topogr       Date:  2018-04-26       Impact factor: 3.020

4.  Deep brain activities can be detected with magnetoencephalography.

Authors:  F Pizzo; N Roehri; S Medina Villalon; A Trébuchon; S Chen; S Lagarde; R Carron; M Gavaret; B Giusiano; A McGonigal; F Bartolomei; J M Badier; C G Bénar
Journal:  Nat Commun       Date:  2019-02-27       Impact factor: 14.919

5.  Changes in Global and Nodal Networks in Patients With Unipolar Depression After 3-Week Repeated Transcranial Magnetic Stimulation Treatment.

Authors:  Kuk-In Jang; Miseon Shim; Sangmin Lee; Han-Jeong Hwang; Jeong-Ho Chae
Journal:  Front Psychiatry       Date:  2019-10-09       Impact factor: 4.157

6.  Increased gamma and decreased fast ripple connections of epileptic tissue: A high-frequency directed network approach.

Authors:  Willemiek J E M Zweiphenning; Hanneke M Keijzer; Eric van Diessen; Maryse A van 't Klooster; Nicole E C van Klink; Frans S S Leijten; Peter C van Rijen; Michel J A M van Putten; Kees P J Braun; Maeike Zijlmans
Journal:  Epilepsia       Date:  2019-07-22       Impact factor: 5.864

Review 7.  Current Conceptual Understanding of the Epileptogenic Network From Stereoelectroencephalography-Based Connectivity Inferences.

Authors:  Kanupriya Gupta; Pulkit Grover; Taylor J Abel
Journal:  Front Neurol       Date:  2020-11-25       Impact factor: 4.003

8.  Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation.

Authors:  Dorien van Blooijs; Frans S S Leijten; Peter C van Rijen; Hil G E Meijer; Geertjan J M Huiskamp
Journal:  Hum Brain Mapp       Date:  2018-07-21       Impact factor: 5.038

  8 in total

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