Literature DB >> 27888520

Identifying the epileptogenic zone in interictal resting-state MEG source-space networks.

Ida A Nissen1, Cornelis J Stam1, Jaap C Reijneveld2, Ilse E C W van Straaten1, Eef J Hendriks3, Johannes C Baayen4, Philip C De Witt Hamer4, Sander Idema4, Arjan Hillebrand1.   

Abstract

OBJECTIVE: In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the epileptogenic zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the epileptogenic zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks.
METHODS: Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas.
RESULTS: Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity). SIGNIFICANCE: Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the epileptogenic zone in patients without interictal abnormalities. Wiley Periodicals, Inc.
© 2016 International League Against Epilepsy.

Entities:  

Keywords:  Beamformer-based virtual electrodes; Betweenness centrality; Epilepsy; Functional network; Hub; Neurosurgery

Mesh:

Year:  2016        PMID: 27888520     DOI: 10.1111/epi.13622

Source DB:  PubMed          Journal:  Epilepsia        ISSN: 0013-9580            Impact factor:   5.864


  25 in total

Review 1.  Astrocytes and Glutamine Synthetase in Epileptogenesis.

Authors:  Tore Eid; Tih-Shih W Lee; Peter Patrylo; Hitten P Zaveri
Journal:  J Neurosci Res       Date:  2018-07-18       Impact factor: 4.164

2.  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

3.  Mapping Functional Connectivity of Epileptogenic Networks through Virtual Implantation.

Authors:  Ludovica Corona; Eleonora Tamilia; Joseph R Madsen; Steven M Stufflebeam; Phillip L Pearl; Christos Papadelis
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2021-11

Review 4.  Localizing the Epileptogenic Zone with Novel Biomarkers.

Authors:  Christos Papadelis; M Scott Perry
Journal:  Semin Pediatr Neurol       Date:  2021-08-20       Impact factor: 3.042

5.  Electroencephalography Network Effects of Corpus Callosotomy in Patients with Lennox-Gastaut Syndrome.

Authors:  Jun-Ge Liang; Dongpyo Lee; Song Ee Youn; Heung Dong Kim; Nam-Young Kim
Journal:  Front Neurol       Date:  2017-09-04       Impact factor: 4.003

6.  Deep Source Localization with Magnetoencephalography Based on Sensor Array Decomposition and Beamforming.

Authors:  Yegang Hu; Yicong Lin; Baoshan Yang; Guangrui Tang; Tao Liu; Yuping Wang; Jicong Zhang
Journal:  Sensors (Basel)       Date:  2017-08-11       Impact factor: 3.576

7.  The Effect of Head Model Simplification on Beamformer Source Localization.

Authors:  Frank Neugebauer; Gabriel Möddel; Stefan Rampp; Martin Burger; Carsten H Wolters
Journal:  Front Neurosci       Date:  2017-11-09       Impact factor: 4.677

8.  An evaluation of kurtosis beamforming in magnetoencephalography to localize the epileptogenic zone in drug resistant epilepsy patients.

Authors:  Michael B H Hall; Ida A Nissen; Elisabeth C W van Straaten; Paul L Furlong; Caroline Witton; Elaine Foley; Stefano Seri; Arjan Hillebrand
Journal:  Clin Neurophysiol       Date:  2018-03-09       Impact factor: 3.708

9.  A comparison between scalp- and source-reconstructed EEG networks.

Authors:  Margherita Lai; Matteo Demuru; Arjan Hillebrand; Matteo Fraschini
Journal:  Sci Rep       Date:  2018-08-16       Impact factor: 4.379

10.  Localization of the Epileptogenic Zone Using Interictal MEG and Machine Learning in a Large Cohort of Drug-Resistant Epilepsy Patients.

Authors:  Ida A Nissen; Cornelis J Stam; Elisabeth C W van Straaten; Viktor Wottschel; Jaap C Reijneveld; Johannes C Baayen; Philip C de Witt Hamer; Sander Idema; Demetrios N Velis; Arjan Hillebrand
Journal:  Front Neurol       Date:  2018-08-07       Impact factor: 4.003

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