Vasilios K Kimiskidis1, Alkiviadis Tsimpiris2, Philippe Ryvlin3, Reetta Kalviainen4, Michalis Koutroumanidis5, Antonio Valentin6, Nikolaos Laskaris7, Dimitris Kugiumtzis8. 1. Laboratory of Clinical Neurophysiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece. Electronic address: kimiskid@auth.gr. 2. Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece. Electronic address: alkisser@auth.gr. 3. Department of Functional Neurology and Epileptology, Hospices Civils de Lyon, Lyon, France; Department of Clinical Neurosciences, CHUV, Lausanne, Switzerland. Electronic address: ryvlin@cermep.fr. 4. Kuopio Epilepsy Center, Department of Neurology, Kuopio University Hospital, Kuopio, Finland; Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland. Electronic address: reetta.kalviainen@kuh.fi. 5. Clinical Neurophysiology Dpt., Epilepsy, Guys, St Thomas' NHS Foundation Trust, Kings College London, London, UK; Department of Academic Neurosciences, Kings College London, London, UK. Electronic address: Michael.Koutroumanidis@gstt.nhs.uk. 6. Department of Basic and Clinical Neuroscience, KCL-IOPP, London, UK; Department of Clinical Neurophysiology, KCH, London, UK; Department of Human Physiology, Universidad Complutense Madrid, Madrid, Spain. Electronic address: Antonio.valentin@kcl.ac.uk. 7. Artificial Intelligence Information Analysis Lab, Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece; Neuroinformatics Group, Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece. Electronic address: laskaris@aiia.csd.auth.gr. 8. Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece. Electronic address: dkugiu@auth.gr.
Abstract
OBJECTIVES: (A) To develop a TMS-EEG stimulation and data analysis protocol in genetic generalized epilepsy (GGE). (B) To investigate the diagnostic accuracy of TMS-EEG in GGE. METHODS: Pilot experiments resulted in the development and optimization of a paired-pulse TMS-EEG protocol at rest, during hyperventilation (HV), and post-HV combined with multi-level data analysis. This protocol was applied in 11 controls (C) and 25 GGE patients (P), further dichotomized into responders to antiepileptic drugs (R, n=13) and non-responders (n-R, n=12).Features (n=57) extracted from TMS-EEG responses after multi-level analysis were given to a feature selection scheme and a Bayesian classifier, and the accuracy of assigning participants into the classes P-C and R-nR was computed. RESULTS: On the basis of the optimal feature subset, the cross-validated accuracy of TMS-EEG for the classification P-C was 0.86 at rest, 0.81 during HV and 0.92 at post-HV, whereas for R-nR the corresponding figures are 0.80, 0.78 and 0.65, respectively. Applying a fusion approach on all conditions resulted in an accuracy of 0.84 for the classification P-C and 0.76 for the classification R-nR. CONCLUSION: TMS-EEG can be used for diagnostic purposes and for assessing the response to antiepileptic drugs. SIGNIFICANCE: TMS-EEG holds significant diagnostic potential in GGE.
OBJECTIVES: (A) To develop a TMS-EEG stimulation and data analysis protocol in genetic generalized epilepsy (GGE). (B) To investigate the diagnostic accuracy of TMS-EEG in GGE. METHODS: Pilot experiments resulted in the development and optimization of a paired-pulse TMS-EEG protocol at rest, during hyperventilation (HV), and post-HV combined with multi-level data analysis. This protocol was applied in 11 controls (C) and 25 GGE patients (P), further dichotomized into responders to antiepileptic drugs (R, n=13) and non-responders (n-R, n=12).Features (n=57) extracted from TMS-EEG responses after multi-level analysis were given to a feature selection scheme and a Bayesian classifier, and the accuracy of assigning participants into the classes P-C and R-nR was computed. RESULTS: On the basis of the optimal feature subset, the cross-validated accuracy of TMS-EEG for the classification P-C was 0.86 at rest, 0.81 during HV and 0.92 at post-HV, whereas for R-nR the corresponding figures are 0.80, 0.78 and 0.65, respectively. Applying a fusion approach on all conditions resulted in an accuracy of 0.84 for the classification P-C and 0.76 for the classification R-nR. CONCLUSION: TMS-EEG can be used for diagnostic purposes and for assessing the response to antiepileptic drugs. SIGNIFICANCE: TMS-EEG holds significant diagnostic potential in GGE.
Authors: Prisca R Bauer; Annika A de Goede; William M Stern; Adam D Pawley; Fahmida A Chowdhury; Robert M Helling; Romain Bouet; Stiliyan N Kalitzin; Gerhard H Visser; Sanjay M Sisodiya; John C Rothwell; Mark P Richardson; Michel J A M van Putten; Josemir W Sander Journal: Brain Date: 2018-02-01 Impact factor: 13.501
Authors: Henri Lehtinen; Jyrki P Mäkelä; Teemu Mäkelä; Pantelis Lioumis; Liisa Metsähonkala; Laura Hokkanen; Juha Wilenius; Eija Gaily Journal: Epilepsia Open Date: 2018-04-06
Authors: Simone Rossi; Andrea Antal; Sven Bestmann; Marom Bikson; Carmen Brewer; Jürgen Brockmöller; Linda L Carpenter; Massimo Cincotta; Robert Chen; Jeff D Daskalakis; Vincenzo Di Lazzaro; Michael D Fox; Mark S George; Donald Gilbert; Vasilios K Kimiskidis; Giacomo Koch; Risto J Ilmoniemi; Jean Pascal Lefaucheur; Letizia Leocani; Sarah H Lisanby; Carlo Miniussi; Frank Padberg; Alvaro Pascual-Leone; Walter Paulus; Angel V Peterchev; Angelo Quartarone; Alexander Rotenberg; John Rothwell; Paolo M Rossini; Emiliano Santarnecchi; Mouhsin M Shafi; Hartwig R Siebner; Yoshikatzu Ugawa; Eric M Wassermann; Abraham Zangen; Ulf Ziemann; Mark Hallett Journal: Clin Neurophysiol Date: 2020-10-24 Impact factor: 4.861