Literature DB >> 33923317

Distinction of Physiologic and Epileptic Ripples: An Electrical Stimulation Study.

Jan Schönberger1,2,3,4, Anja Knopf1,2,3, Kerstin Alexandra Klotz1,2,3,4, Matthias Dümpelmann1,3, Andreas Schulze-Bonhage1,3, Julia Jacobs2,3,5,6.   

Abstract

Ripple oscillations (80-250 Hz) are a promising biomarker of epileptic activity, but are also involved in memory consolidation, which impairs their value as a diagnostic tool. Distinguishing physiologic from epileptic ripples has been particularly challenging because usually, invasive recordings are only performed in patients with refractory epilepsy. Here, we identified 'healthy' brain areas based on electrical stimulation and hypothesized that these regions specifically generate 'pure' ripples not coupled to spikes. Intracranial electroencephalography (EEG) recorded with subdural grid electrodes was retrospectively analyzed in 19 patients with drug-resistant focal epilepsy. Interictal spikes and ripples were automatically detected in slow-wave sleep using the publicly available Delphos software. We found that rates of spikes, ripples and ripples coupled to spikes ('spike-ripples') were higher inside the seizure-onset zone (p < 0.001). A comparison of receiver operating characteristic curves revealed that spike-ripples slightly delineated the seizure-onset zone channels, but did this significantly better than spikes (p < 0.001). Ripples were more frequent in the eloquent neocortex than in the remaining non-seizure onset zone areas (p < 0.001). This was due to the higher rates of 'pure' ripples (p < 0.001; median rates 3.3/min vs. 1.4/min), whereas spike-ripple rates were not significantly different (p = 0.87). 'Pure' ripples identified 'healthy' channels significantly better than chance (p < 0.001). Our findings suggest that, in contrast to epileptic spike-ripples, 'pure' ripples are mainly physiological. They may be considered, in addition to electrical stimulation, to delineate eloquent cortex in pre-surgical patients. Since we applied open source software for detection, our approach may be generally suited to tackle a variety of research questions in epilepsy and cognitive science.

Entities:  

Keywords:  electroencephalography; epilepsy; high-frequency oscillations; intracranial; neocortical; physiologic; stimulation

Year:  2021        PMID: 33923317     DOI: 10.3390/brainsci11050538

Source DB:  PubMed          Journal:  Brain Sci        ISSN: 2076-3425


  44 in total

1.  Removing interictal fast ripples on electrocorticography linked with seizure freedom in children.

Authors:  J Y Wu; R Sankar; J T Lerner; J H Matsumoto; H V Vinters; G W Mathern
Journal:  Neurology       Date:  2010-10-06       Impact factor: 9.910

2.  A storm of fast (40-150Hz) oscillations during hypsarrhythmia in West syndrome.

Authors:  Katsuhiro Kobayashi; Tomoyuki Akiyama; Makio Oka; Fumika Endoh; Harumi Yoshinaga
Journal:  Ann Neurol       Date:  2014-11-13       Impact factor: 10.422

3.  AnyWave: a cross-platform and modular software for visualizing and processing electrophysiological signals.

Authors:  B Colombet; M Woodman; J M Badier; C G Bénar
Journal:  J Neurosci Methods       Date:  2015-01-19       Impact factor: 2.390

4.  Gamma oscillations precede interictal epileptiform spikes in the seizure onset zone.

Authors:  Liankun Ren; Michal T Kucewicz; Jan Cimbalnik; Joseph Y Matsumoto; Benjamin H Brinkmann; Wei Hu; W Richard Marsh; Fredric B Meyer; S Matthew Stead; Gregory A Worrell
Journal:  Neurology       Date:  2015-01-14       Impact factor: 9.910

5.  Scalp Ripples Can Predict Development of Epilepsy After First Unprovoked Seizure in Childhood.

Authors:  Kerstin A Klotz; Yusuf Sag; Jan Schönberger; Julia Jacobs
Journal:  Ann Neurol       Date:  2020-10-31       Impact factor: 10.422

6.  Interictal high-frequency oscillations (100-500 Hz) in the intracerebral EEG of epileptic patients.

Authors:  Elena Urrestarazu; Rahul Chander; Francçois Dubeau; Jean Gotman
Journal:  Brain       Date:  2007-07-11       Impact factor: 13.501

7.  Removing high-frequency oscillations: A prospective multicenter study on seizure outcome.

Authors:  Julia Jacobs; Joyce Y Wu; Piero Perucca; Rina Zelmann; Malenka Mader; Francois Dubeau; Gary W Mathern; Andreas Schulze-Bonhage; Jean Gotman
Journal:  Neurology       Date:  2018-08-17       Impact factor: 9.910

8.  What are the assets and weaknesses of HFO detectors? A benchmark framework based on realistic simulations.

Authors:  Nicolas Roehri; Francesca Pizzo; Fabrice Bartolomei; Fabrice Wendling; Christian-George Bénar
Journal:  PLoS One       Date:  2017-04-13       Impact factor: 3.240

9.  Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome.

Authors:  Naoto Kuroda; Masaki Sonoda; Makoto Miyakoshi; Hiroki Nariai; Jeong-Won Jeong; Hirotaka Motoi; Aimee F Luat; Sandeep Sood; Eishi Asano
Journal:  Brain Commun       Date:  2021-03-14

10.  High-frequency oscillations mirror severity of human temporal lobe seizures.

Authors:  Jan Schönberger; Nadja Birk; Daniel Lachner-Piza; Matthias Dümpelmann; Andreas Schulze-Bonhage; Julia Jacobs
Journal:  Ann Clin Transl Neurol       Date:  2019-11-21       Impact factor: 4.511

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  1 in total

1.  Are High Frequency Oscillations in Scalp EEG Related to Age?

Authors:  Philipp Franz Windhager; Adrian V Marcu; Eugen Trinka; Arne Bathke; Yvonne Höller
Journal:  Front Neurol       Date:  2022-01-27       Impact factor: 4.003

  1 in total

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