Literature DB >> 25556401

Temporal and spatial characteristics of high frequency oscillations as a new biomarker in epilepsy.

Matthias Dümpelmann1, Julia Jacobs, Andreas Schulze-Bonhage.   

Abstract

OBJECTIVE: Interictal high frequency oscillations (HFOs) are a promising candidate as a biomarker in epilepsy as well as for defining the seizure-onset zone as for the prediction of the surgical outcome after epilepsy surgery. The purpose of the study is to investigate properties of HFOs in long-term recordings with respect to the sleep-wake cycle and anatomic regions to verify previous results based on observations from short intervals and patients mainly with temporal lobe epilepsy to the analysis of hours of recordings and focal epilepsies with extratemporal origin.
METHODS: Automatic HFO detection using a radial basis function neural network detector was performed in long-term recordings of 15 presurgical patients investigated with subdural strip, grid, and depth contacts. Periods with visual marked sleep stages based on parallel scalp recordings from two consecutive nights were compared to awake intervals. Statistical analysis was based on the Kruskal-Wallis test, Mann-Whitney U-test and Spearman's rank correlations.
RESULTS: HFO rates in seizure-onset contacts differed from other brain regions independent of the sleep-wake cycle. For temporal contacts, the HFO rate increased significantly with sleep stage. In addition, contacts covering the parietal lobe, including rolandic cortex, showed a significant increase of HFO rates during sleep. However, no significant HFO rate changes depending on the sleep-wake cycle were found for frontal contacts. SIGNIFICANCE: The rate of interictal HFOs predicted the SOZ with statistical significance at the group level, but properties other than the HFO rate may need to be considered to improve the diagnostic utility of HFOs. This study gives evidence that the modulation of HFO rates by states of the sleep-wake cycle has particular characteristics within different neocortical regions and in mesiotemporal structures, and contributes to the establishment of HFOs as a biomarker in epilepsy. Wiley Periodicals, Inc.
© 2014 International League Against Epilepsy.

Entities:  

Keywords:  Automatic detection; Biomarker; Epilepsy surgery; High frequency oscillations; Invasive EEG

Mesh:

Substances:

Year:  2014        PMID: 25556401     DOI: 10.1111/epi.12844

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


  25 in total

Review 1.  High-frequency oscillations: The state of clinical research.

Authors:  Birgit Frauscher; Fabrice Bartolomei; Katsuhiro Kobayashi; Jan Cimbalnik; Maryse A van 't Klooster; Stefan Rampp; Hiroshi Otsubo; Yvonne Höller; Joyce Y Wu; Eishi Asano; Jerome Engel; Philippe Kahane; Julia Jacobs; Jean Gotman
Journal:  Epilepsia       Date:  2017-06-30       Impact factor: 5.864

2.  Progress and Remaining Challenges in the Application of High Frequency Oscillations as Biomarkers of Epileptic Brain.

Authors:  Fatemeh Khadjevand; Jan Cimbalnik; Gregory A Worrell
Journal:  Curr Opin Biomed Eng       Date:  2017-09-22

3.  Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone.

Authors:  Michael D Nunez; Krit Charupanit; Indranil Sen-Gupta; Beth A Lopour; Jack J Lin
Journal:  J Neural Eng       Date:  2022-02-22       Impact factor: 5.043

4.  Universal automated high frequency oscillation detector for real-time, long term EEG.

Authors:  Stephen V Gliske; Zachary T Irwin; Kathryn A Davis; Kinshuk Sahaya; Cynthia Chestek; William C Stacey
Journal:  Clin Neurophysiol       Date:  2015-07-22       Impact factor: 3.708

5.  Seizure onset location shapes dynamics of initiation.

Authors:  Pariya Salami; Noam Peled; Jessica K Nadalin; Louis-Emmanuel Martinet; Mark A Kramer; Jong W Lee; Sydney S Cash
Journal:  Clin Neurophysiol       Date:  2020-05-29       Impact factor: 3.708

6.  Effect of sampling rate and filter settings on High Frequency Oscillation detections.

Authors:  Stephen V Gliske; Zachary T Irwin; Cynthia Chestek; William C Stacey
Journal:  Clin Neurophysiol       Date:  2016-07-15       Impact factor: 3.708

Review 7.  Is There a Relation between EEG-Slow Waves and Memory Dysfunction in Epilepsy? A Critical Appraisal.

Authors:  Yvonne Höller; Eugen Trinka
Journal:  Front Hum Neurosci       Date:  2015-06-11       Impact factor: 3.169

8.  Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp electroencephalogram.

Authors:  Jessica K Nadalin; Uri T Eden; Xue Han; R Mark Richardson; Catherine J Chu; Mark A Kramer
Journal:  J Neurosci Methods       Date:  2021-06-04       Impact factor: 2.987

9.  Detection of Epileptic Seizures Using Phase-Amplitude Coupling in Intracranial Electroencephalography.

Authors:  Kohtaroh Edakawa; Takufumi Yanagisawa; Haruhiko Kishima; Ryohei Fukuma; Satoru Oshino; Hui Ming Khoo; Maki Kobayashi; Masataka Tanaka; Toshiki Yoshimine
Journal:  Sci Rep       Date:  2016-05-05       Impact factor: 4.379

10.  High-frequency oscillations in epilepsy and surgical outcome. A meta-analysis.

Authors:  Yvonne Höller; Raoul Kutil; Lukas Klaffenböck; Aljoscha Thomschewski; Peter M Höller; Arne C Bathke; Julia Jacobs; Alexandra C Taylor; Raffaele Nardone; Eugen Trinka
Journal:  Front Hum Neurosci       Date:  2015-10-20       Impact factor: 3.169

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