• Something wrong with this record ?

Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom

V. Dimakopoulos, J. Gotman, W. Stacey, N. von Ellenrieder, J. Jacobs, C. Papadelis, J. Cimbalnik, G. Worrell, MR. Sperling, M. Zijlmans, L. Imbach, B. Frauscher, J. Sarnthein

. 2022 ; 4 (3) : fcac151. [pub] 20220609

Language English Country Great Britain

Document type Journal Article

In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80-250 Hz) and in the fast ripple (250-500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient's HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.

References provided by Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc22023873
003      
CZ-PrNML
005      
20221031095305.0
007      
ta
008      
221010s2022 xxk f 000 0|eng||
009      
AR
024    7_
$a 10.1093/braincomms/fcac151 $2 doi
035    __
$a (PubMed)35770134
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxk
100    1_
$a Dimakopoulos, Vasileios $u Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland $1 https://orcid.org/000000019490565X
245    10
$a Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom / $c V. Dimakopoulos, J. Gotman, W. Stacey, N. von Ellenrieder, J. Jacobs, C. Papadelis, J. Cimbalnik, G. Worrell, MR. Sperling, M. Zijlmans, L. Imbach, B. Frauscher, J. Sarnthein
520    9_
$a In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80-250 Hz) and in the fast ripple (250-500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient's HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.
655    _2
$a časopisecké články $7 D016428
700    1_
$a Gotman, Jean $u Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
700    1_
$a Stacey, William $u Department of Neurology and Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, MI, USA $1 https://orcid.org/0000000283598057
700    1_
$a von Ellenrieder, Nicolás $u Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
700    1_
$a Jacobs, Julia $u Alberta Children's Hospital, University of Calgary, Calgary, Canada
700    1_
$a Papadelis, Christos $u Cook Children's Health Care System, Fort Worth, TX, USA $1 https://orcid.org/0000000161259217
700    1_
$a Cimbalnik, Jan $u St. Anne's University Hospital, Brno, Czech Republic
700    1_
$a Worrell, Gregory $u Department of Neurology, Mayo Clinic, Rochester, MN, USA
700    1_
$a Sperling, Michael R $u Department of Neurology, Jefferson University Hospitals, Philadelphia, PA, USA
700    1_
$a Zijlmans, Maike $u University Medical Center, Utrecht, and Stichting Epilepsie Instellingen Nederland (SEIN), Utrecht, The Netherlands
700    1_
$a Imbach, Lucas $u Schweizerisches Epilepsie Zentrum, Zurich, Switzerland
700    1_
$a Frauscher, Birgit $u Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
700    1_
$a Sarnthein, Johannes $u Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland $1 https://orcid.org/000000019141381X
773    0_
$w MED00205536 $t Brain communications $x 2632-1297 $g Roč. 4, č. 3 (2022), s. fcac151
856    41
$u https://pubmed.ncbi.nlm.nih.gov/35770134 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y - $z 0
990    __
$a 20221010 $b ABA008
991    __
$a 20221031095303 $b ABA008
999    __
$a ind $b bmc $g 1854069 $s 1175161
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2022 $b 4 $c 3 $d fcac151 $e 20220609 $i 2632-1297 $m Brain communications $n Brain Commun $x MED00205536
LZP    __
$a Pubmed-20221010

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...