Atlas of the normal intracranial electroencephalogram: neurophysiological awake activity in different cortical areas

Frauscher, Birgit; Ellenrieder, Nicolás von; Zelmann, Rina; Doležalová, Irena; Minotti, Lorella; Olivier, André; Hall, Jeffery A.; Hoffmann, D.; Nguyen, Dang Khoa; Kahane, Philippe; Dubeau, François; Gotman, Jean

doi:10.1093/brain/awy035

Cited by 220 publications

(251 citation statements)

References 39 publications

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“…15,[17][18][19][20] As a Web-accessible resource, the Open MNI iEEG Atlas (https://mni-open-ieegatlas.research.mcgill.ca), has been made available via the LORIS neuroinformatics platform developed at the MNI. 15,[17][18][19][20] As a Web-accessible resource, the Open MNI iEEG Atlas (https://mni-open-ieegatlas.research.mcgill.ca), has been made available via the LORIS neuroinformatics platform developed at the MNI.…”

Section: Coregistration and Anatomical Localization Of Electrodes Andmentioning

confidence: 99%

“…15 We merged some of these regions to increase the number of channels per region and hence the reliability of the results. 15 We merged some of these regions to increase the number of channels per region and hence the reliability of the results.…”

Section: Grouping Regions For Hfo Rate Analysismentioning

confidence: 99%

“…15 We were able to select approximately 18% of all channels. In most of them, however, some electrodes need to be placed in nonepileptogenic zones apparently devoid of structural or physiological anomalies.…”

Section: Selection Of Channels From Presumably Normal Brain Regionsmentioning

confidence: 99%

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High‐Frequency Oscillations in the Normal Human Brain

Frauscher

Ellenrieder

Zelmann

et al. 2018

Annals of Neurology

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185

178

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This multicenter atlas is the first to provide region-specific normative values for physiological HFO rates and HFA in common stereotactic space; rates above these can now be considered pathological. Physiological ripples are frequent in eloquent cortex. In contrast, physiological fast ripples are very rare, making fast ripples a good candidate for defining the epileptogenic zone. Ann Neurol 2018;84:374-385.

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Section: Coregistration and Anatomical Localization Of Electrodes Andmentioning

confidence: 99%

Section: Grouping Regions For Hfo Rate Analysismentioning

confidence: 99%

See 1 more Smart Citation

High‐Frequency Oscillations in the Normal Human Brain

Frauscher

Ellenrieder

Zelmann

et al. 2018

Annals of Neurology

Self Cite

185

178

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“…13 In fact, this study is the first to identify relative entropy in the ripple band to be the best-performing feature independent of the state of vigilance. 34,35 If this feature will be more frequently selected should it be normalized for region-specific spectral frequencies awaits further investigation. In signal processing, relative entropy reflects the randomness and spectral richness between two time-series and describes how entropy of one signal diverges from a second one.…”

Section: Feature Selectionmentioning

confidence: 99%

NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram

et al. 2019

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Objective: Interictal epileptiform anomalies such as epileptiform discharges or highfrequency oscillations show marked variations across the sleep-wake cycle. This study investigates which state of vigilance is the best to localize the epileptogenic zone (EZ) in interictal intracranial electroencephalography (EEG). Methods: Thirty patients with drug-resistant epilepsy undergoing stereo-EEG (SEEG)/sleep recording and subsequent open surgery were included; 13 patients (43.3%) had good surgical outcome (Engel class I). Sleep was scored following standard criteria. Multiple features based on the interictal EEG (interictal epileptiform discharges, high-frequency oscillations, univariate and bivariate features) were used to train a support vector machine (SVM) model to classify SEEG contacts placed in the EZ. The performance of the algorithm was evaluated by the mean area under the receiver-operating characteristic (ROC) curves (AUCs) and positive predictive values (PPVs) across 10-minute sections of wake, non-rapid eye movement sleep (NREM) stages N2 and N3, REM sleep, and their combination.Results: Highest AUCs were achieved in NREM sleep stages N2 and N3 compared to wakefulness and REM (P < .01). There was no improvement when using a combination of all four states (P > .05); the best performing features in the combined state were selected from NREM sleep. There were differences between good (Engel I) and poor (Engel II-IV) outcomes in PPV (P < .05) and AUC (P < .01) across all states. The SVM multifeature approach outperformed spikes and high-frequency oscillations (P < .01) and resulted in results similar to those of the seizure-onset zone (SOZ; P > .05). Significance: Sleep improves the localization of the EZ with best identification obtained in NREM sleep stages N2 and N3. Results based on the multifeature classification in 10 minutes of NREM sleep were not different from the results achieved by the SOZ based on 12.7 days of seizure monitoring. This finding might ultimately result in a more time-efficient intracranial presurgical investigation of focal epilepsy. K E Y W O R D Sconnectivity, drug-resistant epilepsy, high-frequency oscillations, machine learning, sleep-wake cycle | 2405 KLIMES Et aL.

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“…As an additional analysis, we determined whether anatomical variability in MI (>150 Hz & 3‐4 Hz) during slow‐wave sleep would exist among nonepileptic electrode sites, defined as those unaffected by magnetic resonance imaging (MRI) lesion, SOZ, or interictal spike discharges . We believed this analysis was necessary for validating the application of MI (>150 Hz & 3‐4 Hz) in presurgical evaluation, because previous studies of nonepileptic eloquent cortex reported that the degree of phase‐amplitude coupling between HFA and theta/alpha/beta activity was modulated by sensorimotor or cognitive tasks during wakefulness .…”

Section: Introductionmentioning

confidence: 99%