Intracranial EEG biomarkers for seizure lateralization in rapidly-bisynchronous epilepsy after laser corpus callosotomy.

Objective: It has been asserted that high-frequency analysis of intracranial EEG (iEEG) data may yield information useful in localizing epileptogenic foci. Methods: We tested whether proposed biomarkers could predict lateralization based on iEEG data collected prior to corpus callostomy (CC) in patients with bisynchronous epilepsy, whose seizures lateralized definitively post-CC. Lateralization data derived from algorithmically-computed ictal phase-locked high gamma (PLHG), high gamma amplitude (HGA) and line length (LL), as well as interictal high-frequency oscillation (HFO) and interictal epileptiform discharge (IED) rate metrics were compared against ground-truth lateralization from post-CC ictal iEEG. Results: Pre-CC unilateral IEDs were more frequent on the more-pathologic side in all subjects. HFO rate predicted lateralization in one subject, but was sensitive to detection threshold. On pre-CC data, no ictal metric showed better predictive power than any other. All post-corpus callosotomy seizures lateralized to the pathological hemisphere using PLHG, HGA and LL metrics. Conclusions: While quantitative metrics of IED rate and ictal HGA, PHLG and LL all accurately lateralize based on post-CC iEEG, only IED rate consistently does so based on pre-CC data. Significance: Quantitative analysis of IEDs may be useful in localizing seizure pathology. More work is needed to develop reliable techniques for high-frequency iEEG analysis.

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Intracranial EEG Biomarkers for Seizure Lateralization in Rapidly-Bisynchronous Epilepsy After Laser Corpus Callosotomy

Frontiers in Neurology ◽

10.3389/fneur.2021.696492 ◽

2021 ◽

Vol 12 ◽

Author(s):

Simon Khuvis ◽

Sean T. Hwang ◽

Ashesh D. Mehta

Keyword(s):

High Frequency ◽

Detection Threshold ◽

Low Frequency ◽

Ground Truth ◽

Line Length ◽

High Frequency Oscillation ◽

Corpus Callosotomy ◽

Intracranial Eeg ◽

Interictal Epileptiform Discharge ◽

High Gamma

Objective: It has been asserted that high-frequency analysis of intracranial EEG (iEEG) data may yield information useful in localizing epileptogenic foci.Methods: We tested whether proposed biomarkers could predict lateralization based on iEEG data collected prior to corpus callosotomy (CC) in three patients with bisynchronous epilepsy, whose seizures lateralized definitively post-CC. Lateralization data derived from algorithmically-computed ictal phase-locked high gamma (PLHG), high gamma amplitude (HGA), and low-frequency (filtered) line length (LFLL), as well as interictal high-frequency oscillation (HFO) and interictal epileptiform discharge (IED) rate metrics were compared against ground-truth lateralization from post-CC ictal iEEG.Results: Pre-CC unilateral IEDs were more frequent on the more-pathologic side in all subjects. HFO rate predicted lateralization in one subject, but was sensitive to detection threshold. On pre-CC data, no ictal metric showed better predictive power than any other. All post-corpus callosotomy seizures lateralized to the pathological hemisphere using PLHG, HGA, and LFLL metrics.Conclusions: While quantitative metrics of IED rate and ictal HGA, PHLG, and LFLL all accurately lateralize based on post-CC iEEG, only IED rate consistently did so based on pre-CC data.Significance: Quantitative analysis of IEDs may be useful in lateralizing seizure pathology. More work is needed to develop reliable techniques for high-frequency iEEG analysis.

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Spatial variation in high-frequency oscillation rates and amplitudes in intracranial EEG

Neurology ◽

10.1212/wnl.0000000000004998 ◽

2018 ◽

Vol 90 (8) ◽

pp. e639-e646 ◽

Cited By ~ 23

Author(s):

Hari Guragain ◽

Jan Cimbalnik ◽

Matt Stead ◽

David M. Groppe ◽

Brent M. Berry ◽

...

Keyword(s):

High Frequency ◽

Anatomic Variation ◽

Brain Regions ◽

Brain Atlas ◽

Reproducible Research ◽

High Frequency Oscillation ◽

Seizure Onset ◽

Frequency Oscillation ◽

Intracranial Eeg ◽

Seizure Onset Zone

ObjectiveTo assess the variation in baseline and seizure onset zone interictal high-frequency oscillation (HFO) rates and amplitudes across different anatomic brain regions in a large cohort of patients.MethodsSeventy patients who had wide-bandwidth (5 kHz) intracranial EEG (iEEG) recordings during surgical evaluation for drug-resistant epilepsy between 2005 and 2014 who had high-resolution MRI and CT imaging were identified. Discrete HFOs were identified in 2-hour segments of high-quality interictal iEEG data with an automated detector. Electrode locations were determined by coregistering the patient's preoperative MRI with an X-ray CT scan acquired immediately after electrode implantation and correcting electrode locations for postimplant brain shift. The anatomic locations of electrodes were determined using the Desikan-Killiany brain atlas via FreeSurfer. HFO rates and mean amplitudes were measured in seizure onset zone (SOZ) and non-SOZ electrodes, as determined by the clinical iEEG seizure recordings. To promote reproducible research, imaging and iEEG data are made freely available (msel.mayo.edu).ResultsBaseline (non-SOZ) HFO rates and amplitudes vary significantly in different brain structures, and between homologous structures in left and right hemispheres. While HFO rates and amplitudes were significantly higher in SOZ than non-SOZ electrodes when analyzed regardless of contact location, SOZ and non-SOZ HFO rates and amplitudes were not separable in some lobes and structures (e.g., frontal and temporal neocortex).ConclusionsThe anatomic variation in SOZ and non-SOZ HFO rates and amplitudes suggests the need to assess interictal HFO activity relative to anatomically accurate normative standards when using HFOs for presurgical planning.

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210. Spatiotemporal patterns of high frequency oscillation from intracranial EEG before and during seizure

Journal of Clinical Neuroscience ◽

10.1016/j.jocn.2008.07.037 ◽

2009 ◽

Vol 16 (3) ◽

pp. 472-473

Author(s):

Karen Fuller ◽

Dean Freestone ◽

Simon Vogrin ◽

Alan Lai ◽

Levin Kuhlmann ◽

...

Keyword(s):

High Frequency ◽

Spatiotemporal Patterns ◽

High Frequency Oscillation ◽

Frequency Oscillation ◽

Intracranial Eeg

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Identification of seizure onset zone using automatically detected spike and high-frequency oscillation in human intracranial EEG

2016 24th Signal Processing and Communication Application Conference (SIU) ◽

10.1109/siu.2016.7496221 ◽

2016 ◽

Author(s):

Su Liu ◽

Zhiyi Sha ◽

Aviva Abosch ◽

Thomas Henry ◽

Nuri Firat Ince

Keyword(s):

High Frequency ◽

High Frequency Oscillation ◽

Seizure Onset ◽

Frequency Oscillation ◽

Intracranial Eeg ◽

Seizure Onset Zone

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Dynamic Spectral Imaging: Online and Offline Functional Brain Mapping Using High-Frequency Activity [50–150 Hz] in SEEG

Invasive Studies of the Human Epileptic Brain ◽

10.1093/med/9780198714668.003.0033 ◽

2018 ◽

pp. 453-464

Author(s):

Jean-Philippe Lachaux

Keyword(s):

High Frequency ◽

Large Scale ◽

Human Cognition ◽

Gamma Activity ◽

Intracranial Eeg ◽

Cortical Dynamics ◽

Functional Brain Mapping ◽

High Gamma ◽

High Frequency Activity ◽

Eeg Recordings

At the end of the twentieth century, a handful of research groups discovered that neural processing leaves a characteristic signature in intracranial EEG recordings: an increase of power in a broad frequency range above 50 Hz, dubbed ‘high-gamma’ of high-frequency activity ([50–150 Hz]). Since then, intracranial EEG research on human cognition has focused primarily on high-gamma activity to reveal the large-scale cortical dynamics of most major cognitive functions, not only offline in well-controlled paradigms, but also online, while patients freely interact with their environment. This chapter introduces that approach, including its recent extension to task-induced neural activity suppressions and functional connectivity mapping, and its clinical application to minimize cognitive deficits induced by epilepsy surgery.

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Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome

Brain Communications ◽

10.1093/braincomms/fcab042 ◽

2021 ◽

Author(s):

Naoto Kuroda ◽

Masaki Sonoda ◽

Makoto Miyakoshi ◽

Hiroki Nariai ◽

Jeong-Won Jeong ◽

...

Keyword(s):

Standard Model ◽

High Frequency ◽

Classification Accuracy ◽

Modulation Index ◽

High Frequency Oscillation ◽

Frequency Oscillation ◽

Model Accuracy ◽

Intracranial Eeg ◽

Postoperative Seizure ◽

Phase Amplitude

Abstract Researchers have looked for rapidly- and objectively-measurable electrophysiology biomarkers that accurately localize the epileptogenic zone. Promising candidates include interictal high-frequency oscillation and phase-amplitude coupling. Investigators have independently created the toolboxes that compute the high-frequency oscillation rate and the severity of phase-amplitude coupling. This study of 135 patients determined what toolboxes and analytic approaches would optimally classify patients achieving postoperative seizure control. Four different detector toolboxes computed the rate of high-frequency oscillation at ≥ 80 Hz at intracranial EEG channels. Another toolbox calculated the modulation index reflecting the strength of phase-amplitude coupling between high-frequency oscillation and slow-wave at 3-4 Hz. We defined the completeness of resection of interictally-abnormal regions as the subtraction of high-frequency oscillation rate (or modulation index) averaged across all preserved sites from that averaged across all resected sites. We computed the outcome classification accuracy of the logistic regression-based standard model considering clinical, ictal intracranial EEG, and neuroimaging variables alone. We then determined how well the incorporation of high-frequency oscillation/modulation index would improve the standard model mentioned above. To assess the anatomical variability across nonepileptic sites, we generated the normative atlas of detector-specific high-frequency oscillation and modulation index. Each atlas allowed us to compute the statistical deviation of high-frequency oscillation/modulation index from the nonepileptic mean. We determined whether the model accuracy would be improved by incorporating absolute or normalized high-frequency oscillation/modulation index as a biomarker assessing interictally-abnormal regions. We finally determined whether the model accuracy would be improved by selectively incorporating high-frequency oscillation verified to have high-frequency oscillatory components unattributable to a high-pass filtering effect. Ninety-five patients achieved successful seizure control, defined as International League Against Epilepsy class 1 outcome. Multivariate logistic regression analysis demonstrated that complete resection of interictally-abnormal regions additively increased the chance of success. The model accuracy was further improved by incorporating z-score normalized high-frequency oscillation/modulation index or selective incorporation of verified high-frequency oscillation. The standard model had a classification accuracy of 0.75. Incorporation of normalized high-frequency oscillation/modulation index or verified high-frequency oscillation improved the classification accuracy up to 0.82. These outcome prediction models survived the cross-validation process and demonstrated an agreement between the model-based likelihood of success and the observed success on an individual basis. Interictal high-frequency oscillation and modulation index had a comparably additive utility in epilepsy presurgical evaluation. Our empirical data support the theoretical notion that the prediction of postoperative seizure outcomes can be optimized with the consideration of both interictal and ictal abnormalities.

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