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

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

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

Neurology ◽  
2018 ◽  
Vol 90 (8) ◽  
pp. e639-e646 ◽  
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.

scholarly journals Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome

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.

High-Frequency Oscillation and Chronic Lung Disease in Very Low Birth Weight Infants

PEDIATRICS ◽  
2001 ◽  
Vol 108 (1) ◽  
pp. 212-214
Author(s):  
J. P. Shenai; ◽  
P. Rimensberger; ◽  
U. Thome ◽  
F. Pohlandt; ◽  
P. Rimensberger
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Lung Disease ◽  
Chronic Lung Disease ◽  
High Frequency ◽  
Very Low Birth Weight ◽  
High Frequency Oscillation ◽  
Frequency Oscillation ◽  
Low Birth Weight Infants

scholarly journals A Comprehensive Investigation on High-Frequency Oscillation in DC Microgrid

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohammad Habibullah ◽  
Nadarajah Mithulananthan ◽  
Krischonme Bhumkittipich ◽  
Mohammad Amin
Keyword(s):  
High Frequency ◽  
High Frequency Oscillation ◽  
Frequency Oscillation ◽  
Comprehensive Investigation ◽  
Dc Microgrid

scholarly journals Distinct EEG seizure patterns reflect different seizure generation mechanisms

2015 ◽  
Vol 113 (7) ◽  
pp. 2840-2844 ◽  
Author(s):  
Pariya Salami ◽  
Maxime Lévesque ◽  
Jean Gotman ◽  
Massimo Avoli
Keyword(s):  
High Frequency ◽  
Channel Blocker ◽  
Seizure Activity ◽  
Low Voltage ◽  
High Frequency Oscillation ◽  
Seizure Onset ◽  
Frequency Oscillation ◽  
Oscillation Analysis ◽  
Pilocarpine Model ◽  
Generation Mechanisms

Low-voltage fast (LVF)- and hypersynchronous (HYP)-seizure onset patterns can be recognized in the EEG of epileptic animals and patients with temporal lobe epilepsy. Ripples (80–200 Hz) and fast ripples (250–500 Hz) have been linked to each pattern, with ripples predominating during LVF seizures and fast ripples predominating during HYP seizures in the rat pilocarpine model. This evidence led us to hypothesize that these two seizure-onset patterns reflect the contribution of neural networks with distinct transmitter signaling characteristics. Here, we tested this hypothesis by analyzing the seizure activity induced with the K+ channel blocker 4-aminopyridine (4AP, 4–5 mg/kg ip), which enhances both glutamatergic and GABAergic transmission, or the GABAA receptor antagonist picrotoxin (3–5 mg/kg ip); rats were implanted with electrodes in the hippocampus, the entorhinal cortex, and the subiculum. We found that LVF onset occurred in 82% of 4AP-induced seizures whereas seizures after picrotoxin were always HYP. In addition, high-frequency oscillation analysis revealed that 4AP-induced LVF seizures were associated with higher ripple rates compared with fast ripples ( P < 0.05), whereas picrotoxin-induced seizures contained higher rates of fast ripples compared with ripples ( P < 0.05). These results support the hypothesis that two distinct patterns of seizure onset result from different pathophysiological mechanisms.

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