scholarly journals High frequency oscillations in epileptic and non-epileptic human hippocampus during a cognitive task

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Martin Pail ◽  
Jan Cimbálník ◽  
Robert Roman ◽  
Pavel Daniel ◽  
Daniel J. Shaw ◽  
...  
Keyword(s):  
High Frequency ◽  
Focal Epilepsy ◽  
Cognitive Task ◽  
Normal Brain ◽  
High Frequency Oscillations ◽  
Depth Electrodes ◽  
Hippocampal Activity ◽  
Normal Brain Function ◽  
The Impact ◽  
Resting Period

Abstract Hippocampal high-frequency electrographic activity (HFOs) represents one of the major discoveries not only in epilepsy research but also in cognitive science over the past few decades. A fundamental challenge, however, has been the fact that physiological HFOs associated with normal brain function overlap in frequency with pathological HFOs. We investigated the impact of a cognitive task on HFOs with the aim of improving differentiation between epileptic and non-epileptic hippocampi in humans. Hippocampal activity was recorded with depth electrodes in 15 patients with focal epilepsy during a resting period and subsequently during a cognitive task. HFOs in ripple and fast ripple frequency ranges were evaluated in both conditions, and their rate, spectral entropy, relative amplitude and duration were compared in epileptic and non-epileptic hippocampi. The similarity of HFOs properties recorded at rest in epileptic and non-epileptic hippocampi suggests that they cannot be used alone to distinguish between hippocampi. However, both ripples and fast ripples were observed with higher rates, higher relative amplitudes and longer durations at rest as well as during a cognitive task in epileptic compared with non-epileptic hippocampi. Moreover, during a cognitive task, significant reductions of HFOs rates were found in epileptic hippocampi. These reductions were not observed in non-epileptic hippocampi. Our results indicate that although both hippocampi generate HFOs with similar features that probably reflect non-pathological phenomena, it is possible to differentiate between epileptic and non-epileptic hippocampi using a simple odd-ball task.

scholarly journals Interictal high frequency oscillations (HFOs) in patients with focal epilepsy and normal MRI

2012 ◽  
Vol 123 (1) ◽  
pp. 100-105 ◽  
Author(s):  
Luciana Andrade-Valença ◽  
Francesco Mari ◽  
Julia Jacobs ◽  
Maeike Zijlmans ◽  
André Olivier ◽  
...  
Keyword(s):  
High Frequency ◽  
Focal Epilepsy ◽  
High Frequency Oscillations

scholarly journals Patient-specific optimization of automated detection improves seizure onset zone localization based on high frequency oscillations

2020 ◽  
Author(s):  
Casey L. Trevino ◽  
Jack J. Lin ◽  
Indranil Sen-Gupta ◽  
Beth A. Lopour
Keyword(s):  
High Frequency ◽  
Classification Accuracy ◽  
Patient Specific ◽  
Seizure Onset ◽  
Data Set ◽  
Localization Accuracy ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
Wide Range ◽  
The Impact

AbstractHigh frequency oscillations (HFOs) are a promising biomarker of epileptogenicity, and automated algorithms are critical tools for their detection. However, previously validated algorithms often exhibit decreased HFO detection accuracy when applied to a new data set, if the parameters are not optimized. This likely contributes to decreased seizure localization accuracy, but this has never been tested. Therefore, we evaluated the impact of parameter selection on seizure onset zone (SOZ) localization using automatically detected HFOs. We detected HFOs in intracranial EEG from twenty medically refractory epilepsy patients with seizure free surgical outcomes using an automated algorithm. For each patient, we assessed classification accuracy of channels inside/outside the SOZ using a wide range of detection parameters and identified the parameters associated with maximum classification accuracy. We found that only three out of twenty patients achieved maximal localization accuracy using conventional HFO detection parameters, and optimal parameter ranges varied significantly across patients. The parameters for amplitude threshold and root-mean-square window had the greatest impact on SOZ localization accuracy; minimum event duration and rejection of false positive events did not significantly affect the results. Using individualized optimal parameters led to substantial improvements in localization accuracy, particularly in reducing false positives from non-SOZ channels. We conclude that optimal HFO detection parameters are patient-specific, often differ from conventional parameters, and have a significant impact on SOZ localization. This suggests that individual variability should be considered when implementing automatic HFO detection as a tool for surgical planning.

Evoked versus spontaneous high frequency oscillations in the chronic electrocorticogram in focal epilepsy

2017 ◽  
Vol 128 (5) ◽  
pp. 858-866 ◽  
Author(s):  
M.A. van 't Klooster ◽  
N.E.C. van Klink ◽  
D. van Blooijs ◽  
C.H. Ferrier ◽  
K.P.J. Braun ◽  
...  
Keyword(s):  
High Frequency ◽  
Focal Epilepsy ◽  
High Frequency Oscillations

scholarly journals High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ece Boran ◽  
Johannes Sarnthein ◽  
Niklaus Krayenbühl ◽  
Georgia Ramantani ◽  
Tommaso Fedele
Keyword(s):  
Seizure Frequency ◽  
Epilepsy Surgery ◽  
High Frequency ◽  
Focal Epilepsy ◽  
Low Noise ◽  
Drug Resistant ◽  
Scalp Eeg ◽  
High Frequency Oscillations ◽  
Custom Made ◽  
Active Epilepsy

Abstract High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R2 = 0.80, p < 0.001). Overall, scalp HFO rates were higher in patients with active epilepsy (19 recordings, p = 0.0066, PPV = 86%, NPV = 80%, accuracy = 84% CI [62% 94%]) and decreased following successful epilepsy surgery. The location of the highest HFO rates in scalp EEG matched the location of the highest HFO rates in ECoG. This study is the first step towards using non-invasively recorded scalp HFO to monitor disease severity in patients affected by epilepsy.

scholarly journals Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy

Brain ◽  
2018 ◽  
Vol 141 (3) ◽  
pp. 713-730 ◽  
Author(s):  
Su Liu ◽  
Candan Gurses ◽  
Zhiyi Sha ◽  
Michael M Quach ◽  
Altay Sencer ◽  
...  
Keyword(s):  
High Frequency ◽  
Focal Epilepsy ◽  
Machine Learning Techniques ◽  
Seizure Onset ◽  
Functional Regions ◽  
Clinical Biomarkers ◽  
High Frequency Oscillations ◽  
Potential Association ◽  
Waveform Similarity ◽  
Seizure Onset Zones

Abstract High-frequency oscillations in local field potentials recorded with intracranial EEG are putative biomarkers of seizure onset zones in epileptic brain. However, localized 80–500 Hz oscillations can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones, which limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 epilepsy patients and five control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified by using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent from the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step towards the translation of these signature events into valid clinical biomarkers. 5721572971001 awx374media1 5721572971001

scholarly journals Spike-related haemodynamic responses overlap with high frequency oscillations in patients with focal epilepsy

Brain ◽  
2018 ◽  
Vol 141 (3) ◽  
pp. 731-743 ◽  
Author(s):  
Karina A González Otárula ◽  
Hui Ming Khoo ◽  
Nicolás von Ellenrieder ◽  
Jeffery A Hall ◽  
François Dubeau ◽  
...  
Keyword(s):  
High Frequency ◽  
Focal Epilepsy ◽  
High Frequency Oscillations

scholarly journals Emergence of Narrowband High Frequency Oscillations from Asynchronous, Uncoupled Neural Firing

2016 ◽  
Vol 27 (01) ◽  
pp. 1650049 ◽  
Author(s):  
Stephen V. Gliske ◽  
William C. Stacey ◽  
Eugene Lim ◽  
Katherine A. Holman ◽  
Christian G. Fink
Keyword(s):  
High Frequency ◽  
Feedback Inhibition ◽  
Experimental Studies ◽  
Field Potential ◽  
Epileptic Seizures ◽  
Spike Timing ◽  
Normal Brain ◽  
Neural Firing ◽  
Similar Frequency ◽  
High Frequency Oscillations

Previous experimental studies have demonstrated the emergence of narrowband local field potential oscillations during epileptic seizures in which the underlying neural activity appears to be completely asynchronous. We derive a mathematical model explaining how this counterintuitive phenomenon may occur, showing that a population of independent, completely asynchronous neurons may produce narrowband oscillations if each neuron fires quasi-periodically, without requiring any intrinsic oscillatory cells or feedback inhibition. This quasi-periodicity can occur through cells with similar frequency–current ([Formula: see text]–[Formula: see text]) curves receiving a similar, high amount of uncorrelated synaptic noise. Thus, this source of oscillatory behavior is distinct from the usual cases (pacemaker cells entraining a network, or oscillations being an inherent property of the network structure), as it requires no oscillatory drive nor any specific network or cellular properties other than cells that repetitively fire with continual stimulus. We also deduce bounds on the degree of variability in neural spike-timing which will permit the emergence of such oscillations, both for action potential- and postsynaptic potential-dominated LFPs. These results suggest that even an uncoupled network may generate collective rhythms, implying that the breakdown of inhibition and high synaptic input often observed during epileptic seizures may generate narrowband oscillations. We propose that this mechanism may explain why so many disparate epileptic and normal brain mechanisms can produce similar high frequency oscillations.

scholarly journals Detection of High-Frequency Oscillations by Hybrid Depth Electrodes in Standard Clinical Intracranial EEG Recordings

2014 ◽  
Vol 5 ◽  
Author(s):  
Efstathios D. Kondylis ◽  
Thomas A. Wozny ◽  
Witold J. Lipski ◽  
Alexandra Popescu ◽  
Vincent J. DeStefino ◽  
...  
Keyword(s):  
High Frequency ◽  
Intracranial Eeg ◽  
High Frequency Oscillations ◽  
Depth Electrodes ◽  
Eeg Recordings

scholarly journals Ictal Occurrence of High-Frequency Oscillations Correlates With Seizure Severity in a Rat Model of Temporal Lobe Epilepsy

2021 ◽  
Vol 15 ◽  
Author(s):  
Nadja Birk ◽  
Jan Schönberger ◽  
Karin Helene Somerlik-Fuchs ◽  
Andreas Schulze-Bonhage ◽  
Julia Jacobs
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Rat Model ◽  
High Frequency ◽  
Focal Epilepsy ◽  
Epileptic Activity ◽  
Seizure Severity ◽  
Chronic Epilepsy ◽  
High Frequency Oscillations

High-frequency oscillations (HFOs, ripples 80–250 Hz, fast ripples 250–500 Hz) are biomarkers of epileptic tissue. They are most commonly observed over areas generating seizures and increase in occurrence during the ictal compared to the interictal period. It has been hypothesized that their rate correlates with the severity of epilepsy and seizure in affected individuals. In the present study, it was aimed to investigate whether the HFO count mirrors the observed behavioral seizure severity using a kainate rat model for temporal lobe epilepsy. Seizures were selected during the chronic epilepsy phase of this model and classified by behavioral severity according to the Racine scale. Seizures with Racine scale 5&amp;6 were considered generalized and severe. HFOs were marked in 24 seizures during a preictal, ictal, and postictal EEG segment. The duration covered by the HFO during these different segments was analyzed and compared between mild and severe seizures. HFOs were significantly increased during ictal periods (p &lt; 0.001) and significantly decreased during postictal periods (p &lt; 0.03) compared to the ictal segment. Ictal ripples (p = 0.04) as well as fast ripples (p = 0.02) were significantly higher in severe seizures compared to mild seizures. The present study demonstrates that ictal HFO occurrence mirrors seizure severity in a chronic focal epilepsy model in rats. This is similar to recent observations in patients with refractory mesio-temporal lobe epilepsy. Moreover, postictal HFO decrease might reflect postictal inhibition of epileptic activity. Overall results provide additional evidence that HFOs can be used as biomarkers for measuring seizure severity in epilepsy.

scholarly journals Blinded study: prospectively defined high frequency oscillations predict seizure outcome in individual patients

2020 ◽  
Author(s):  
V Dimakopoulos ◽  
P Mégevand ◽  
E Boran ◽  
S Momjian ◽  
M Seeck ◽  
...  
Keyword(s):  
Epilepsy Surgery ◽  
High Frequency ◽  
Dwell Time ◽  
Nrem Sleep ◽  
Seizure Outcome ◽  
Seizure Freedom ◽  
University Hospitals ◽  
High Frequency Oscillations ◽  
Depth Electrodes

AbstractBackgroundInterictal high frequency oscillations (HFO) are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant HFO in intracranial EEG (iEEG) from MNI Montreal and tested it in iEEG from Zurich. We here validate the algorithm on iEEG recorded in an independent epilepsy center so that HFO analysis was blinded to seizure outcome.MethodsWe selected consecutive patients from Geneva University Hospitals who underwent resective epilepsy surgery with postsurgical follow-up > 12 months. We analyzed long-term iEEG recordings during non-rapid eye movement (NREM) sleep that we segmented into intervals of 5 min. HFOs were defined in the ripple (80-250 Hz) and the fast ripple (FR, 250-500 Hz) frequency band. Contacts with the highest rate of ripples co-occurring with FR (FRandR) designated the HFO area. If the HFO area was not fully resected and the patient suffered from recurrent seizures (ILAE 2-6), this was classified as a true positive (TP) prediction.ResultsWe included iEEG recordings from 16 patients (median age 32 y, range [18-53]) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 mo, range [12-55]). The HFO area had high test-retest reliability across intervals (median dwell time 95%). We excluded two patients with dwell time < 50% from further analysis.The HFO area was fully included in the resected volume in 2/4 patients who achieved postoperative seizure freedom (ILAE 1, specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (ILAE > 1, sensitivity 90%), leading to an accuracy of 79%.ConclusionsWe validated the automated procedure to delineate the clinical relevant HFO area in individual patients of an independently recorded dataset and achieved the same good accuracy as in our previous studies.SignificanceThe reproducibility of our results across datasets is promising for a multicienter study testing the clinical application of HFO detection to guide epilepsy surgery.

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