scholarly journals High-frequency bioelectrical activity of the brain in the diagnosis of epilepsy

2018 ◽  
Vol 10 (3) ◽  
pp. 6-13 ◽  
Author(s):  
N. D. Sorokina ◽  
S. S. Pertsov ◽  
G. V. Selitsky
Keyword(s):  
High Frequency ◽  
Epileptiform Activity ◽  
Gamma Activity ◽  
Epileptic Focus ◽  
Bioelectrical Activity ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Gamma Rhythm ◽  
High Frequency Activity ◽  
The Brain

Recent studies show that the brain gamma activity includes both the gamma rhythm (standard EEG) and high frequency (100-1000 Hz) as well as super-high (>1000 Hz) frequency oscillations, as recorded by electrocorticography. As reported in the literature, the high-frequency oscillations (80-500 Hz) are highly informative markers of an epileptic focus. In this review, we analyze features of high-frequency activity associated with the epileptiform activity, and its relation to the seizure onset range. Further study of high-frequency bioelectric activity of the brain is of interest to researchers and clinicians, and may improve the EEG differential diagnosis of epilepsy.

scholarly journals High-frequency oscillations and gamma activity in the diagnosis of epilepsy

2019 ◽  
Vol 2 (12) ◽  
pp. 9-12
Author(s):  
G. V. Selitsky ◽  
S. S. Pertsov ◽  
N. D. Sorokina
Keyword(s):  
Differential Diagnosis ◽  
High Frequency ◽  
Gamma Activity ◽  
Epileptic Focus ◽  
Bioelectric Activity ◽  
High Frequency Oscillations ◽  
Gamma Rhythm ◽  
The Brain

Modern studies of gamma rhythm indicate that gamma activity (30–80 Hz in standard EEG), and high-frequency (80–1000 Hz) and ultra-frequency oscillations (more than 1000 Hz), recorded by ECOG, are highly informative markers of epileptic focus. Further study of high-frequency bioelectric activity of the brain is of interest to researchers and clinicians in order to improve the electroencephalographic differential diagnosis in epilepsy.

scholarly journals Interictal high frequency background activity as a biomarker of epileptogenic tissue

2021 ◽  
Author(s):  
Truman Stovall ◽  
Brian Hunt ◽  
Simon Glynn ◽  
William C Stacey ◽  
Stephen V Gliske
Keyword(s):  
Logistic Regression ◽  
Confidence Interval ◽  
High Frequency ◽  
High Frequency Oscillation ◽  
Epileptogenic Zone ◽  
Odds Ratios ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
Background Data

Abstract High Frequency Oscillations are very brief events that are a well-established biomarker of the epileptogenic zone, but are rare and comprise only a tiny fraction of the total recorded EEG. We hypothesize that the interictal high frequency “background” data, which has received little attention but represents the majority of the EEG record, also may contain additional, novel information for identifying the epileptogenic zone. We analyzed intracranial EEG (30–500 Hz frequency range) acquired from 24 patients who underwent resective surgery. We computed 38 quantitative features based on all usable, interictal data (63–307 hours per subject), excluding all detected high frequency oscillations. We assessed association between each feature and the seizure onset zone and resected volume using logistic regression. A pathology score per channel was also created via principle component analysis and logistic regression, using hold-out-one-patient cross validation to avoid in-sample training. Association of the pathology score with the seizure onset zone and resected volume was quantified using an asymmetry measure. Many features were associated with the seizure onset zone: 23/38 features had odds ratios >1.3 or < 0.7 and 17/38 had odds ratios different than zero with high significance (p < 0.001/39, logistic regression with Bonferroni Correction). The pathology score, the rate of high frequency oscillations, and their channel-wise product were each strongly associated with the seizure onset zone (median asymmetry > =0.44, good surgery outcome patients; median asymmetry > =0.40, patients with other outcomes; 95% confidence interval > 0.27 in both cases). The pathology score and the channel-wise product also had higher asymmetry with respect to the seizure onset zone than the high frequency oscillation rate alone (median difference in asymmetry > =0.18, 95% confidence interval >0.05). These results support that the high frequency background data contains useful information for determining the epileptogenic zone, distinct and complementary to information from detected high frequency oscillations. The concordance between the high frequency activity pathology score and the rate of high frequency oscillations appears to be a better biomarker of epileptic tissue than either measure alone.

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.

Cognitive refractory state caused by spontaneous epileptic high-frequency oscillations in the human brain

2019 ◽  
Vol 11 (514) ◽  
pp. eaax7830 ◽  
Author(s):  
Su Liu ◽  
Josef Parvizi
Keyword(s):  
Cognitive Deficits ◽  
High Frequency ◽  
Medial Temporal Lobe ◽  
Memory Performance ◽  
Physiological Responses ◽  
Epileptic Activity ◽  
Epileptic Focus ◽  
Surgical Removal ◽  
High Frequency Oscillations ◽  
Refractory State

Epileptic brain tissue is often considered physiologically dysfunctional, and the optimal treatment of many patients with uncontrollable seizures involves surgical removal of the epileptic tissue. However, it is unclear to what extent the epileptic tissue is capable of generating physiological responses to cognitive stimuli and how cognitive deficits ensuing surgical resections can be determined using state-of-the-art computational methods. To address these unknowns, we recruited six patients with nonlesional epilepsies and identified the epileptic focus in each patient with intracranial electrophysiological monitoring. We measured spontaneous epileptic activity in the form of high-frequency oscillations (HFOs), recorded stimulus-locked physiological responses in the form of physiological high-frequency broadband activity, and explored the interaction of the two as well as their behavioral correlates. Across all patients, we found abundant normal physiological responses to relevant cognitive stimuli in the epileptic sites. However, these physiological responses were more likely to be “seized” (delayed or missed) when spontaneous HFOs occurred about 850 to 1050 ms before, until about 150 to 250 ms after, the onset of relevant cognitive stimuli. Furthermore, spontaneous HFOs in medial temporal lobe affected the subjects’ memory performance. Our findings suggest that nonlesional epileptic sites are capable of generating normal physiological responses and highlight a compelling mechanism for cognitive deficits in these patients. The results also offer clinicians a quantitative tool to differentiate pathological and physiological high-frequency activities in epileptic sites and to indirectly assess their possible cognitive reserve function and approximate the risk of resective surgery.

scholarly journals DC shifts, high frequency oscillations, ripples and fast ripples in relation to the seizure onset zone

Seizure ◽  
2020 ◽  
Vol 77 ◽  
pp. 52-58 ◽  
Author(s):  
Somin Lee ◽  
Naoum P. Issa ◽  
Sandra Rose ◽  
James X. Tao ◽  
Peter C. Warnke ◽  
...  
Keyword(s):  
High Frequency ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone

Neuronal Aggregate Formation Underlies Spatiotemporal Dynamics of Nonsynaptic Seizure Initiation

2003 ◽  
Vol 89 (4) ◽  
pp. 2330-2333 ◽  
Author(s):  
Marom Bikson ◽  
John E. Fox ◽  
John G. R. Jefferys
Keyword(s):  
Synaptic Transmission ◽  
Gap Junctions ◽  
High Frequency ◽  
Formation Rate ◽  
Simultaneous Recording ◽  
Aggregate Formation ◽  
Seizure Onset ◽  
Electrographic Seizures ◽  
High Frequency Activity

High-frequency activity often precedes seizure onset. We found that electrographic seizures, induced in vitro using the low-Ca2+ model, start with high-frequency (>150 Hz) activity that then decreases in frequency while increasing in amplitude. Multichannel and unit recordings showed that the mechanism of this transition was the progressive formation of larger neuronal aggregates. Thus the apparenthigh-frequency activity, at seizure onset, can reflect the simultaneous recording of several slower firing aggregates. Aggregate formation rate can be accelerated by reducing osmolarity. Because synaptic transmission is blocked when extracellular Ca2+ is reduced, nonsynaptic mechanisms (gap junctions, field effects) must be sufficient for aggregate formation and recruitment.

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

Neurophysiological evaluation of canine congenital hydrocephalus in three dogs

2020 ◽  
Vol 8 (1) ◽  
pp. e000949
Author(s):  
Mary Gutiérrez ◽  
Gimena Feijóo ◽  
Luis J Delucchi
Keyword(s):  
Auditory Evoked Potentials ◽  
Epileptiform Activity ◽  
Clinical Signs ◽  
Congenital Hydrocephalus ◽  
Bioelectrical Activity ◽  
Functional Changes ◽  
Sharp Waves ◽  
Clinical Responses ◽  
The Brain ◽  
Cerebrospinal Fluid Volume

Congenital hydrocephalus is a neurological disorder frequently observed in dogs. It is characterised by an increase of cerebrospinal fluid volume in the ventricular system that can cause atrophy of brain tissue. It can be provoked by diverse causes, as it can be idiopathic or secondary to nervous system abnormalities. Diagnosis is based on clinical signs and imaging studies, but neurophysiological techniques can provide valuable information. This report describes functional changes in the brain and brainstem evaluated by electroencephalogram and auditory evoked potentials. In the three cases, the authors found alterations in background rhythm, slow waves, epileptiform activity, hypsarrhythmic tracing and positive sharp waves. These techniques allow detecting alterations in the brain bioelectrical activity that do not trigger clear clinical responses.

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