Fast Ripple Research Articles

Changes in immunoreactivity to cellular markers associated with connexins in the hippocampus of epileptic rats with fast ripple activity

Changes in immunoreactivity to cellular markers associated with connexins in the hippocampus of epileptic rats with fast ripple activity

Changes in glutamate receptors immunoreactivity in the hippocampus of epileptic rats with fast ripple activity

Changes in glutamate receptors immunoreactivity in the hippocampus of epileptic rats with fast ripple activity

Causal relationship of CA3 back projection to dentate gyrus and its role in the CA1 fast ripple generation

Causal relationship of CA3 back projection to dentate gyrus and its role in the CA1 fast ripple generation

Spectral organization of focal seizures within the thalamotemporal network.

ObjectiveTo investigate dynamic changes in neural activity between the anterior nucleus of the thalamus (ANT) and the seizure onset zone (SOZ) in patients with drug‐resistant temporal lobe epilepsy (TLE) based on anatomic location, seizure subtype, and state of vigilance (SOV).MethodsEleven patients undergoing stereoelectroencephalography for seizure localization were recruited prospectively for local field potential (LFP) recording directly from the ANT. The SOZ was identified using line length and epileptogenicity index. Changes in power spectral density (PSD) were compared between the two anatomic sites as seizures (N = 53) transitioned from interictal baseline to the posttermination stage.ResultsAt baseline, the thalamic LFPs were significantly lower and distinct from the SOZ with the presence of higher power in the fast ripple band (P < 0.001). Temporal changes in ictal power of neural activity within ANT mimic those of the SOZ, are increased significantly at seizure onset (P < 0.05), and are distinct for seizures that impaired awareness or that secondarily generalized (P < 0.05). The onset of seizure was preceded by a decrease in the mean power spectral density (PSD) in ANT and SOZ (P < 0.05). Neural activity correlated with different states of vigilance at seizure onset within the ANT but not in the SOZ (P = 0.005).InterpretationThe ANT can be recruited at the onset of mesial temporal lobe seizures, and the recruitment pattern differs with seizure subtypes. Furthermore, changes in neural dynamics precede seizure onset and are widespread to involve temporo‐thalamic regions, thereby providing an opportunity to intervene early with closed‐loop DBS.

Increased gamma and decreased fast ripple connections of epileptic tissue: A high-frequency directed network approach.

ObjectiveNew insights into high‐frequency electroencephalographic activity and network analysis provide potential tools to improve delineation of epileptic tissue and increase the chance of postoperative seizure freedom. Based on our observation of high‐frequency oscillations “spreading outward” from the epileptic source, we hypothesize that measures of directed connectivity in the high‐frequency range distinguish epileptic from healthy brain tissue.MethodsWe retrospectively selected refractory epilepsy patients with a malformation of cortical development or tumor World Health Organization grade I/II who underwent epilepsy surgery with intraoperative electrocorticography for tailoring the resection based on spikes. We assessed directed functional connectivity in the theta (4‐8 Hz), gamma (30‐80 Hz), ripple (80‐250 Hz), and fast ripple (FR; 250‐500 Hz) bands using the short‐time direct directed transfer function, and calculated the total, incoming, and outgoing propagation strength for each electrode. We compared network measures of electrodes covering the resected and nonresected areas separately for patients with good and poor outcome, and of electrodes with and without spikes, ripples, and FRs (group level: paired t test; patient level: Mann‐Whitney U test). We selected the measure that could best identify the resected area and channels with epileptic events using the area under the receiver operating characteristic curve, and calculated the positive and negative predictive value, sensitivity, and specificity.ResultsWe found higher total and outstrength in the ripple and gamma bands in resected tissue in patients with good outcome (rippletotal: P = .01; rippleout: P = .04; gammatotal: P = .01; gammaout: P = .01). Channels with events showed lower total and instrength, and higher outstrength in the FR band, and higher total and outstrength in the ripple, gamma, and theta bands (FRtotal: P = .05; FRin: P < .01; FRout: P = .02; gammatotal: P < .01; gammain: P = .01; gammaout: P < .01; thetatotal: P = .01; thetaout: P = .01). The total strength in the gamma band was most distinctive at the channel level (positive predictive value [PPV]good = 74%, PPVpoor = 43%).SignificanceInterictally, epileptic tissue is isolated in the FR band and acts as a driver up to the (fast) ripple frequency range. The gamma band total strength seems promising to delineate epileptic tissue intraoperatively.

Fast ripple analysis in human mesial temporal lobe epilepsy suggests two different seizure-generating mechanisms.

The distinction of hypersynchronous (HYP) and low-voltage fast (LVF) onset seizures in mesial temporal lobe epilepsy (MTLE) is well established, but classifying individual seizures and patients is often challenging. Experimental work indicates a strong association of HYP with fast ripples (250-500 Hz) and of LVF with ripples (80-250 Hz). We aimed to investigate whether analysis of high-frequency oscillations can be useful for characterizing the process of seizure generation in human MTLE patients. We retrospectively compared 19 HYP and 14 LVF onset clinical seizures from ten and six consecutive MTLE patients with a predominance of the respective pattern. Five-second intervals of stereotactic EEGs from the seizure onset zone were selected, each representing the onset of HYP and LVF, the corresponding pre-ictal periods and, after the large spikes of HYP onsets, the LVF-like pattern that frequently followed. Pre-ictal fast ripple density and rate were higher for HYP than for LVF seizures (p < .05). This association was also found for initial ictal segments (p < .001). Furthermore, fast ripple density and rate were higher during the LVF-like pattern after HYP spikes than during LVF without preceding HYP (p < .01). Ripple density and rate in contrast did not differ significantly (p > .05). Fast ripple (p < .01) and ripple (p < .001) amplitude was higher during the LVF-like pattern after HYP spikes when compared to LVF without preceding HYP. Our findings indicate a clear connection between experimental findings and human epilepsy. The association of fast ripples with HYP suggests that out-of-phase firing of different pyramidal cell clusters contributes specifically to generation of these seizures, rather than to LVF onsets. Both during and immediately before seizures, fast ripple analysis may facilitate classification.

High-Frequency Oscillation Networks and Surgical Outcome in Adult Focal Epilepsy.

To investigate whether high-frequency oscillations (HFOs) show spatiotemporal propagation and assess the relevance of the earliest oscillations in relation to the seizure onset zone (SOZ) and postsurgical outcome. We retrospectively investigated the intracerebral electroencephalography (EEG) of patients who became seizure free after subsequent surgery. We marked HFOs during 1 hour of recordings. We calculated the time delay between pairs of channels as the median delay between their HFOs and constructed a time line of the delay of each channel with respect to the earliest channel (first source channel). A network was defined when a temporal order could be established among the channels based on the existence of statistically significant delays. Fifteen patients with good surgical outcome were included. We found ripple networks in all patients, and fast ripple networks in 9. For ripples, first source channels were found in a higher proportion in the SOZ than the rest of the network channels (15 of 27 [56%] versus 93 of 262 [35%]; p = 0.04). For both ripples and fast ripples, first source channels were resected more often that the rest of the network channels (ripples: 13 of 27 [48%] versus 65 of 262 [25%]; p = 0.01; fast ripples: 8 of 9 [89%] versus 17 of 40 [43%]; p = 0.002); channels with the highest rates of ripples and fast ripples were resected in a similar proportion. These results demonstrate that interictal HFOs are organized in networks and indicate a possible need for the resection of first source channels. However, resecting them is not superior to resecting channels with highest rates of HFOs. Ann Neurol 2019;85:485-494.

CRF Mediates Stress-Induced Pathophysiological High-Frequency Oscillations in Traumatic Brain Injury.

It is not known why there is increased risk to have seizures with increased anxiety and stress after traumatic brain injury (TBI). Stressors cause the release of corticotropin-releasing factor (CRF) both from the hypothalamic pituitary adrenal (HPA) axis and from CNS neurons located in the central amygdala and GABAergic interneurons. We have previously shown that CRF signaling is plastic, becoming excitatory instead of inhibitory after the kindling model of epilepsy. Here, using Sprague Dawley rats we have found that CRF signaling increased excitability after TBI. Following TBI, CRF type 1 receptor (CRFR1)-mediated activity caused abnormally large electrical responses in the amygdala, including fast ripples, which are considered to be epileptogenic. After TBI, we also found the ripple (120–250 Hz) and fast ripple activity (>250 Hz) was cross-frequency coupled with θ (3–8 Hz) oscillations. CRFR1 antagonists reduced the incidence of phase coupling between ripples and fast ripples. Our observations indicate that pathophysiological signaling of the CRFR1 increases the incidence of epileptiform activity after TBI. The use for CRFR1 antagonist may be useful to reduce the severity and frequency of TBI associated epileptic seizures.

Epileptic High-frequency Oscillations in Scalp Electroencephalography.

Electroencephalography (EEG) examines the functional state of the brain. High-frequency oscillations (HFOs) in the ripple (80-200/250 Hz) and fast ripple (200/250-500/600 Hz) bands have recently been attracting attention, and their recording has been enabled by advancements in digital EEG techniques. The detection of HFOs was previously limited to intracranial EEG, but fast oscillations (FOs) in the gamma (40-80 Hz) and ripple bands can now be detected over the scalp. HFOs and FOs have been shown to be related to epileptogenicity in intracranial EEG and scalp EEG, respectively. A large number of FOs are found in the scalp EEGs of pediatric patients with various epileptic encephalopathies, particularly West syndrome. FOs are suggested to be a biomarker of the epileptogenic cortical region in epilepsy surgery. FOs are detectable even in patients with idiopathic focal epilepsies, including benign epilepsy with centrotemporal spikes and Panayiotopoulos syndrome, who are not generally candidates for operation. The detection of HFOs and FOs may provide clues to the pathophysiology of epilepsy and the relationship between HFOs and cognitive dysfunction.

Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography

ObjectiveTo test the utility of a novel semi-automated method for detecting, validating, and quantifying high-frequency oscillations (HFOs): ripples (80–200 Hz) and fast ripples (200–600 Hz) in intra-operative electrocorticography (ECoG) recordings. MethodsSixteen adult patients with temporal lobe epilepsy (TLE) had intra-operative ECoG recordings at the time of resection. The computer-annotated ECoG recordings were visually inspected and false positive detections were removed. We retrospectively determined the sensitivity, specificity, positive and negative predictive value (PPV/NPV) of HFO detections in unresected regions for determining post-operative seizure outcome. ResultsVisual validation revealed that 2.81% of ripple and 43.68% of fast ripple detections were false positive. Inter-reader agreement for false positive fast ripple on spike classification was good (ICC = 0.713, 95% CI: 0.632–0.779). After removing false positive detections, the PPV of a single fast ripple on spike in an unresected electrode site for post-operative non-seizure free outcome was 85.7 [50–100%]. Including false positive detections reduced the PPV to 64.2 [57.8–69.83%]. ConclusionsApplying automated HFO methods to intraoperative electrocorticography recordings results in false positive fast ripple detections. True fast ripples on spikes are rare, but predict non-seizure free post-operative outcome if found in an unresected site. SignificanceSemi-automated HFO detection methods are required to accurately identify fast ripple events in intra-operative ECoG recordings.

Using Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery.

For the more than 15 million patients who have drug-resistant epilepsy, surgical resection of the region where seizure arise is often the only alternative therapy. However, the identification of this epileptogenic zone (EZ) is often imprecise. Generally, insufficient EZ identification and resection may cause seizures to continue and too much resection may lead to unnecessary neurological deficits. In this paper, an automatic high frequency oscillations (HFOs) detection method based on noise-assisted multivariate EMD (NA-MEMD) is proposed to improve the localization of the EZ for epilepsy patients. In this method, different detected HFO types such as fast-ripple (FR), ripple (R), and fast-ripple concurrent with ripple (FRandR) are utilized to investigate their clinical relevance in identifying EZ. The proposed method may significantly improve the precision by which pathological brain tissue can be identified.

S173. Automatic detection of high frequency oscillations (HFOs) to locate epileptogenic zone

Introduction Surgical resection of epileptogenic zone is the first choice for the treatment of refractory epilepsy, and accurate localization of epileptogenic zone is the key to determine the outcome of the operation. In recent years, There are many research show that high-frequency oscillations (HFOs) is a promising biomarker for the epileptogenic zone. Visual HFOs evaluation is subjectivity and time-consuming. It seriously limits the application of the HFOs in clinic. Therefore, it is very important to develop high frequency oscillation automatic detection program to precisely locate the epileptogenic zone for improving the curative effect of epilepsy surgery. Methods We retrospectively studied EEG data from 20 patients at Xuanwu hospital, Capital Medical University in who underwent epilepsy surgery (from January 2017 to March 2017). Inclusion criterion: 1. Patients with complete medical records and underwent surgery resection. 2. Implanted subdural grids, depth electrode or stereotactic deep electrode (for SEEG). 3. Sampling rate > 2000 Hz. 4. For more than half a year’s follow-up. Exclusion criterion: 1. EEG data with obvious artifacts. 2. Patients with bilateral lesions. 3. Patients with occipital lobe epilepsy. Inclusion criterion of channels: HFOs rate > 1/min (the number of highest HFOs channels is less than 10/min, then all channels are analyzed). All patients signed informed consent and the project was approved by the Ethics Committee of Xuanwu hospital, Capital Medical University. We applied the automatic detection program based on maximum distribution peak point calculating the baseline which developed by our laboratory to perform HFOs analysis for all of patients. After that, we sort the channels from high to low according to the number of HFOs, then the resection ratio was calculated used the total number of channels which from the highest frequency channels to the first channel without resected divided the total number of produced HFOs channel. Finally, the resection rate was compared with the prognosis of the patients. Results Patients are in two groups based on Engel Classification. thirteen patients in good outcome which means Engel Ia (seizure free), and seven patients in poor outcome which means Engel Ib-IV (non-seizure free). In good outcome patients’ group, the resection ratio of ripples is 24% and fast ripple is 82% (the lowest ratio is 40%). In poor outcome patients’ group, the resection ratio of ripples is 6% and fast ripples is 12% (the highest ratio is 21.4%). We found that resection ratio of fast ripple has obvious statistical significance (P Conclusion Our results show that patients who remove higher ratio channels of the Fast ripple would have better surgical outcome, therefore, fast ripple is more accurate than ripple in localizing epileptogenic zone.

The Development of Power Supply for Negative Ion Source Extraction Grid

The extraction grid power supply (EGPS) is important for neutral beam injection of tokamak, which is used to extract the negative ions from the plasma source. A set of power supply with pulse step modulation topology at 16 kV/20 A has been designed for the negative ions extraction at the Institute of Plasma Physics, Chinese Academy of Sciences, which meets the requirements of fast dynamics, low ripple, and good output operation. The development of EGPS is presented in this paper. Two different voltage ratings and series-connected switched power supply (SPS) modules are fed from multisecondary transformer. Insulated-gate bipolar transistors are applied to the switch of SPS module. Rise time and fall time of EGPS out voltage can be set from few microseconds to milliseconds. When the overcurrent occur, the output voltage falls to zero less than $5~\mu \text{s}$ . The dynamic performance tests with dummy load of EGPS are presented. The testing results indicate that the power supply meets the demands of negative ions extraction. The design provides a foundation for the next phase of extraction experimental operation for radio frequency ion source.

Extrahippocampal high-frequency oscillations during epileptogenesis.

The current study aimed to investigate the spatial and temporal patterns of high-frequency oscillations (HFOs) in the intra-/extrahippocampal areas during epileptogenesis. Local field potentials were bilaterally recorded from hippocampus (CA1), thalamus, motor cortex, and prefrontal cortex in 13 rats before and after intrahippocampal kainic acid (KA) lesions. HFOs in the ripple (100-200 Hz) and fast ripple (250-500 Hz) ranges were detected and their rates were computed during different time periods (1-5 weeks) after KA-induced status epilepticus (SE). Recurrent spontaneous seizures were observed in 7 rats after SE, and the other 6 rats did not develop epilepsy. During the latent period, the rate of hippocampal HFOs increased at the ipsilateral site of the KA lesion in both groups, and the HFO rate was significantly higher in the animals that later developed epilepsy. Animals that later developed epilepsy also demonstrated widespread appearance of HFOs, in both the ripple and the fast ripple range, whereas animals that did not develop epilepsy only exhibited changes in the ipsilateral intrahippocampal HFO rate. This study demonstrates an association between an increased rate of widespread HFOs and the later development of epilepsy, suggesting the formation of large-scale distributed pathological networks during epileptogenesis.

Torque Ripple Modeling and Minimization for Interior PMSM Considering Magnetic Saturation

Torque ripple modeling and minimization for interior permanent magnet synchronous machines (IPMSMs) requires accurate information of the inductances, which vary nonlinearly due to magnetic saturation. However, existing approaches fail to consider the magnetic saturation and, thus, their performance are limited under different load conditions. Therefore, this paper improves the torque ripple model by considering magnetic saturation, and employs this model for the optimal current design to improve the performance of torque ripple minimization for IPMSMs under different load conditions. At first, numerical studies are performed to analyze and understand how magnetic saturation affects the torque ripples in IPMSMs. Then, a novel torque ripple model for IPMSMs is developed, in which the inductance term is replaced by exploring the machine electrical model. This improved torque ripple model is computationally efficient and it can provide fast and accurate torque ripple prediction. Based on this model, a genetic algorithm (GA)-based optimal stator current design approach is proposed to minimize the torque ripple in IPMSMs. The proposed GA-based approach can adaptively optimize the stator current under different load conditions, which can guarantee the robust performance of torque ripple minimization under different saturation levels. The proposed approach is validated through an experimental test on a laboratory IPMSM drive system.

On the origin of epileptic High Frequency Oscillations observed on clinical electrodes

ObjectiveIn this study we aim to identify the key (patho)physiological mechanisms and biophysical factors which impact the observability and spectral features of High Frequency Oscillations (HFOs). MethodsIn order to accurately replicate HFOs we developed virtual-brain/virtual-electrode simulation environment combining novel neurophysiological models of neuronal populations with biophysical models for the source/sensor relationship. Both (patho)physiological mechanisms (synaptic transmission, depolarizing GABAA effect, hyperexcitability) and physical factors (geometry of extended cortical sources, size and position of electrodes) were taken into account. Simulated HFOs were compared to real HFOs extracted from intracerebral recordings of 2 patients. ResultsOur results revealed that HFO pathological activity is being generated by feed-forward activation of cortical interneurons that produce fast depolarizing GABAergic post-synaptic potentials (PSPs) onto pyramidal cells. Out of phase patterns of depolarizing GABAergic PSPs explained the shape, entropy and spatiotemporal features of real human HFOs. ConclusionsThe terminology “high-frequency oscillation” (HFO) might be misleading as the fast ripple component (200–600 Hz) is more likely a “high-frequency activity” (HFA), the origin of which is independent from any oscillatory process. SignificanceNew insights regarding the origins and observability of HFOs along depth-EEG electrodes were gained in terms of spatial extent and 3D geometry of neuronal sources.

Resection of high frequency oscillations predicts seizure outcome in the individual patient

High frequency oscillations (HFOs) are recognized as biomarkers for epileptogenic brain tissue. A remaining challenge for epilepsy surgery is the prospective classification of tissue sampled by individual electrode contacts. We analysed long-term invasive recordings of 20 consecutive patients who subsequently underwent epilepsy surgery. HFOs were defined prospectively by a previously validated, automated algorithm 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 over several five-minute time intervals designated the HFO area. The HFO area was fully included in the resected area in all 13 patients who achieved seizure freedom (specificity 100%) and in 3 patients where seizures reoccurred (negative predictive value 81%). The HFO area was only partially resected in 4 patients suffering from recurrent seizures (positive predictive value 100%, sensitivity 57%). Thus, the resection of the prospectively defined HFO area proved to be highly specific and reproducible in 13/13 patients with seizure freedom, while it may have improved the outcome in 4/7 patients with recurrent seizures. We thus validated the clinical relevance of the HFO area in the individual patient with an automated procedure. This is a prerequisite before HFOs can guide surgical treatment in multicentre studies.

A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings

ObjectiveTo develop a reliable software method using a topographic analysis of time-frequency plots to distinguish ripple (80–200 Hz) oscillations that are often associated with EEG sharp waves or spikes (RonS) from sinusoid-like waveforms that appear as ripples but correspond with digital filtering of sharp transients contained in the wide bandwidth EEG. MethodsA custom algorithm distinguished true from false ripples in one second intracranial EEG (iEEG) recordings using wavelet convolution, identifying contours of isopower, and categorizing these contours into sets of open or closed loop groups. The spectral and temporal features of candidate groups were used to classify the ripple, and determine its duration, frequency, and power. Verification of detector accuracy was performed on the basis of simulations, and visual inspection of the original and band-pass filtered signals. ResultsThe detector could distinguish simulated true from false ripple on spikes (RonS). Among 2934 visually verified trials of iEEG recordings and spectrograms exhibiting RonS the accuracy of the detector was 88.5% with a sensitivity of 81.8% and a specificity of 95.2%. The precision was 94.5% and the negative predictive value was 84.0% (N = 12). Among, 1,370 trials of iEEG recording exhibiting RonS that were reviewed blindly without spectrograms the accuracy of the detector was 68.0%, with kappa equal to 0.01 ± 0.03. The detector successfully distinguished ripple from high spectral frequency ‘fast ripple’ oscillations (200–600 Hz), and characterize ripple duration and spectral frequency and power. The detector was confounded by brief bursts of gamma (30–80 Hz) activity in 7.31 ± 6.09% of trials, and in 30.2 ± 14.4% of the true RonS detections ripple duration was underestimated. ConclusionsCharacterizing the topographic features of a time-frequency plot generated by wavelet convolution is useful for distinguishing true oscillations from false oscillations generated by filter ringing. SignificanceCategorizing ripple oscillations and characterizing their properties can improve the clinical utility of the biomarker.

Shortening phase lag between fast ripple and ripple, and lowering power of fast ripple are necessary for seizure discontinuation

Shortening phase lag between fast ripple and ripple, and lowering power of fast ripple are necessary for seizure discontinuation

O154 Deactivation of default mode network in focal epilepsy, inferred by single pulse electrical stimulation

Objective To investigate default-mode network (DMN) deactivation in different types of focal epilepsy by single pulse electric stimulation (SPES). Method We analyzed 18 patients with focal epilepsy (8 frontal, 8 temporal, 2 posterior), implanted with depth electrodes. SPES was applied to each pair of adjacent contacts in seiwure onset zone (SOZ), and responses were recorded from the 10 different DMN-hubs; The responses to SPES in Gamma, Ripple (R) and Fast-Ripple (FR) bands were quantified in a 60–250 ms time-window following each stimulation pulse and compared to baseline. Time of propagation was determined based on N1 latency. These responses were compared across the three epilepsy types. Results Overall, we observed a significant deactivation of the DMN in the upper frequency ranges for all epilepsies (75% drop from baseline for gamma and 93% for R and FR). In the gamma band, the stimulation of frontal SOZs deactivated the ipsilateral posterior cingulate and precuneus significantly more than in the other epilepsy-types, with shorter general latencies of 22, 25 and 23 ms, respectively. Discussion Our results might explain why in prefrontal focal epilepsies and in generalized epilepsies, thought to have a frontal generator, the consciousness is preferentially and significantly impaired. Significance This is a promising method for quantifying the engagement of the consciousness processing areas by the epileptic focus. Detailed illustrations and tables for all 10 DMN-hubs, in all 3 high-frequency bands are provided.