High-Frequency Oscillations in Somatosensory System

2005 ◽  
Vol 36 (4) ◽  
pp. 278-284 ◽  
Author(s):  
Hitoshi Mochizuki ◽  
Yoshikazu Ugawa
Keyword(s):  
High Frequency ◽  
Neurological Diseases ◽  
Somatosensory System ◽  
Pyramidal Cells ◽  
High Frequency Oscillation ◽  
Medial Lemniscus ◽  
High Frequency Oscillations ◽  
Primary Sensory Cortex ◽  
Thalamocortical Projection ◽  
Deep Brain

The recent revival of interest in high-frequency oscillation (HFO) is triggered by getting an opportunity to noninvasively monitor the timing of highly synchronized and rapidly repeating population spikes generated in the human somatosensory system. HFOs could be recorded from brainstem, cuneothalamic relay neurons, thalamus, thalamocortical radiation, thalamocortical terminals and cortex with deep brain or surface electrodes, or with magnetoencephalography. Here we briefly review the HFOs at each level of somatosensory pathways. HFOs recorded at brainstem might be produced by volume conduction from oscillations of the medial lemniscus. Thalamic HFOs at around 1000 Hz frequency would be generated within the somatosensory thalamus. Cortical HFOs would be generated by at least a few different mechanisms, thalamocortical projection terminals, interneurons and pyramidal cells of the primary sensory cortex. HFOs have been studied in several ways: their modulation by arousal changes, movements or drugs, their recovery function, effects of transcranial magnetic stimulation on them and also their changes in patients with various neurological diseases.

scholarly journals Beta, gamma and High-Frequency Oscillation characterization for targeting in Deep Brain Stimulation procedures

TecnoLógicas ◽  
2020 ◽  
Vol 23 (49) ◽  
pp. 11-32
Author(s):  
Sarah Valderrama-Hincapié ◽  
Sebastián Roldán-Vasco ◽  
Sebastián Restrepo-Agudelo ◽  
Frank Sánchez-Restrepo ◽  
William D. Hutchison ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
High Frequency ◽  
Oscillatory Activity ◽  
High Frequency Oscillation ◽  
Beta Band ◽  
Frequency Pattern ◽  
High Frequency Oscillations ◽  
Power Spectral ◽  
Deep Brain

Deep Brain Stimulation (DBS) has been successfully used to treat patients with Parkinson’s Disease. DBS employs an electrode that regulates the oscillatory activity of the basal ganglia, such as the subthalamic nucleus (STN). A critical point during the surgical implantation of such electrode is the precise localization of the target. This is done using presurgical images, stereotactic frames, and microelectrode recordings (MER). The latter allows neurophysiologists to visualize the electrical activity of different structures along the surgical track, each of them with well-defined variations in the frequency pattern; however, this is far from an automatic or semi-automatic method to help these specialists make decisions concerning the surgical target. To pave the way to automation, we analyzed three frequency bands in MER signals acquired from 11 patients undergoing DBS: beta (13-40 Hz), gamma (40-200 Hz), and high-frequency oscillations (HFO – 201-400 Hz). In this study, we propose and assess five indexes in order to detect the STN: variations in autoregressive parameters and their derivative along the surgical track, the energy of each band calculated using the Yule-Walker power spectral density, the high-to-low (H/L) ratio, and its derivative. We found that the derivative of one parameter of the beta band and the H/L ratio of the HFO/gamma bands produced errors in STN targeting like those reported in the literature produced by image-based methods (<2 mm). Although the indexes introduced here are simple to compute and could be applied in real time, further studies must be conducted to be able to generalize their results.

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 &gt;1.3 or &lt; 0.7 and 17/38 had odds ratios different than zero with high significance (p &lt; 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 &gt; =0.44, good surgery outcome patients; median asymmetry &gt; =0.40, patients with other outcomes; 95% confidence interval &gt; 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 &gt; =0.18, 95% confidence interval &gt;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 Stable high frequency background EEG activity distinguishes epileptic from healthy brain regions

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Annika Minthe ◽  
Wibke G Janzarik ◽  
Daniel Lachner-Piza ◽  
Peter Reinacher ◽  
Andreas Schulze-Bonhage ◽  
...  
Keyword(s):  
High Frequency ◽  
Background Activity ◽  
Sleep Stage ◽  
Strong Association ◽  
Brain Regions ◽  
High Frequency Oscillation ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
Low Entropy

Abstract High-frequency oscillations are markers of epileptic tissue. Recently, different patterns of EEG background activity were described from which high-frequency oscillations occur: high-frequency oscillations with continuously oscillating background were found to be primarily physiological, those from quiet background were linked to epileptic tissue. It is unclear, whether these interactions remain stable over several days and during different sleep-wake stages. High-frequency oscillation patterns (oscillatory vs. quiet background) were analysed in 23 patients implanted with depth and subdural grid electrodes. Pattern scoring was performed on every channel in 10 s intervals in three separate day- and night-time EEG segments. An entropy value, measuring variability of patterns per channel, was calculated. A low entropy value indicated a stable occurrence of the same pattern in one channel, whereas a high value indicated pattern instability. Differences in pattern distribution and entropy were analysed for 143 280 10 s intervals with allocated patterns from inside and outside the seizure onset zone, different electrode types and brain regions. We found a strong association between high-frequency oscillations out of quiet background activity, and channels of the seizure onset zone (35.2% inside versus 9.7% outside the seizure onset zone, P &lt; 0.001), no association was found for high-frequency oscillations from continuous oscillatory background (P = 0.563). The type of background activity remained stable over the same brain region over several days and was independent of sleep stage and recording technique. Stability of background activity was significantly higher in channels of the seizure onset zone (entropy mean value 0.56 ± 0.39 versus 0.64 ± 0.41; P &lt; 0.001). This was especially true for the presumed epileptic high-frequency oscillations out of quiet background (0.57 ± 0.39 inside versus 0.72 ± 0.37 outside the seizure onset zone; P &lt; 0.001). In contrast, presumed physiological high-frequency oscillations from continuous oscillatory backgrounds were significantly more stable outside the seizure onset zone (0.72 ± 0.45 versus 0.48 ± 0.53; P &lt; 0.001). The overall low entropy values suggest that interactions between high-frequency oscillations and background activity are a stable phenomenon specific to the function of brain regions. High-frequency oscillations occurring from a quiet background are strongly linked to the seizure onset zone whereas high-frequency oscillations from an oscillatory background are not. Pattern stability suggests distinct underlying mechanisms. Analysing short time segments of high-frequency oscillations and background activity could help distinguishing epileptic from physiologically active brain regions.

High-Frequency Oscillations and Epileptogenic Network

2021 ◽  
Vol 19 ◽  
Author(s):  
Xiaonan Li ◽  
Herui Zhang ◽  
Huanling Lai ◽  
Jiaoyang Wang ◽  
Wei Wang ◽  
...  
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
High Frequency Oscillation ◽  
Epileptogenic Zone ◽  
Network Development ◽  
High Frequency Oscillations ◽  
Frequency Coupling ◽  
Different Characteristics ◽  
The Relationship ◽  
Cross Frequency Coupling

: Epilepsy is a network disease caused by aberrant neocortical large-scale connectivity spanning regions on the scale of several centimeters. High-frequency oscillations, characterized by the 80–600 Hz signals in electroencephalography, have been proven to be a promising biomarker of epilepsy that can be used in assessing the severity and susceptibility of epilepsy as well as the location of the epileptogenic zone. However, the presence of a high-frequency oscillation network remains a topic of debate as high-frequency oscillations have been previously thought to be incapable of propagation, and the relationship between high-frequency oscillations and the epileptogenic network has rarely been discussed. Some recent studies reported that high-frequency oscillations may behave like networks that are closely relevant to the epileptogenic network. Pathological high-frequency oscillations are network-driven phenomena and elucidate epileptogenic network development; high-frequency oscillations show different characteristics coincident with the epileptogenic network dynamics, and cross-frequency coupling between high-frequency oscillations and other signals may mediate the generation and propagation of abnormal discharges across the network.

Generation of high-frequency oscillations in local circuits of rat somatosensory cortex cultures

1996 ◽  
Vol 76 (6) ◽  
pp. 4180-4184 ◽  
Author(s):  
D. Plenz ◽  
S. T. Kitai
Keyword(s):  
High Frequency ◽  
Pyramidal Neurons ◽  
Reversal Potential ◽  
High Frequency Oscillation ◽  
Cortical Circuits ◽  
High Frequency Oscillations ◽  
Rat Somatosensory Cortex ◽  
Local Circuits ◽  
Stimulation Of

1. Rhythmic cortical activity was investigated with intracellular recordings in cortex-striatum-mesencephalon organotypic cultures grown for 42 +/- 3 (SE) days in vitro. 2. Electrical stimulation of supragranular layers induced a self-sustained high-frequency oscillation (HFO) in pyramidal neurons and interneurons. 3. The HFO started 197 +/- 39 ms after stimulation and had a mean duration of 1.0 +/- 0.2 s and an initial frequency of 38 +/- 2 Hz. A decrease in frequency at a rate of 11.5 +/- 2.7 Hz/s started on average 547 +/- 109 ms after the onset of the HFO. 4. During the HFO, local interneurons and pyramidal neurons synchronized their activities. The synaptic origin of the HFO was confirmed by its reversal potential at -57 +/- 4 mV. 5. These results suggest that a self-maintained HFO can be induced in local cortical circuits by excitation of supragranular layers. This HFO would facilitate synchronization between distant cortical and thalamic regions.

Nonlinear interactions of high-frequency oscillations in the human somatosensory system

2008 ◽  
Vol 119 (11) ◽  
pp. 2647-2657 ◽  
Author(s):  
U. Jaros ◽  
B. Hilgenfeld ◽  
S. Lau ◽  
G. Curio ◽  
J. Haueisen
Keyword(s):  
High Frequency ◽  
Somatosensory System ◽  
Nonlinear Interactions ◽  
High Frequency Oscillations

scholarly journals Scalp high-frequency oscillation rates are higher in younger children

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Dorottya Cserpan ◽  
Ece Boran ◽  
Santo Pietro Lo Biundo ◽  
Richard Rosch ◽  
Johannes Sarnthein ◽  
...  
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Paediatric Population ◽  
High Frequency Oscillation ◽  
Older Children ◽  
Frequency Oscillation ◽  
Scalp Eeg ◽  
High Frequency Oscillations ◽  
Median Test ◽  
Generalized Epilepsy

Abstract High-frequency oscillations in scalp EEG are promising non-invasive biomarkers of epileptogenicity. However, it is unclear how high-frequency oscillations are impacted by age in the paediatric population. We prospectively recorded whole-night scalp EEG in 30 children and adolescents with focal or generalized epilepsy. We used an automated and clinically validated high-frequency oscillation detector to determine ripple rates (80–250 Hz) in bipolar channels. Children &lt; 7 years had higher high-frequency oscillation rates (P = 0.021) when compared with older children. The median test−retest reliability of high-frequency oscillation rates reached 100% (iqr 50) for a data interval duration of 10 min. Scalp high-frequency oscillation frequency decreased with age (r = −0.558, P = 0.002), whereas scalp high-frequency oscillation duration and amplitude were unaffected. The signal-to-noise ratio improved with age (r = 0.37, P = 0.048), and the background ripple band activity decreased with age (r = −0.463, P = 0.011). We characterize the relationship of scalp high-frequency oscillation features and age in paediatric patients. EEG intervals of ≥10 min duration are required for reliable measurements of high-frequency oscillation rates. This study is a further step towards establishing scalp high-frequency oscillations as a valid epileptogenicity biomarker in this vulnerable age group.

Ventilatory saving by external chest wall compression or oral high-frequency oscillation in normal subjects and those with chronic airflow obstruction

1985 ◽  
Vol 69 (3) ◽  
pp. 349-359 ◽  
Author(s):  
R. J. D. George ◽  
R. J. D. Winter ◽  
S. J. Flockton ◽  
D. M. Geddes
Keyword(s):  
Chest Wall ◽  
High Frequency ◽  
Minute Ventilation ◽  
Airflow Obstruction ◽  
Normal Subjects ◽  
High Frequency Oscillation ◽  
Tidal Breathing ◽  
Frequency Oscillation ◽  
Resonant Frequencies ◽  
High Frequency Oscillations

1. Oscillation of the air within the lungs at high frequency is associated with an increased clearance of CO2. Because of the high frequency and low volume of these oscillations, spontaneous breathing is unhindered and the technique has potential value as a supplement to ventilation. 2. High-frequency oscillations were superimposed upon tidal breathing by using a loudspeaker attached to a mouthpiece (oral high-frequency oscillation, OHFO) or by external chest wall compression (ECWC). We set out (a) to compare the changes in ventilation and breathlessness by using OHFO and ECWC in normal subjects with those in patients with chronic airflow obstruction (CAO), and (b) to relate the pattern of saving to the resonant frequencies of the respiratory system as a whole (fOT, 5–10 Hz in normal subjects, 16–26 Hz in CAO) and those of the ribcage(foc,70 Hz). 3. OHFO reduced minute ventilation (VE) by up to 46% in normal subjects (P < 0.01) and 29% in CAO (P < 0.01) without any rise in CO2. ECWC reduced VE by 27% in normal subjects (P < 0.01) and 16% in CAO (P < 0.01) without a rise in CO2. 4. High-frequency oscillation by either method relieved breathlessness in those with CAO and was comfortable and well tolerated. 5. In normal subjects for was discrete and varied little with respiration. Maximum savings occurred around for (5–10 Hz). 6. In CAO, there was no obvious single resonant frequency and flow and pressure signals were intermittently in phase over a band of about 10 Hz. Thus the reductions in minute ventilation were only loosely related to for (13–26 Hz). Neither group reduced VE at foc (65–75 Hz). 7. OHFO has considerable potential in the management of patients with CAO, where it may be of value as an assistance to breathing and in the relief of breathlessness. ECWC, although effective in principle, is impractical by our methods and awaits the development of an acceptable delivery system.

scholarly journals A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Liu ◽  
Siqi Sun ◽  
Yang Liu ◽  
Jiayang Guo ◽  
Hailong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Modern Medicine ◽  
High Frequency Oscillation ◽  
High Frequency Oscillations ◽  
Clinical Detection ◽  
Intracranial Electrodes ◽  
Intracranial Electroencephalography ◽  
First Time

Epilepsy is a neurological disease, and the location of a lesion before neurosurgery or invasive intracranial electroencephalography (iEEG) surgery using intracranial electrodes is often very challenging. The high-frequency oscillation (HFOs) mode in MEG signal can now be used to detect lesions. Due to the time-consuming and error-prone operation of HFOs detection, an automatic HFOs detector with high accuracy is very necessary in modern medicine. Therefore, an optimized capsule neural network was used, and a MEG (magnetoencephalograph) HFOs detector based on MEGNet was proposed to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first time that a neural network has been used to detect HFOs in MEG. After optimized configuration, the accuracy, precision, recall, and F1-score of the proposed detector reached 94%, 95%, 94%, and 94%, which were better than other classical machine learning models. In addition, we used the k-fold cross-validation scheme to test the performance consistency of the model. The distribution of various performance indicators shows that our model is robust.

High-Frequency Signals (>400 Hz): A New Window in Electrophysiological Analysis of the Somatosensory System

2005 ◽  
Vol 36 (4) ◽  
pp. 285-292 ◽  
Author(s):  
Y. Okada ◽  
I. Ikeda ◽  
T. Zhang ◽  
Y. Wang
Keyword(s):  
High Frequency ◽  
Cortical Neurons ◽  
Somatosensory System ◽  
Pyramidal Cells ◽  
Swine Model ◽  
Electrophysiological Analysis ◽  
Somatosensory Pathway

High-frequency signals (HFSs) between 400–1500 Hz in Magnetoencephalography (MEG) and Electroencephalography (EEG) provide a new window in electrophysiological analysis of the somatosensory system in humans and in other animals. The HFS in the primary somatosensory (SI) cortex precedes the conventional N20. In the swine model, they appear to be due to spiking in thalamocortical axonal terminals and in the soma and dendrites of cortical neurons. These spiking activities seem to activate slower conductances in the pyramidal cells in layers II-III and V, which give rise to N20. The HFS monitoring may be useful for separately evaluating the electrophysiology of the subcortical and cortical components of the somatosensory pathway.

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