scholarly journals High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle

2016 ◽  
Vol 113 (33) ◽  
pp. 9363-9368 ◽  
Author(s):  
Michel Le Van Quyen ◽  
Lyle E. Muller ◽  
Bartosz Telenczuk ◽  
Eric Halgren ◽  
Sydney Cash ◽  
...  
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
Microelectrode Array ◽  
Slow Wave Sleep ◽  
Inhibitory Neurons ◽  
Sleep Cycle ◽  
High Frequency Oscillations ◽  
Wake Patterns ◽  
Fast Oscillations ◽  
Array Recordings

Beta (β)- and gamma (γ)-oscillations are present in different cortical areas and are thought to be inhibition-driven, but it is not known if these properties also apply to γ-oscillations in humans. Here, we analyze such oscillations in high-density microelectrode array recordings in human and monkey during the wake–sleep cycle. In these recordings, units were classified as excitatory and inhibitory cells. We find that γ-oscillations in human and β-oscillations in monkey are characterized by a strong implication of inhibitory neurons, both in terms of their firing rate and their phasic firing with the oscillation cycle. The β- and γ-waves systematically propagate across the array, with similar velocities, during both wake and sleep. However, only in slow-wave sleep (SWS) β- and γ-oscillations are associated with highly coherent and functional interactions across several millimeters of the neocortex. This interaction is specifically pronounced between inhibitory cells. These results suggest that inhibitory cells are dominantly involved in the genesis of β- and γ-oscillations, as well as in the organization of their large-scale coherence in the awake and sleeping brain. The highest oscillation coherence found during SWS suggests that fast oscillations implement a highly coherent reactivation of wake patterns that may support memory consolidation during SWS.

scholarly journals Scalp EEG interictal high frequency oscillations as an objective EEG biomarker of infantile spasms

2020 ◽  
Author(s):  
Hiroki Nariai ◽  
Shaun A. Hussain ◽  
Danilo Bernardo ◽  
Hirotaka Motoi ◽  
Masaki Sonoda ◽  
...  
Keyword(s):  
Slow Wave ◽  
High Frequency ◽  
Slow Wave Sleep ◽  
Wave Activity ◽  
Infantile Spasms ◽  
Slow Wave Activity ◽  
List Type ◽  
Normal Children ◽  
High Frequency Oscillations ◽  
Epileptic Spasms

ABSTRACTObjectiveTo investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms.MethodsWe retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000 Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80-500 Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3-4 Hz and 0.5-1 Hz as quantified with modulation indices (MIs).ResultsTwenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p < 0.001 by HFO rate; p < 0.01 by MIs at 3-4 Hz; p = 0.02 by MIs at 0.5-1 Hz). The presence of active spasms was predicted by the logistic regression models incorporating HFO-related metrics (AUC: 0.80-0.98) better than that incorporating hypsarrhythmia (AUC: 0.61). The predictive performance of the best model remained favorable (87.5% accuracy) after a cross-validation procedure.ConclusionsIncreased rate of HFOs and coupling between HFOs and SWA are associated with active epileptic spasms.SignificanceScalp-recorded HFOs may serve as an objective EEG biomarker for active epileptic spasms.HighlightsObjective analyses of scalp high frequency oscillations and its coupling with slow-wave activity in infantile spasms were feasible.Increased rate of high frequency oscillations and its coupling with slow-wave activity correlated with active epileptic spasms.The scalp high frequency oscillations were also detected in neurologically normal children (although at the low rate).

scholarly journals The Oscillatory Basis of Working Memory Function and Dysfunction in Epilepsy

2021 ◽  
Vol 14 ◽  
Author(s):  
Olivia N. Arski ◽  
Julia M. Young ◽  
Mary-Lou Smith ◽  
George M. Ibrahim
Keyword(s):  
Working Memory ◽  
High Frequency ◽  
Large Scale ◽  
Memory Function ◽  
Epileptic Activity ◽  
Functional Roles ◽  
High Frequency Oscillations ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges

Working memory (WM) deficits are pervasive co-morbidities of epilepsy. Although the pathophysiological mechanisms underpinning these impairments remain elusive, it is thought that WM depends on oscillatory interactions within and between nodes of large-scale functional networks. These include the hippocampus and default mode network as well as the prefrontal cortex and frontoparietal central executive network. Here, we review the functional roles of neural oscillations in subserving WM and the putative mechanisms by which epilepsy disrupts normative activity, leading to aberrant oscillatory signatures. We highlight the particular role of interictal epileptic activity, including interictal epileptiform discharges and high frequency oscillations (HFOs) in WM deficits. We also discuss the translational opportunities presented by greater understanding of the oscillatory basis of WM function and dysfunction in epilepsy, including potential targets for neuromodulation.

scholarly journals Long-range phase synchronization of high-frequency oscillations in human cortex

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
G. Arnulfo ◽  
S. H. Wang ◽  
V. Myrov ◽  
B. Toselli ◽  
J. Hirvonen ◽  
...  
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
Phase Synchronization ◽  
Brain Activity ◽  
Brain Regions ◽  
Spike Timing ◽  
Human Cortex ◽  
Neuronal Communication ◽  
High Frequency Oscillations ◽  
Connectivity Patterns

Abstract Inter-areal synchronization of neuronal oscillations at frequencies below ~100 Hz is a pervasive feature of neuronal activity and is thought to regulate communication in neuronal circuits. In contrast, faster activities and oscillations have been considered to be largely local-circuit-level phenomena without large-scale synchronization between brain regions. We show, using human intracerebral recordings, that 100–400 Hz high-frequency oscillations (HFOs) may be synchronized between widely distributed brain regions. HFO synchronization expresses individual frequency peaks and exhibits reliable connectivity patterns that show stable community structuring. HFO synchronization is also characterized by a laminar profile opposite to that of lower frequencies. Importantly, HFO synchronization is both transiently enhanced and suppressed in separate frequency bands during a response-inhibition task. These findings show that HFO synchronization constitutes a functionally significant form of neuronal spike-timing relationships in brain activity and thus a mesoscopic indication of neuronal communication per se.

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.

scholarly journals Motoneuron firing patterns underlying fast oscillations in phrenic nerve discharge in the rat

2012 ◽  
Vol 108 (8) ◽  
pp. 2134-2143 ◽  
Author(s):  
Vitaliy Marchenko ◽  
Michael G. Z. Ghali ◽  
Robert F. Rogers
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Medium Frequency ◽  
Adult Rats ◽  
Time Frequency ◽  
High Frequency Oscillations ◽  
Novel Method ◽  
Frequency Coherence ◽  
Fast Oscillations ◽  
First Time

Fast oscillations are ubiquitous throughout the mammalian central nervous system and are especially prominent in respiratory motor outputs, including the phrenic nerves (PhNs). Some investigators have argued for an epiphenomenological basis for PhN high-frequency oscillations because phrenic motoneurons (PhMNs) firing at these same frequencies have never been recorded, although their existence has never been tested systematically. Experiments were performed on 18 paralyzed, unanesthetized, decerebrate adult rats in which whole PhN and individual PhMN activity were recorded. A novel method for evaluating unit-nerve time-frequency coherence was applied to PhMN and PhN recordings. PhMNs were classified according to their maximal firing rate as high, medium, and low frequency, corresponding to the analogous bands in PhN spectra. For the first time, we report the existence of PhMNs firing at rates corresponding to high-frequency oscillations during eupneic motor output. The majority of PhMNs fired only during inspiration, but a small subpopulation possessed tonic activity throughout all phases of respiration. Significant time-varying PhMN-PhN coherence was observed for all PhMN classes. High-frequency, early-recruited units had significantly more consistent onset times than low-frequency, early/middle-recruited and medium-frequency, middle/late-recruited PhMNs. High- and medium-frequency PhMNs had significantly more consistent offset times than low-frequency units. This suggests that startup and termination of PhMNs with higher firing rates are more precisely controlled, which may contribute to the greater PhMN-PhN coherence at the beginning and end of inspiration. Our findings provide evidence that near-synchronous discharge of PhMNs firing at high rates may underlie fast oscillations in PhN discharge.

Neural Mechanisms of the Ultrafast Activities

2005 ◽  
Vol 36 (4) ◽  
pp. 271-277 ◽  
Author(s):  
Isamu Ozaki ◽  
Isao Hashimoto
Keyword(s):  
High Frequency ◽  
Action Potentials ◽  
Evoked Potential ◽  
Neural Mechanisms ◽  
Sleep Cycle ◽  
Stimulus Rate ◽  
High Frequency Oscillations ◽  
Postsynaptic Potential ◽  
Somatosensory Cortices ◽  
Area 3B

A brief review of previous studies is presented on ultrafast activities > 300 Hz (high frequency oscillations, HFOs) overlying the cortical response in the somatosensory evoked potential (SEP) or magnetic field (SEF). The characteristics of somatosensory HFOs are described in terms of reproducibility and origin (area 3b and 1) of the HFOs, changes during a wake-sleep cycle, effects of higher stimulus rate or tactile interference, etc. Also, several hypotheses on the neural mechanisms of the HFOs are introduced; the early HFO burst is probably generated from action potentials of thalamocortical fibers at the time when they arrive at the area 3b (and 1), since this component is resistant to higher stimulus rate > 10Hz or general anesthesia: by contrast, the late HFO burst is sensitive to higher stimulus rate, reflecting activities of a postsynaptic neural network in the somatosensory cortices, area 3b and 1. As to possible mechanisms of the late HFO burst genesis, an interneuron hypothesis, a fast inhibitory postsynaptic potential (IPSP) hypothesis of the pyramidal cell and a chattering cell hypothesis will be discussed on the basis of physiological and pathological features of the somatosensory HFOs.

scholarly journals Towards automatic classification of pathological epileptic tissue with interictal high frequency oscillations

2020 ◽  
Author(s):  
Michael D. Nunez ◽  
Krit Charupanit ◽  
Indranil Sen-Gupta ◽  
Beth A. Lopour ◽  
Jack J. Lin
Keyword(s):  
High Frequency ◽  
Negative Binomial ◽  
Slow Wave Sleep ◽  
Nrem Sleep ◽  
Oscillatory Dynamics ◽  
Scalp Eeg ◽  
High Frequency Oscillations ◽  
Binomial Mixture ◽  
Brain States

AbstractHigh frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection (Frauscher et al., 2017). However, previous research has shown that the number of HFOs per minute (i.e. the HFO “rate”) is not stable over the duration of intracranial recordings. The rate of HFOs increases during periods of slow-wave sleep (von Ellenrieder et al., 2017), and HFOs that are predictive of epileptic tissue may occur in oscillatory patterns (Motoi et al., 2018). We sought to further understand how between-seizure (i.e. “interictal”) HFO dynamics predict the seizure onset zone (SOZ). Using long-term intracranial EEG from 16 subjects, we fit Poisson and Negative Binomial mixture models that describe HFO dynamics and include the ability to switch between two discrete brain states. Oscillatory dynamics of HFO occurrences were found to be predictive of SOZ and were more consistently predictive than HFO rate. Using concurrent scalp-EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM (NREM) sleep and (2) awake and rapid eye movement (REM) sleep. This work suggests that unsupervised approaches for classification of epileptic tissue without sleep-staging can be developed using mixture modeling of HFO dynamics.

scholarly journals Association of Magnetoencephalographically Measured High-Frequency Oscillations in Visual Cortex With Circuit Dysfunctions in Local and Large-scale Networks During Emerging Psychosis

2020 ◽  
Vol 77 (8) ◽  
pp. 852 ◽  
Author(s):  
Tineke Grent-‘t-Jong ◽  
Ruchika Gajwani ◽  
Joachim Gross ◽  
Andrew I. Gumley ◽  
Rajeev Krishnadas ◽  
...  
Keyword(s):  
Visual Cortex ◽  
High Frequency ◽  
Large Scale ◽  
High Frequency Oscillations ◽  
Large Scale Networks

scholarly journals Suppression mechanisms of high frequency electrical stimulation on seizure-like events in rat hippocampal microelectrode array recordings

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S155
Author(s):  
Yun Seo Choi ◽  
Hye-Young Joung ◽  
Sol Ah Kim ◽  
Sang Beom Jun ◽  
Chang-Hyeon Ji ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
High Frequency ◽  
Microelectrode Array ◽  
Array Recordings

scholarly journals Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Guoping Ren ◽  
Yueqian Sun ◽  
Dan Wang ◽  
Jiechuan Ren ◽  
Jindong Dai ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
Slow Wave Sleep ◽  
Intractable Epilepsy ◽  
Wavelet Spectrum ◽  
Epileptogenic Zone ◽  
High Frequency Oscillations ◽  
Scale Transformations ◽  
Better Than

Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80–200 Hz) and fast ripples (FRs, 200–500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner–Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p &lt; 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 ± 1.43% and 77.85 ± 1.61% for ripples and FRs, respectively, p &lt; 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.

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