Selective loss of high-frequency oscillations in phrenic and hypoglossal activity in the decerebrate rat during gasping

2006 ◽  
Vol 291 (5) ◽  
pp. R1414-R1429 ◽  
Author(s):  
Vitaliy Marchenko ◽  
Robert F. Rogers
Keyword(s):  
Power Distribution ◽  
High Frequency ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
High Amplitude ◽  
Medial Branch ◽  
Adult Rats ◽  
Time Frequency ◽  
High Frequency Oscillations ◽  
Nerve Recordings

Respiratory motor outputs contain medium-(MFO) and high-frequency oscillations (HFO) that are much faster than the fundamental breathing rhythm. However, the associated changes in power spectral characteristics of the major respiratory outputs in unanesthetized animals during the transition from normal eupneic breathing to hypoxic gasping have not been well characterized. Experiments were performed on nine unanesthetized, chemo- and barodenervated, decerebrate adult rats, in which asphyxia elicited hyperpnea, followed by apnea and gasping. A gated fast Fourier transform (FFT) analysis and a novel time-frequency representation (TFR) analysis were developed and applied to whole phrenic and to medial branch hypoglossal nerve recordings. Our results revealed one MFO and one HFO peak in the phrenic output during eupnea, where HFO was prominent in the first two-thirds of the burst and MFO was prominent in the latter two-thirds of the burst. The hypoglossal activity contained broadband power distribution with several distinct peaks. During gasping, two high-amplitude MFO peaks were present in phrenic activity, and this state was characterized by a conspicuous loss in HFO power. Hypoglossal activity showed a significant reduction in power and a shift in its distribution toward lower frequencies during gasping. TFR analysis of phrenic activity revealed the increasing importance of an initial low-frequency “start-up” burst that grew in relative intensity as hypoxic conditions persisted. Significant changes in MFO and HFO rhythm generation during the transition from eupnea to gasping presumably reflect a reconfiguration of the respiratory network and/or alterations in signal processing by the circuitry associated with the two motor pools.

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.

Time-frequency coherence analysis of phrenic and hypoglossal activity in the decerebrate rat during eupnea, hyperpnea, and gasping

2006 ◽  
Vol 291 (5) ◽  
pp. R1430-R1442 ◽  
Author(s):  
Vitaliy Marchenko ◽  
Robert F. Rogers
Keyword(s):  
Low Frequency ◽  
Adult Rats ◽  
Time Frequency ◽  
High Frequency Oscillations ◽  
Respiratory Network ◽  
Decerebrate Rat ◽  
State Changes ◽  
Nerve Recordings ◽  
Frequency Coherence ◽  
Respiratory Rhythms

Fast respiratory rhythms include medium- (MFO) and high-frequency oscillations (HFO), which are much faster than the fundamental breathing rhythm. According to previous studies, HFO is characterized by high coherence (Coh) in phrenic (Ph) nerve activity, thereby providing a means of distinguishing between these two types of oscillations. Changes in Coh between the Ph and hypoglossal (XII) nerves during the transition from normal eupnic breathing to gasping have not been characterized. Experiments were performed on nine unanesthetized, chemo- and barodenervated, decerebrate adult rats, in which sustained asphyxia elicited hyperpnea and gasping. A gated time-frequency Coh analysis was developed and applied to whole Ph and medial XII nerve recordings. The results showed dynamic Ph-Ph Coh during eupnea, including MFO and HFO. XII-XII Coh during eupnea was broadband and included four distinct peaks, with low-frequency Coh dominating the epochs preceding the onset of Ph activity. During gasping, only MFO-peaks were present in Ph-Ph Coh. Bilateral XII activity showed a significant reduction in Coh and a shift toward lower frequencies during gasping. In contrast, contralateral Ph-XII Coh progressively increased during state changes from eupnea to gasping, a tendency mirrored in the startup part of the Ph activity. These data suggest significant hypoxia/hypercapnia-induced alterations in synchronization between respiratory outputs during the transition from eupnea to gasping, reflecting a reconfiguration of the respiratory network and/or alterations in the circuitry associated with the motor pools, including dynamic coupling between outputs.

scholarly journals Pathological and physiological high-frequency oscillations in focal human epilepsy

2013 ◽  
Vol 110 (8) ◽  
pp. 1958-1964 ◽  
Author(s):  
Andrew Matsumoto ◽  
Benjamin H. Brinkmann ◽  
S. Matthew Stead ◽  
Joseph Matsumoto ◽  
Michal T. Kucewicz ◽  
...  
Keyword(s):  
High Frequency ◽  
Motor Task ◽  
High Amplitude ◽  
Spectral Amplitude ◽  
Support Vector ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Human Epilepsy ◽  
High Frequency Oscillations ◽  
Measuring Frequency

High-frequency oscillations (HFO; gamma: 40–100 Hz, ripples: 100–200 Hz, and fast ripples: 250–500 Hz) have been widely studied in health and disease. These phenomena may serve as biomarkers for epileptic brain; however, a means of differentiating between pathological and normal physiological HFO is essential. We categorized task-induced physiological HFO during periods of HFO induced by a visual or motor task by measuring frequency, duration, and spectral amplitude of each event in single trial time-frequency spectra and compared them to pathological HFO similarly measured. Pathological HFO had higher mean spectral amplitude, longer mean duration, and lower mean frequency than physiological-induced HFO. In individual patients, support vector machine analysis correctly classified pathological HFO with sensitivities ranging from 70–98% and specificities >90% in all but one patient. In this patient, infrequent high-amplitude HFO were observed in the motor cortex just before movement onset in the motor task. This finding raises the possibility that in epileptic brain physiological-induced gamma can assume higher spectral amplitudes similar to those seen in pathologic HFO. This method if automated and validated could provide a step towards differentiating physiological HFO from pathological HFO and improving localization of epileptogenic brain.

Combining Distributed Acoustic Sensing and Beamforming in a Volcanic Environment on Mount Meager, British Columbia.

2021 ◽  
Author(s):  
Sara Klaasen ◽  
Patrick Paitz ◽  
Jan Dettmer ◽  
Andreas Fichtner
Keyword(s):  
British Columbia ◽  
Power Distribution ◽  
High Frequency ◽  
Low Frequency ◽  
Volcanic Tremor ◽  
Extreme Environment ◽  
Volcanic Complex ◽  
Acoustic Sensing ◽  
Distributed Acoustic Sensing ◽  
Volcanic Environment

<p>We present one of the first applications of Distributed Acoustic Sensing (DAS) in a volcanic environment. The goals are twofold: First, we want to examine the feasibility of DAS in such a remote and extreme environment, and second, we search for active volcanic signals of Mount Meager in British Columbia (Canada). </p><p>The Mount Meager massif is an active volcanic complex that is estimated to have the largest geothermal potential in Canada and caused its largest recorded landslide in 2010. We installed a 3-km long fibre-optic cable at 2000 m elevation that crosses the ridge of Mount Meager and traverses the uppermost part of a glacier, yielding continuous measurements from 19 September to 17 October 2019.</p><p>We identify ~30 low-frequency (0.01-1 Hz) and 3000 high-frequency (5-45 Hz) events. The low-frequency events are not correlated with microseismic ocean or atmospheric noise sources and volcanic tremor remains a plausible origin. The frequency-power distribution of the high-frequency events indicates a natural origin, and beamforming on these events reveals distinct event clusters, predominantly in the direction of the main peaks of the volcanic complex. Numerical examples show that we can apply conventional beamforming to the data, and that the results are improved by taking the signal-to-noise ratio of individual channels into account.</p><p>The increased data quantity of DAS can outweigh the limitations due to the lower quality of individual channels in these hazardous and remote environments. We conclude that DAS is a promising tool in this setting that warrants further development.</p>

scholarly journals An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Dongju Chen ◽  
Shuai Zhou ◽  
Lihua Dong ◽  
Jinwei Fan
Keyword(s):  
Machine Tool ◽  
Frequency Domain ◽  
Surface Topography ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Time Frequency ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method

This paper presents a new identification method to identify the main errors of the machine tool in time-frequency domain. The low- and high-frequency signals of the workpiece surface are decomposed based on the Daubechies wavelet transform. With power spectral density analysis, the main features of the high-frequency signal corresponding to the imbalance of the spindle system are extracted from the surface topography of the workpiece in the frequency domain. With the cross-correlation analysis method, the relationship between the guideway error of the machine tool and the low-frequency signal of the surface topography is calculated in the time domain.

scholarly journals Exclusion of the Possibility of “False Ripples” From Ripple Band High-Frequency Oscillations Recorded From Scalp Electroencephalogram in Children With Epilepsy

2021 ◽  
Vol 15 ◽  
Author(s):  
Katsuhiro Kobayashi ◽  
Takashi Shibata ◽  
Hiroki Tsuchiya ◽  
Tomoyuki Akiyama
Keyword(s):  
High Frequency ◽  
Numerical Differentiation ◽  
Convincing Evidence ◽  
Exact Test ◽  
Time Frequency ◽  
High Frequency Oscillations ◽  
Eeg Data ◽  
Group A ◽  
Spurious Oscillations ◽  
Scalp Electroencephalogram

AimRipple-band epileptic high-frequency oscillations (HFOs) can be recorded by scalp electroencephalography (EEG), and tend to be associated with epileptic spikes. However, there is a concern that the filtration of steep waveforms such as spikes may cause spurious oscillations or “false ripples.” We excluded such possibility from at least some ripples by EEG differentiation, which, in theory, enhances high-frequency signals and does not generate spurious oscillations or ringing.MethodsThe subjects were 50 pediatric patients, and ten consecutive spikes during sleep were selected for each patient. Five hundred spike data segments were initially reviewed by two experienced electroencephalographers using consensus to identify the presence or absence of ripples in the ordinary filtered EEG and an associated spectral blob in time-frequency analysis (Session A). These EEG data were subjected to numerical differentiation (the second derivative was denoted as EEG″). The EEG″ trace of each spike data segment was shown to two other electroencephalographers who judged independently whether there were clear ripple oscillations or uncertain ripple oscillations or an absence of oscillations (Session B).ResultsIn Session A, ripples were identified in 57 spike data segments (Group A-R), but not in the other 443 data segments (Group A-N). In Session B, both reviewers identified clear ripples (strict criterion) in 11 spike data segments, all of which were in Group A-R (p < 0.0001 by Fisher’s exact test). When the extended criterion that included clear and/or uncertain ripples was used in Session B, both reviewers identified 25 spike data segments that fulfilled the criterion: 24 of these were in Group A-R (p < 0.0001).DiscussionWe have demonstrated that real ripples over scalp spikes exist in a certain proportion of patients. Ripples that were visualized consistently using both ordinary filters and the EEG″ method should be true, but failure to clarify ripples using the EEG″ method does not mean that true ripples are absent.ConclusionThe numerical differentiation of EEG data provides convincing evidence that HFOs were detected in terms of the presence of such unusually fast oscillations over the scalp and the importance of this electrophysiological phenomenon.

“Eclipse” Bifurcation in a Twinkling Oscillator

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Smruti R. Panigrahi ◽  
Brian F. Feeny ◽  
Alejandro R. Diaz
Keyword(s):  
Bifurcation Point ◽  
High Frequency ◽  
Low Frequency ◽  
Negative Stiffness ◽  
Symmetric System ◽  
Ambient Vibrations ◽  
High Frequency Oscillations ◽  
Mass Chain

This work regards the use of cubic springs with intervals of negative stiffness, in other words, “snap-through” elements, in order to convert low-frequency ambient vibrations into high-frequency oscillations, referred to as “twinkling.” The focus of this paper is on the bifurcation of a two-mass chain that, in the symmetric system, involves infinitely many equilibria at the bifurcation point. The structure of this “eclipse bifurcation” is uncovered, and perturbations of the bifurcation are studied. The energies associated with the equilibria are examined.

Global “Eclipse” Bifurcation in a Twinkling Oscillator

2013 ◽  
Author(s):  
Smruti R. Panigrahi ◽  
Brian F. Feeny ◽  
Alejandro R. Diaz
Keyword(s):  
Bifurcation Point ◽  
High Frequency ◽  
Global Bifurcation ◽  
Low Frequency ◽  
Negative Stiffness ◽  
Symmetric System ◽  
Ambient Vibrations ◽  
High Frequency Oscillations ◽  
Mass Chain

This work regards the use of cubic springs with intervals of negative stiffness, in other words “snap-through” elements, in order to convert low-frequency ambient vibrations into high-frequency oscillations, referred to as “twinkling”. The focus of this paper is on a global bifurcation of a two-mass chain which, in the symmetric system, involves infinitely many equilibria at the bifurcation point. The structure of this “eclipse” bifurcation is uncovered, and perturbations of the bifurcation are studied. The energies associated with the equilibria are examined.

scholarly journals Recognition of Emotional States using Multiscale Information Analysis of High Frequency EEG Oscillations

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 609 ◽  
Author(s):  
Gao ◽  
Cui ◽  
Wan ◽  
Gu
Keyword(s):  
Emotion Recognition ◽  
High Frequency ◽  
Low Frequency ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Emotional States ◽  
Circumplex Model ◽  
Information Analysis ◽  
Eeg Signals ◽  
High Frequency Oscillations

Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell's circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51–100Hz) of EEG signals rather than low frequency oscillations (0.3–49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals.

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