Contributors: Hitoshi Mochizuki, Yoshikazu Ugawa, Katsuyuki Machii, Yasuo Terao, Ritsuko Hanajima, Toshiaki Furubayashi, Haruo Uesugi, Ichiro Kanazawa

Date: 1999-01-01

Somatosensory evoked potentials of a normal subject. Two SEPs are superimposed in a single trace. A few small notches were seen on the ascending slope of N20 in raw SEPs (A). The onset of N20 component was 15.7 ms (dotted line). Oscillation potentials were clearly detectable and their amplitudes were measurable in SEPs filtered at 500 to 1000 Hz (B). Three potentials (indicated by arrows) were judged as significant (larger than 3 times mean amplitude range of background recordings) peaks of an oscillation. ...The amplitudes of oscillation potentials (A) and their size ratios to the main components of SEP (B,C) were plotted against the groups of their onset latencies. (•, controls; ○, Parkinson's disease; ×, myoclonus epilepsy). The latencies for the 1st to 10th groups were 0.0–1.2, 1.3–2.5, 2.6–3.9, 4.0–5.3, 5.4–7.0, 7.1–8.7, 8.8–10.4, 10.5–12.1, 12.2–12.8 and 12.9–14.5 ms. The amplitudes of oscillation potentials are shown in (A), the ratio of the amplitude of oscillation potential to that of N20o-N20p [ratio (osc/N20o-N20p)] in (B), and the ratio of oscillation potential to N20p-P25p [ratio (osc/N20p-N25p)] in (C). (A) In PD patients, the sizes of some oscillation potentials were abnormally larger than the normal oscillation potentials in the first to sixth groups. In contrast, in patients with ME, extremely enlarged oscillations were observed in the fourth to tenth groups. (B) The ratios (osc/N20o-N20p) for abnormally enlarged oscillation potentials were significantly larger than the normal values in both patients with PD and ME. (C) In PD patients, the ratios (osc/N20p-P25p) for enlarged oscillation peaks were again abnormally larger than the normal values. In ME patients, however, those for enlarged potentials were the same as the normal values for earlier (1st to 5th) group oscillation potentials. In patients with ME, oscillation potentials were present at late latencies when they were never seen in normal subjects. ...High-frequency oscillation...High-frequency oscillation...SEPs of a patient with Parkinson's disease. Several notches were clearly seen on the ascending and descending slopes of N20 even in conventional SEPs (A). The onset of N20 (14.4 ms) following the end of P14 subcortical component is shown by a dashed line. Six oscillation potentials (indicated by arrows) followed P14 in highly filtered (500–1000 Hz) SEPs (B). Four of them (indicated by large arrows) were abnormally enlarged (>mean+3 SD of normal values). This patient was considered to have a giant oscillation. ...Aim: A high-frequency oscillation in the range of 600–900 Hz has been shown to be a component of the somatosensory evoked potential (SEP) in humans. In the present communication, we studied these oscillation potentials in two neurological disorders....Aim: A high-frequency oscillation in the range of 600–900 Hz has been shown to be a component of the somatosensory evoked potential (SEP) in humans. In the present communication, we studied these oscillation potentials in two neurological disorders....Histograms of the onset latencies of oscillation potentials in normal subjects (A) and Parkinson's disease patients (B). There were five groups in the onset latencies of oscillation potentials in normal subjects (A). In patients with Parkinson's disease, oscillation potentials were observed at almost the same latency groups as normals (B). ...Somatosensory evoked high-frequency oscillation in Parkinson's disease and myoclonus epilepsy...SEPs of a patient with myoclonus epilepsy (ME). In conventional SEPs (A), the latencies of N20, P25 and N33 components were within the normal range even though the N20-P25 and P25-N33 amplitudes were extremely enlarged. Several oscillation potentials were seen on the slope from P25 to N33 and descending slope of N33. Clearly differentiated 9 oscillation potentials (arrows) were detected in SEPs filtered 500–1000 Hz (B). Seven of them (third to ninth potentials) were abnormally large (large arrows). The onset of sixth potential was 8.9 ms. The last 4 oscillation potentials were evoked at the latencies when no oscillation potentials were observed in normals. ... Aim: A high-frequency oscillation in the range of 600–900 Hz has been shown to be a component of the somatosensory evoked potential (SEP) in humans. In the present communication, we studied these oscillation potentials in two neurological disorders.

Contributors: Wolfgang Winkler, Johannes Borngräber, Bernd Heinemann

Date: 2005-02-01

Photograph of the oscillator chip. ...A 117GHz LC-oscillator in SiGe:C BiCMOS technology...In this paper a voltage-controlled oscillator (VCO) is presented reaching oscillation frequencies well above 100GHz. The oscillator has been fabricated in a 200GHz SiGe:C BiCMOS technology with 0.25μm minimum feature size. In the design of the VCO two circuit approaches were considered. The first used transmission- lines in the resonator and the second used inductors above the silicon substrate. It is shown by simulation that by using inductors a higher oscillation frequency can be obtained. The fabricated oscillator has a tuning range from 113.2 to 117.2GHz at a supply voltage of −3V. This oscillation frequency is the highest reported so far for a silicon-based transistor technology....Tuning curve of the LC oscillator. ...In this paper a voltage-controlled oscillator (VCO) is presented reaching oscillation frequencies well above 100GHz. The oscillator has been fabricated in a 200GHz SiGe:C BiCMOS technology with 0.25μm minimum feature size. In the design of the VCO two circuit approaches were considered. The first used transmission- lines in the resonator and the second used inductors above the silicon substrate. It is shown by simulation that by using inductors a higher oscillation frequency can be obtained. The fabricated oscillator has a tuning range from 113.2 to 117.2GHz at a supply voltage of −3V. This oscillation frequency is the highest reported so far for a silicon-based transistor technology....Simulation results and measurement of the 117GHz oscillator (VEE=-3V) ...Circuit diagram of the high frequency oscillator. ...LC-oscillator...Circuit diagram of the high frequency oscillator. ... In this paper a voltage-controlled oscillator (VCO) is presented reaching oscillation frequencies well above 100GHz. The oscillator has been fabricated in a 200GHz SiGe:C BiCMOS technology with 0.25μm minimum feature size. In the design of the VCO two circuit approaches were considered. The first used transmission- lines in the resonator and the second used inductors above the silicon substrate. It is shown by simulation that by using inductors a higher oscillation frequency can be obtained. The fabricated oscillator has a tuning range from 113.2 to 117.2GHz at a supply voltage of −3V. This oscillation frequency is the highest reported so far for a silicon-based transistor technology.

Contributors: Binbin Qiu, Junjie Yan, Jiping Liu, Daotong Chong, Quanbin Zhao, Xinzhuang Wu

Date: 2014-01-01

The first and the second dominant frequencies variation with the steam mass flux. ...The first and the second dominant frequencies variation with the steam mass flux. ...Frequen...The dominant frequency regime map. ...The dominant frequency regime map. ...Experimental investigation on the second dominant frequency of pressure oscillation for sonic steam jet in subcooled water...Experimental investigations and analysis on the dominant frequency of pressure oscillation for sonic steam jet in subcooled water have been performed. It was found that sometimes there is only one dominant frequency for pressure oscillation, and sometimes there is a second dominant frequency for pressure oscillation. The first dominant frequency had been investigated by many scholars before, but the present study mainly investigated the characteristics of the second dominant frequency. The first dominant frequency is mainly caused by the periodical variation of the steam plume and the second dominant frequency is mainly caused by the generating and rupture of the large steam bubbles. A dominant frequency regime map related to the water temperature and steam mass flux is given. When the water temperature and the steam mass flux are low, there is only one dominant frequency of pressure oscillation. When the water temperature or the steam mass flux is high, the second dominant frequency appears for pressure oscillation. The second dominant frequency decreases with the increasing water temperature and steam mass flux. Meanwhile, the second dominant frequency at high steam mass flux and water temperature is lower than the first dominant frequency at low steam mass flux and water temperature. A dimensionless correlation is proposed to predict the second dominant frequency for sonic steam jet. The predictions agree well with the present experimental data, the discrepancies are within ±20%....The first and the second dominant frequencies variation with the water temperature. ...Dominant frequency...The first and the second dominant frequencies variation with the water temperature. ...Pressure oscillation...Frequency spectrums of pressure oscillation at different water temperatures and steam mass flux. ...Experimental investigations and analysis on the dominant frequency of pressure oscillation for sonic steam jet in subcooled water have been performed. It was found that sometimes there is only one dominant frequency for pressure oscillation, and sometimes there is a second dominant frequency for pressure oscillation. The first dominant frequency had been investigated by many scholars before, but the present study mainly investigated the characteristics of the second dominant frequency. The first dominant frequency is mainly caused by the periodical variation of the steam plume and the second dominant frequency is mainly caused by the generating and rupture of the large steam bubbles. A dominant frequency regime map related to the water temperature and steam mass flux is given. When the water temperature and the steam mass flux are low, there is only one dominant frequency of pressure oscillation. When the water temperature or the steam mass flux is high, the second dominant frequency appears for pressure oscillation. The second dominant frequency decreases with the increasing water temperature and steam mass flux. Meanwhile, the second dominant frequency at high steam mass flux and water temperature is lower than the first dominant frequency at low steam mass flux and water temperature. A dimensionless correlation is proposed to predict the second dominant frequency for sonic steam jet. The predictions agree well with the present experimental data, the discrepancies are within ±20%....The dominant frequencies in different measurement points by Qiu et al. [14]. ...Comparison of experimental first dominant frequency and predictions by other correlations. ...The reproducibility of the experiment. ... Experimental investigations and analysis on the dominant frequency of pressure oscillation for sonic steam jet in subcooled water have been performed. It was found that sometimes there is only one dominant frequency for pressure oscillation, and sometimes there is a second dominant frequency for pressure oscillation. The first dominant frequency had been investigated by many scholars before, but the present study mainly investigated the characteristics of the second dominant frequency. The first dominant frequency is mainly caused by the periodical variation of the steam plume and the second dominant frequency is mainly caused by the generating and rupture of the large steam bubbles. A dominant frequency regime map related to the water temperature and steam mass flux is given. When the water temperature and the steam mass flux are low, there is only one dominant frequency of pressure oscillation. When the water temperature or the steam mass flux is high, the second dominant frequency appears for pressure oscillation. The second dominant frequency decreases with the increasing water temperature and steam mass flux. Meanwhile, the second dominant frequency at high steam mass flux and water temperature is lower than the first dominant frequency at low steam mass flux and water temperature. A dimensionless correlation is proposed to predict the second dominant frequency for sonic steam jet. The predictions agree well with the present experimental data, the discrepancies are within ±20%.

Contributors: Eric Maris, Marieke van Vugt, Michael Kahana

Date: 2011-01-15

Illustration of a tensor decomposition of the four-dimensional array of weighted phase-locking factors (wPLFs). The two spatial maps and the two frequency spectra (see text) are each denoted by a different color (red, yellow, green, and blue) and a different index (i, i', f, f'). The same colors are used both for the boundaries of the panels and the symbols in the formula for the wPLF. In panels a and b, with red and index i, we show the complex-valued spatial map of the high-frequency bursts that are coupled to a common low-frequency oscillation (the coupling oscillation). In panel a, we show the magnitude (absolute value) of this complex-valued spatial map (one colored circle per channel), which expresses the strength of the coupling. In panel b we show the phases of the coupling oscillation to which the high-frequency bursts are locked (one arrow per channel). In panel c, with yellow and index i', we show the spatial map of the coupling oscillation (one colored circle per channel). The more a coefficient bi’ deviates from zero, the more this channel is affected by the coupling oscillation. In panel d, with green and index f, we show the frequency spectrum of the phase-coupled bursts (shown on a logarithmic scale). In panel e, with blue and index f', we show the frequency spectrum of the coupling oscillation. The spectra shown in panels a, b, c, d and e are all in arbitrary units (a.u.). This is because the spectra are produced by a tensor decomposition which involves an arbitrary multiplicative scaling (see Supplemental Material). In panels f, h, and j, we show the magnitudes of the complex wPLFs for three selected channel pairs (see text). By means of arrows, we connect the channels in panels a and c for which these wPLFs were calculated, with the corresponding x-axes (showing the frequency of the coupling oscillation), respectively, y-axes (showing the frequency of the phase-coupled bursts), in panels f, h, and j. In panels g and i, we show the phases of the complex wPLFs that correspond to panels f and h, respectively. ...Our conclusion about the number of sources involved in PAC depends on our definition of a source. We defined a source in terms of two patterns: (1) a spatial map that specifies how strongly source activity affects the measurements at the sensor level, and (2) its frequency spectrum. We showed that, with this source definition, the array of PAC-measures (wPLFs) can be written as a tensor product of two spatial maps (one complex- and one real-valued) and two frequency spectra (both real-valued), which is exactly the structure that is extracted by our tensor decomposition (see Supplemental Methods). However, we cannot exclude PAC-generating source configurations that cannot be characterized in this way. In fact, we have argued that PAC-patterns may also be generated by a source configuration that would be considered a single source when viewed from the perspective of the mechanism that generates the physiological signal. This confronts us with the problem that sources can be defined both in terms of their formal characteristics (i.e., in terms of a spatial map and a frequency spectrum) and in terms of the neuronal network that generates the physiological signal. The difference between the two definitions is most clear if the physiological mechanism consist of multiple components, such as networks of inhibitory neurons that are connected to one or multiple classes of principal neurons, each with its own network topology. This whole multi-component network may be considered as a single source, but also as multiple sources, each one corresponding to one component. Importantly, if these components differ with respect to their spatial maps and frequency spectra, then they can be extracted by means of tensor decomposition. This shows there may be a need for a linking of the set of extracted source configurations on the basis of the neuronal interactions that may have produced them....Localization of components showing a reliable modulation in PAC strength between the activation and the baseline period. The upper panel shows the location of the components with a PAC strength that decreases from the baseline to the activation period. Every component is denoted by a different color. Electrodes are denoted by disks with a diameter proportional to the magnitude of their loadings in the spatial map of the high-frequency bursts. PAC decreases are predominantly observed over the right temporal lobe, but there also decreases over a few left frontal and left temporal areas. The lower panel shows the location of the components with a PAC strength that increases from the baseline to the activation period. PAC increases are predominantly observed over the right temporal and the right parietal lobe, but there are also increases over a few left parietal, left temporal, and right frontal areas. ...Package of figures showing the spatial maps and the frequency spectra of a representative set of reliable cross-frequency patterns extracted from the activation period wPLFs. The figures belonging to one cross-frequency patterns are each in one folder, of which the name refers to the patient. The files that have freqspectra_and_compass as a part of their name contain a figure of which the left panel show the frequency spectra of a coupling oscillation and the associated phase-coupled bursts, and the right panel shows the preferred phases of the PAC in a compass plots. The files that have _IA_ (instantaneous amplitude) as a part of their name contain figures of the magnitudes of the spatial maps for the phase-coupled bursts. Different figures show different subsets of the electrodes, each from on optimal viewpoint. Electrode subsets on the medial side of the brain are shown from two viewpoints, lateral and occipital. The files that have _AWIP_ (amplitude-weighted instantaneous phase) as a part of their name contain figures of the spatial maps of the coupling oscillation. Again, different figures show different subsets of the electrodes, each from on optimal viewpoint. ...Spatial maps of the phase-coupled bursts are smaller than the spatial maps of the associated coupling oscillation. Both panels show scatter plots of the extent of the spatial maps of the phase-coupled bursts (horizontal axis) against the extent of the corresponding spatial maps of the coupling oscillation (vertical axis). Panels a and b show the scatter plots for, respectively, the baseline and the activation period. Panels a and b show the scatter plots for, respectively, the baseline and the activation period. In both periods, for most of the PAC patterns, the extent of the spatial map of the phase-coupled bursts is substantially smaller than the extent of the spatial map of the associated coupling oscillation. ...Our conclusion about the number of sources involved in PAC depends on our definition of a source. We defined a source in terms of two patterns: (1) a spatial map that specifies how strongly source activity affects the measurements at the sensor level, and (2) its frequency spectrum. We showed that, with this source definition, the array of PAC-measures (wPLFs) can be written as a tensor product of two spatial maps (one complex- and one real-valued) and two frequency spectra (both real-valued), which is exactly the structure that is extracted by our tensor decomposition (see Supplemental Methods). However, we cannot exclude PAC-generating source configurations that cannot be characterized in this way. In fact, we have argued that PAC-patterns may also be generated by a source configuration that would be considered a single source when viewed from the perspective of the mechanism that generates the physiological signal. This confronts us with the problem that sources can be defined both in terms of their formal characteristics (i.e., in terms of a spatial map and a frequency spectrum) and in terms of the neuronal network that generates the physiological signal. The difference between the two definitions is most clear if the physiological mechanism consist of multiple components, such as networks of inhibitory neurons that are connected to one or multiple classes of principal neurons, each with its own network topology. This whole multi-component network may be considered as a single source, but also as multiple sources, each one corresponding to one component. Importantly, if these components differ with respect to their spatial maps and frequency spectra, then they can be extracted by means of tensor decomposition. This shows there may be a need for a linking of the set of extracted source configurations on the basis of the neuronal interactions that may have produced them....In the analysis of the activation period wPLFs, 36 reliable PAC patterns were identified: two subjects had four reliable PAC patterns, eight had two, 12 had one, and four had none. In the analysis of the baseline period wPLFs, 17 reliable PAC patterns were identified: four subjects had two reliable PAC patterns, nine had one, and 13 had none. Reliability was defined in terms of the split-half correlation between two independent estimates of the spatial and the frequency spectra produced by the tensor decomposition. All identified PAC patterns had reliabilities much larger than what can be expected under the hypothesis of a random PAC (see Materials and methods). A representative selection of reliable PAC patterns is shown in Supplemental Fig. 3....Spatially distributed coherent oscillations provide temporal windows of excitability that allow for interactions between distinct neuronal groups. It has been hypothesized that this mechanism for neuronal communication is realized by bursts of high-frequency oscillations that are phase-coupled to a low-frequency spatially distributed coupling oscillation. This mechanism requires multiple physiologically different interacting sources, one generating the low-frequency coupling oscillation and the others generating phase-coupled high-frequency oscillations. Using human intracranial EEG (iEEG) data, we provide evidence for multiple oscillatory patterns, as characterized on the basis of their spatial maps (topographies) and their frequency spectra. In fact, we show that the spatial maps for the coupling oscillations are much more widespread than the ones for the associated phase-coupled bursts. Second, in the majority of the patterns of phase-amplitude coupling (PAC), phase-coupled bursts of high-frequency activity are synchronized across brain areas. Third and last, working memory operations affect the PAC strength in a heterogeneous way: in some PAC patterns, working memory operations increase their strength, whereas in others they decrease it....Simulated field potentials with bursts of gamma oscillations (60Hz) that are phase-coupled to the rising phase of a theta oscillation (5Hz). ...In the analysis of the activation period wPLFs, 36 reliable PAC patterns were identified: two subjects had four reliable PAC patterns, eight had two, 12 had one, and four had none. In the analysis of the baseline period wPLFs, 17 reliable PAC patterns were identified: four subjects had two reliable PAC patterns, nine had one, and 13 had none. Reliability was defined in terms of the split-half correlation between two independent estimates of the spatial and the frequency spectra produced by the tensor decomposition. All identified PAC patterns had reliabilities much larger than what can be expected under the hypothesis of a random PAC (see Materials and methods). A representative selection of reliable PAC patterns is shown in Supplemental Fig. 3....Package of figures showing the spatial maps and the frequency spectra of a representative set of reliable cross-frequency patterns extracted from the activation period wPLFs. The figures belonging to one cross-frequency patterns are each in one folder, of which the name refers to the patient. The files that have freqspectra_and_compass as a part of their name contain a figure of which the left panel show the frequency spectra of a coupling oscillation and the associated phase-coupled bursts, and the right panel shows the preferred phases of the PAC in a compass plots. The files that have _IA_ (instantaneous amplitude) as a part of their name contain figures of the magnitudes of the spatial maps for the phase-coupled bursts. Different figures show different subsets of the electrodes, each from on optimal viewpoint. Electrode subsets on the medial side of the brain are shown from two viewpoints, lateral and occipital. The files that have _AWIP_ (amplitude-weighted instantaneous phase) as a part of their name contain figures of the spatial maps of the coupling oscillation. Again, different figures show different subsets of the electrodes, each from on optimal viewpoint. ...Schematic of the calculation of the weighted phase-locking factor (wPLF). Raw signals from channels i and i' are convolved with a frequency-indexed wavelet, with indices f and f', producing complex-valued signals. With this convolution we estimate the time-varying amplitudes and the phases for the different frequency bins that are provided by our wavelet filter bank. The wPLF is obtained by taking the average over epochs of the inner products (complex covariances) of an amplitude envelope and a wavelet transform. Prior to calculating the inner product, the amplitude envelopes and the wavelet transform are centered (i.e., their mean is subtracted) and normalized (i.e., dividing the signal by its norm, the square-root of the inner product of the signal with its conjugate transpose). The wPLF is a complex association measure, with absolute value between 0 and 1. ...Illustration of a tensor decomposition of the four-dimensional array of weighted phase-locking factors (wPLFs). The two spatial maps and the two frequency spectra (see text) are each denoted by a different color (red, yellow, green, and blue) and a different index (i, i', f, f'). The same colors are used both for the boundaries of the panels and the symbols in the formula for the wPLF. In panels a and b, with red and index i, we show the complex-valued spatial map of the high-frequency bursts that are coupled to a common low-frequency oscillation (the coupling oscillation). In panel a, we show the magnitude (absolute value) of this complex-valued spatial map (one colored circle per channel), which expresses the strength of the coupling. In panel b we show the phases of the coupling oscillation to which the high-frequency bursts are locked (one arrow per channel). In panel c, with yellow and index i', we show the spatial map of the coupling oscillation (one colored circle per channel). The more a coefficient bi’ deviates from zero, the more this channel is affected by the coupling oscillation. In panel d, with green and index f, we show the frequency spectrum of the phase-coupled bursts (shown on a logarithmic scale). In panel e, with blue and index f', we show the frequency spectrum of the coupling oscillation. The spectra shown in panels a, b, c, d and e are all in arbitrary units (a.u.). This is because the spectra are produced by a tensor decomposition which involves an arbitrary multiplicative scaling (see Supplemental Material). In panels f, h, and j, we show the magnitudes of the complex wPLFs for three selected channel pairs (see text). By means of arrows, we connect the channels in panels a and c for which these wPLFs were calculated, with the corresponding x-axes (showing the frequency of the coupling oscillation), respectively, y-axes (showing the frequency of the phase-coupled bursts), in panels f, h, and j. In panels g and i, we show the phases of the complex wPLFs that correspond to panels f and h, respectively. ...Spectral signature of the PAC patterns. Panels a and b show the results for, respectively, the baseline and the activation period. Both panels show a scatter plot of the central frequencies of the phase-coupled bursts (horizontal axis) against the central frequencies of the coupling oscillations (vertical axis). The central frequency of the phase-coupled bursts is always smaller than the one for the associated coupling oscillation. For all PAC patterns above the thick black line, the central frequency for the phase-coupled bursts is larger than the central frequency for the associated coupling oscillation. There is not a dominant frequency, neither for the phase-coupled bursts, nor for the associated coupling frequencies. ...Spectral signature of the PAC patterns. Panels a and b show the results for, respectively, the baseline and the activation period. Both panels show a scatter plot of the central frequencies of the phase-coupled bursts (horizontal axis) against the central frequencies of the coupling oscillations (vertical axis). The central frequency of the phase-coupled bursts is always smaller than the one for the associated coupling oscillation. For all PAC patterns above the thick black line, the central frequency for the phase-coupled bursts is larger than the central frequency for the associated coupling oscillation. There is not a dominant frequency, neither for the phase-coupled bursts, nor for the associated coupling frequencies. ...Spatially distributed coherent oscillations provide temporal windows of excitability that allow for interactions between distinct neuronal groups. It has been hypothesized that this mechanism for neuronal communication is realized by bursts of high-frequency oscillations that are phase-coupled to a low-frequency spatially distributed coupling oscillation. This mechanism requires multiple physiologically different interacting sources, one generating the low-frequency coupling oscillation and the others generating phase-coupled high-frequency oscillations. Using human intracranial EEG (iEEG) data, we provide evidence for multiple oscillatory patterns, as characterized on the basis of their spatial maps (topographies) and their frequency spectra. In fact, we show that the spatial maps for the coupling oscillations are much more widespread than the ones for the associated phase-coupled bursts. Second, in the majority of the patterns of phase-amplitude coupling (PAC), phase-coupled bursts of high-frequency activity are synchronized across brain areas. Third and last, working memory operations affect the PAC strength in a heterogeneous way: in some PAC patterns, working memory operations increase their strength, whereas in others they decrease it....Spatially distributed patterns of oscillatory coupling between high-frequency amplitudes and low-frequency phases in human iEEG ... Spatially distributed coherent oscillations provide temporal windows of excitability that allow for interactions between distinct neuronal groups. It has been hypothesized that this mechanism for neuronal communication is realized by bursts of high-frequency oscillations that are phase-coupled to a low-frequency spatially distributed coupling oscillation. This mechanism requires multiple physiologically different interacting sources, one generating the low-frequency coupling oscillation and the others generating phase-coupled high-frequency oscillations. Using human intracranial EEG (iEEG) data, we provide evidence for multiple oscillatory patterns, as characterized on the basis of their spatial maps (topographies) and their frequency spectra. In fact, we show that the spatial maps for the coupling oscillations are much more widespread than the ones for the associated phase-coupled bursts. Second, in the majority of the patterns of phase-amplitude coupling (PAC), phase-coupled bursts of high-frequency activity are synchronized across brain areas. Third and last, working memory operations affect the PAC strength in a heterogeneous way: in some PAC patterns, working memory operations increase their strength, whereas in others they decrease it.

Contributors: M. Domínguez, J. Pons, J. Ricart, E. Figueras

Date: 2007-05-01

Theory and simulation results of the normalized digital frequency fD as a function of the f0/fS ratio and ρ=0.01. The r and δ values which identify each f0/fS segment are also specified. ...Oscilloscope screen captures of resonator position, input pulses (D6), delayed comparator output (D3) and sample clock (D0), for a PDO topology with m=1 and a ‘not perfect’ frequency fS=46.093kHz (r=2). ...Theory and simulation results of the normalized digital frequency fD as a function of the f0/fS ratio and ρ=0.01. The r and δ values which identify each f0/fS segment are also specified. ...The MEMS pulsed digital oscillator (PDO) below the Nyquist limit...Oscilloscope screen captures of resonator position, input pulses (D6), delayed comparator output (D3) and sample clock (D0), for a PDO topology with m=1 and a ‘not perfect’ frequency fS=46.093kHz (r=2). ...Pulsed digital oscillators, MEMS, Oscillators, Sigma-delta...This paper describes new theoretical and experimental results showing that the pulsed digital oscillator, a set of sigma–delta-based oscillator structures for MEMS recently introduced by the authors, can maintain a good oscillation behaviour even for sampling frequencies below the Nyquist limit. Specifically, the theory is extended to the undersampling region and the complete set of ‘perfect’ frequencies (sampling frequencies at which the oscillation frequency is the natural frequency of the resonator) is analyzed. Therefore, an extension of the use of this kind of oscillators to high frequency applications becomes straightforward....Oscilloscope screen captures of resonator position, input pulses (D6), delayed comparator output (D3 and D1) and sample clock (D0), for a PDO topology with m=2 and the ‘perfect’ frequency fS=44.052kHz (r=2). ...Oscilloscope screen captures of resonator position, input pulses (D6), delayed comparator output (D3 and D1) and sample clock (D0), for a PDO topology with m=2 and the ‘perfect’ frequency fS=44.052kHz (r=2). ...This paper describes new theoretical and experimental results showing that the pulsed digital oscillator, a set of sigma–delta-based oscillator structures for MEMS recently introduced by the authors, can maintain a good oscillation behaviour even for sampling frequencies below the Nyquist limit. Specifically, the theory is extended to the undersampling region and the complete set of ‘perfect’ frequencies (sampling frequencies at which the oscillation frequency is the natural frequency of the resonator) is analyzed. Therefore, an extension of the use of this kind of oscillators to high frequency applications becomes straightforward. ... This paper describes new theoretical and experimental results showing that the pulsed digital oscillator, a set of sigma–delta-based oscillator structures for MEMS recently introduced by the authors, can maintain a good oscillation behaviour even for sampling frequencies below the Nyquist limit. Specifically, the theory is extended to the undersampling region and the complete set of ‘perfect’ frequencies (sampling frequencies at which the oscillation frequency is the natural frequency of the resonator) is analyzed. Therefore, an extension of the use of this kind of oscillators to high frequency applications becomes straightforward.

Contributors: Wei-Bin Yang, Chi-Hsiung Wang, Sheng-Shih Yeh, Chao-Cheng Liao

Date: 2013-08-01

(a) Basic design conception of the wide operation frequency range phase interpolator circuit, and (b) the timing diagram of the DTA phase interpolator with various oscillation frequencies. ...Circuit architecture of the multiple frequency clock generator. ...Simulation output waveforms of the multi-frequency outputs. ...Microphotograph of the multiple frequency clock generator. ...Normalized phase difference error with various oscillation frequencies. ...This paper presents a multiple frequency clock generator that is composed of the wide operation frequency range phase interpolator and the phase combiner. The wide operation frequency range phase interpolator is developed using a delay-time-adjustment phase interpolator (DTAPI) circuit with various oscillation frequencies for different clock domain applications. The phase combiner generates multiple clock frequencies through various phase combination inputs generated by the preceding proposed phase interpolators. The varying output transition delay time of the proposed DTAPI is the result of the various oscillation frequencies of the voltage-controlled oscillator. The test chip was fabricated in a 0.18μm CMOS process with a 1.8V supply voltage. The measured phase noise and power dissipation are −87.28dBc/Hz at 1MHz offset frequency from 88.8MHz and 1.32mW, −77.47dBc/Hz and 2.06mW from 797.8MHz, respectively. The duty cycle error rate of the output clock frequency is less than 1.5%....This paper presents a multiple frequency clock generator that is composed of the wide operation frequency range phase interpolator and the phase combiner. The wide operation frequency range phase interpolator is developed using a delay-time-adjustment phase interpolator (DTAPI) circuit with various oscillation frequencies for different clock domain applications. The phase combiner generates multiple clock frequencies through various phase combination inputs generated by the preceding proposed phase interpolators. The varying output transition delay time of the proposed DTAPI is the result of the various oscillation frequencies of the voltage-controlled oscillator. The test chip was fabricated in a 0.18μm CMOS process with a 1.8V supply voltage. The measured phase noise and power dissipation are −87.28dBc/Hz at 1MHz offset frequency from 88.8MHz and 1.32mW, −77.47dBc/Hz and 2.06mW from 797.8MHz, respectively. The duty cycle error rate of the output clock frequency is less than 1.5%....Multiple frequency...Measured frequency spectra of the output frequency (a) 88.8MHz (=2/3×133MHz) and (b) 797.8MHz (=6/1×133MHz). ...A multiple frequency clock generator using wide operation frequency range phase interpolator...(a) Circuit architecture of the wide operation frequency range phase interpolator, and (b) the operation conception and timing diagram of the wide operation frequency range phase interpolator. ...Voltage-controlled oscillator ... This paper presents a multiple frequency clock generator that is composed of the wide operation frequency range phase interpolator and the phase combiner. The wide operation frequency range phase interpolator is developed using a delay-time-adjustment phase interpolator (DTAPI) circuit with various oscillation frequencies for different clock domain applications. The phase combiner generates multiple clock frequencies through various phase combination inputs generated by the preceding proposed phase interpolators. The varying output transition delay time of the proposed DTAPI is the result of the various oscillation frequencies of the voltage-controlled oscillator. The test chip was fabricated in a 0.18μm CMOS process with a 1.8V supply voltage. The measured phase noise and power dissipation are −87.28dBc/Hz at 1MHz offset frequency from 88.8MHz and 1.32mW, −77.47dBc/Hz and 2.06mW from 797.8MHz, respectively. The duty cycle error rate of the output clock frequency is less than 1.5%.

Contributors: J.C. Cao, X.L. Lei, H.C. Liu

Date: 2003-11-01

Self-oscillating frequencies of the p+pp+ NEM diodes at T=77 K with four doping concentrations indicated by the arrows in Fig. 2(a): (a) Na=7×1016 cm−3 and (b) Na=4.5×1017 cm−3, respectively. ...Terahertz self-oscillations in negative-effective-mass oscillators...We have theoretically investigated spatiotemporal current patterns and self-oscillating characteristics of negative-effective-mass (NEM) p+pp+ diodes. Periodically oscillating current densities are presented as a gray density plot, showing rich patterns with the applied bias and doping concentration as controlling parameters. Such a pattern arises in the NEM p-base with a “N-shaped” velocity–field relation. Two-dimensional applied bias-doping concentration phase diagrams at different lattice temperatures are calculated in order to visualize the effect of lattice temperature on the self-oscillating regions. It is indicated that both the applied bias and the doping concentration strongly influence the patterns and self-oscillating frequencies, which are generally in the terahertz (THz) frequency band. The NEM p+pp+ diode may therefore be used to develop a electrically tunable THz-frequency oscillator....Self-oscillation...Time-periodic self-oscillating current densities for the p+pp+ NEM diodes at T=77 K and doping concentrations Na=7×1016 cm−3 are shown as a density gray plot, where lighter areas correspond to larger amplitudes of the current densities. ...We have theoretically investigated spatiotemporal current patterns and self-oscillating characteristics of negative-effective-mass (NEM) p+pp+ diodes. Periodically oscillating current densities are presented as a gray density plot, showing rich patterns with the applied bias and doping concentration as controlling parameters. Such a pattern arises in the NEM p-base with a “N-shaped” velocity–field relation. Two-dimensional applied bias-doping concentration phase diagrams at different lattice temperatures are calculated in order to visualize the effect of lattice temperature on the self-oscillating regions. It is indicated that both the applied bias and the doping concentration strongly influence the patterns and self-oscillating frequencies, which are generally in the terahertz (THz) frequency band. The NEM p+pp+ diode may therefore be used to develop a electrically tunable THz-frequency oscillator....Self-oscillating frequencies of the p+pp+ NEM diodes at T=77 K with four doping concentrations indicated by the arrows in Fig. 2(a): (a) Na=7×1016 cm−3 and (b) Na=4.5×1017 cm−3, respectively. ...Applied bias-doping concentration phase diagrams of current self-oscillations in the p+pp+ NEM diodes at four lattice temperatures: (a) T=77 K, (b) 145 K, (c) 180 K, and (d) 220 K, respectively. Throughout the paper, the p-base length is set to be l=0.3μm, and the doping concentration in the contact p+-region is assumed to be 2×1018 cm−3. ... We have theoretically investigated spatiotemporal current patterns and self-oscillating characteristics of negative-effective-mass (NEM) p+pp+ diodes. Periodically oscillating current densities are presented as a gray density plot, showing rich patterns with the applied bias and doping concentration as controlling parameters. Such a pattern arises in the NEM p-base with a “N-shaped” velocity–field relation. Two-dimensional applied bias-doping concentration phase diagrams at different lattice temperatures are calculated in order to visualize the effect of lattice temperature on the self-oscillating regions. It is indicated that both the applied bias and the doping concentration strongly influence the patterns and self-oscillating frequencies, which are generally in the terahertz (THz) frequency band. The NEM p+pp+ diode may therefore be used to develop a electrically tunable THz-frequency oscillator.

Contributors: Ting Wang, Xiaoguang Wang, Zhe Sun

Date: 2007-09-15

The thermal concurrences between qubits 1 and 2 in the 2–6-qubit spin-half open Heisenberg chains. ...We study pairwise entanglements in spin-half and spin-one Heisenberg chains with an open boundary condition, respectively. We find out that the ground-state and the first-excited-state entanglements are equal for the three-site spin-one chain. When the number of sites L>3, the concurrences and negativities display oscillatory behaviors, and the oscillations of the ground-state and the first-excited-state entanglements are out of phase or in phase....The thermal concurrences between qubits 1 and 2 in the 2–6-qubit spin-half open Heisenberg chains. ... We study pairwise entanglements in spin-half and spin-one Heisenberg chains with an open boundary condition, respectively. We find out that the ground-state and the first-excited-state entanglements are equal for the three-site spin-one chain. When the number of sites L>3, the concurrences and negativities display oscillatory behaviors, and the oscillations of the ground-state and the first-excited-state entanglements are out of phase or in phase.

Contributors: Li Wang, Qingmei Kong, Ke Li, Yunai Su, Yawei Zeng, Qinge Zhang, Wenji Dai, Mingrui Xia, Gang Wang, Zhen Jin

Date: 2016-02-12

Brain regions showing significant group and frequency (slow-4 and slow-5) interaction effects on ALFF. ...Brain regions showing significant group and frequency (slow-4 and slow-5) interaction effects on ALFF. ...The group and frequency (slow-4 and slow-5) interaction effects on ALFF. The regions showing significant group and frequency interaction effects on ALFF (hot colors): the left ventromedial prefrontal cortex (a), the left inferior frontal gyrus/precentral gyrus (b), and the bilateral posterior cingulate cortex/precuneus (c). The bar maps show the mean ALFF values for these regions. ...Main effects of group and frequency on ALFF. (a) The group main effects on ALFF. Hot colors represent increased ALFF in the MDD group compared with HC, while the blue colors represent the opposite. (b) Frequency main effects on ALFF. Hot colors represent increased ALFF in the slow-5 as compared to slow-4 band, while the blue colors represent the opposite. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) ...Low-frequency oscillation...Research paper - Frequency-dependent changes in amplitude of low-frequency oscillations in depression: A resting-state fMRI study...Low-frequency oscillation...Frequency dependence...We conducted this fMRI study to examine whether the alterations in amplitudes of low-frequency oscillation (LFO) of major depressive disorder (MDD) patients were frequency dependent....Main effects of group and frequency on ALFF. (a) The group main effects on ALFF. Hot colors represent increased ALFF in the MDD group compared with HC, while the blue colors represent the opposite. (b) Frequency main effects on ALFF. Hot colors represent increased ALFF in the slow-5 as compared to slow-4 band, while the blue colors represent the opposite. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) ...We conducted this fMRI study to examine whether the alterations in amplitudes of low-frequency oscillation (LFO) of major depressive disorder (MDD) patients were frequency dependent....The group and frequency (slow-4 and slow-5) interaction effects on ALFF. The regions showing significant group and frequency interaction effects on ALFF (hot colors): the left ventromedial prefrontal cortex (a), the left inferior frontal gyrus/precentral gyrus (b), and the bilateral posterior cingulate cortex/precuneus (c). The bar maps show the mean ALFF values for these regions. ... We conducted this fMRI study to examine whether the alterations in amplitudes of low-frequency oscillation (LFO) of major depressive disorder (MDD) patients were frequency dependent.

Contributors: Ole Schmitz, Joergen Rungby, Linda Edge, Claus B. Juhl

Date: 2008-12-01

Insulin is released in a pulsatile manner, which results in oscillatory concentrations in blood. The oscillatory secretion improves release control and enhances the hormonal action. Insulin oscillates with a slow ultradian periodicity (∼140min) and a high-frequency periodicity (∼6–10min). Only the latter is reviewed in this article. At least 75% of the insulin secretion is released in a pulsatile manner. Individuals prone to developing diabetes or with overt type 2 diabetes are characterized by irregular oscillations of plasma insulin. Many factors have impact on insulin pulsatility such as age, insulin resistance and glycemic level. In addition, tiny glucose oscillations are capable of entraining insulin oscillations in healthy people in contrast to type 2 diabetic individuals emphasizing a profound disruption of the beta-cells in type 2 diabetes to sense or respond to physiological glucose excursions. A crucial question is how ∼1,000,000 islets, each containing from a few to several thousand beta-cells, can be coordinated to secrete insulin in a pulsatile manner. This is blatantly a very complex operation to control involving an intra-pancreatic neural network, an intra-islet communication and metabolic oscillations in the beta-cell itself. Overnight beta-cell rest, e.g. during somatostatin administration, improves the disordered pulsatile insulin secretion in type 2 diabetes. Acute as well as long-term administration of sulphonylureas (SU) leads to substantial amplification (∼50%) of the pulsatile insulin secretion in type 2 diabetes. This is probably cardinal in terms of governing the hepatic glucose release in type 2 diabetes. Whether sulfonylureas also improve the ability of the beta-cells to sense glucose fluctuations remains to be explored. Thiazolidinediones reduce the pulsatile insulin secretion without affecting regularity, but appear to improve the ability of the beta-cell to be entrained by small glucose excursions. Finally, similar to SUs, the incretin hormone GLP-1 also results in an augmented pulsatile burst mass in both healthy and diabetic individuals, in the latter group, however, without influencing the disorderliness of pulses. This review will briefly describe the high-frequency insulin pulsatility during physiologic and pathophysiologic conditions as well as the influence of some hypoglycemic compounds on the insulin oscillations....(A) Schematic illustration of deconvolution analysis based on insulin measurements. The upper panel shows the measured concentration profile and the best-fit line. The lower panel shows the calculated insulin secretory rates and the derived variables (1) insulin secretory burst mass, (2) burst amplitude, (3) basal (not-pulsatile) insulin secretion and (4) interpulse interval. (B) Representative insulin concentration and calculated secretion profile analyzed by fitting basal insulin concentration employing deconvolution. Note the high frequency (adapted from ref. [5]). ...Review - On high-frequency insulin oscillations...Insulin is released in a pulsatile manner, which results in oscillatory concentrations in blood. The oscillatory secretion improves release control and enhances the hormonal action. Insulin oscillates with a slow ultradian periodicity (∼140min) and a high-frequency periodicity (∼6–10min). Only the latter is reviewed in this article. At least 75% of the insulin secretion is released in a pulsatile manner. Individuals prone to developing diabetes or with overt type 2 diabetes are characterized by irregular oscillations of plasma insulin. Many factors have impact on insulin pulsatility such as age, insulin resistance and glycemic level. In addition, tiny glucose oscillations are capable of entraining insulin oscillations in healthy people in contrast to type 2 diabetic individuals emphasizing a profound disruption of the beta-cells in type 2 diabetes to sense or respond to physiological glucose excursions. A crucial question is how ∼1,000,000 islets, each containing from a few to several thousand beta-cells, can be coordinated to secrete insulin in a pulsatile manner. This is blatantly a very complex operation to control involving an intra-pancreatic neural network, an intra-islet communication and metabolic oscillations in the beta-cell itself. Overnight beta-cell rest, e.g. during somatostatin administration, improves the disordered pulsatile insulin secretion in type 2 diabetes. Acute as well as long-term administration of sulphonylureas (SU) leads to substantial amplification (∼50%) of the pulsatile insulin secretion in type 2 diabetes. This is probably cardinal in terms of governing the hepatic glucose release in type 2 diabetes. Whether sulfonylureas also improve the ability of the beta-cells to sense glucose fluctuations remains to be explored. Thiazolidinediones reduce the pulsatile insulin secretion without affecting regularity, but appear to improve the ability of the beta-cell to be entrained by small glucose excursions. Finally, similar to SUs, the incretin hormone GLP-1 also results in an augmented pulsatile burst mass in both healthy and diabetic individuals, in the latter group, however, without influencing the disorderliness of pulses. This review will briefly describe the high-frequency insulin pulsatility during physiologic and pathophysiologic conditions as well as the influence of some hypoglycemic compounds on the insulin oscillations....(A) Schematic illustration of deconvolution analysis based on insulin measurements. The upper panel shows the measured concentration profile and the best-fit line. The lower panel shows the calculated insulin secretory rates and the derived variables (1) insulin secretory burst mass, (2) burst amplitude, (3) basal (not-pulsatile) insulin secretion and (4) interpulse interval. (B) Representative insulin concentration and calculated secretion profile analyzed by fitting basal insulin concentration employing deconvolution. Note the high frequency (adapted from ref. [5]). ... Insulin is released in a pulsatile manner, which results in oscillatory concentrations in blood. The oscillatory secretion improves release control and enhances the hormonal action. Insulin oscillates with a slow ultradian periodicity (∼140min) and a high-frequency periodicity (∼6–10min). Only the latter is reviewed in this article. At least 75% of the insulin secretion is released in a pulsatile manner. Individuals prone to developing diabetes or with overt type 2 diabetes are characterized by irregular oscillations of plasma insulin. Many factors have impact on insulin pulsatility such as age, insulin resistance and glycemic level. In addition, tiny glucose oscillations are capable of entraining insulin oscillations in healthy people in contrast to type 2 diabetic individuals emphasizing a profound disruption of the beta-cells in type 2 diabetes to sense or respond to physiological glucose excursions. A crucial question is how ∼1,000,000 islets, each containing from a few to several thousand beta-cells, can be coordinated to secrete insulin in a pulsatile manner. This is blatantly a very complex operation to control involving an intra-pancreatic neural network, an intra-islet communication and metabolic oscillations in the beta-cell itself. Overnight beta-cell rest, e.g. during somatostatin administration, improves the disordered pulsatile insulin secretion in type 2 diabetes. Acute as well as long-term administration of sulphonylureas (SU) leads to substantial amplification (∼50%) of the pulsatile insulin secretion in type 2 diabetes. This is probably cardinal in terms of governing the hepatic glucose release in type 2 diabetes. Whether sulfonylureas also improve the ability of the beta-cells to sense glucose fluctuations remains to be explored. Thiazolidinediones reduce the pulsatile insulin secretion without affecting regularity, but appear to improve the ability of the beta-cell to be entrained by small glucose excursions. Finally, similar to SUs, the incretin hormone GLP-1 also results in an augmented pulsatile burst mass in both healthy and diabetic individuals, in the latter group, however, without influencing the disorderliness of pulses. This review will briefly describe the high-frequency insulin pulsatility during physiologic and pathophysiologic conditions as well as the influence of some hypoglycemic compounds on the insulin oscillations.