Trapped-ions form a promising platform to realize a future large scale quantum computing device. Qubits are typically stored in internal electronic states, which are coupled using their joint motion in the trap potential. In this thesis this control paradigm is reversed. The harmonic motion of a trapped calcium ion forms the main subject of studies, which is controlled via the internal electronic states. A number of new techniques are introduced and examined, primarily based on the implementation of modular variable measurements. These are realized combining an internal state dependent optical dipole force with readout of the internal states. Modular measurements are used to investigate large "Schrödinger cat'' states of the ion's motion, to violate Leggett-Garg tests of macroscopic realism, and finally to realize a logical qubit encoded in an error-correcting code based on the trapped-ion oscillator. The latter offers an alternative to the standard qubit based quantum information processing approach, which when embedded in systems of coupled oscillators could lead to a large-scale quantum computer. Measurements of a particle's modular position and momentum have been the focus of various discussions of foundational quantum mechanics. Such modular measurements of the trapped-ion's motion are studied in depth in this thesis, in particular their ability to commute, which forms a key element for the latter work on error-correcting codes. Here we make use of the ability to investigate sequences of measurements on a single harmonic oscillator, and study correlations between their results, as well as quantum measurement disturbances between the measurements. In order to achieve the major results of the thesis, it was necessary to characterize and control multiple wave packets in phase space. On the characterization side, the need to cope with states with high energy occupations led to the development of multiple new methods for quantum state tomography, including the use of a squeezed eigenstate basis, and the direct extraction of the characteristic function of the oscillator using state-dependent forces. These were used to analyze some of the largest oscillator "Schrödinger cat'' states which have been produced to date. The main result of this thesis is encoding and full control of a logical qubit in the motional oscillator space using a code proposed 18 years ago by Gottesman, Kitaev and Preskill. Logical code states are realized and manipulated using sequences of up to five modular measurements applied to an ion initially prepared in a squeezed motional state. Such sequences realize superpositions of multiple squeezed wave packets, which form the code words. The usage of the oscillator enables to encode and in principle correct a logical qubit within a single trapped ion, which when compared to typical qubit-array based approaches simplifies control and hardware. While the discussion above focuses on the new physics in this thesis, in addition the work required technical upgrades to the system, improving control of both qubit and oscillator. These form important components which have impact on all experiments in our setup, beyond the bounds of the current thesis.,ISBN:5800134927809,
Contributors:Scarlino, Pasquale, Van Woerkom, David J., Mendes, Udson C., Koski, Jonne V., Landig, Andreas J., Andersen, Christian K., Gasparinetti, Simone, Reichl, Christian, Wegscheider, Werner, Ensslin, Klaus, Ihn, Thomas, Blais, Alexandre, Wallraff, Andreas
Semiconductor qubits rely on the control of charge and spin degrees of freedom of electrons or holes confined in quantum dots. They constitute a promising approach to quantum information processing, complementary to superconducting qubits. Here, we demonstrate coherent coupling between a superconducting transmon qubit and a semiconductor double quantum dot (DQD) charge qubit mediated by virtual microwave photon excitations in a tunable high-impedance SQUID array resonator acting as a quantum bus. The transmon-charge qubit coherent coupling rate (~21 MHz) exceeds the linewidth of both the transmon (~0.8 MHz) and the DQD charge qubit (~2.7 MHz). By tuning the qubits into resonance for a controlled amount of time, we observe coherent oscillations between the constituents of this hybrid quantum system. These results enable a new class of experiments exploring the use of two-qubit interactions mediated by microwave photons to create entangled states between semiconductor and superconducting qubits.,Nature Communications, 10 (1),
Contributors:Tigges, Marcel, Dénervaud, Nicolas, Greber, David, Stelling, Jörg, Fussenegger, Martin
Circadian clocks have long been known to be essential for the maintenance of physiological and behavioral processes in a variety of organisms ranging from plants to humans. Dysfunctions that subvert gene expression of oscillatory circadian-clock components may result in severe pathologies, including tumors and metabolic disorders. While the underlying molecular mechanisms and dynamics of complex gene behavior are not fully understood, synthetic approaches have provided substantial insight into the operation of complex control circuits, including that of oscillatory networks. Using iterative cycles of mathematical model-guided design and experimental analyses, we have developed a novel low-frequency mammalian oscillator. It incorporates intronically encoded siRNA-based silencing of the tetracycline-dependent transactivator to enable the autonomous and robust expression of a fluorescent transgene with periods of 26 h, a circadian clock-like oscillatory behavior. Using fluorescence-based time-lapse microscopy of engineered CHO-K1 cells, we profiled expression dynamics of a destabilized yellow fluorescent protein variant in single cells and real time. The novel oscillator design may enable further insights into the system dynamics of natural periodic processes as well as into siRNA-mediated transcription silencing. It may foster advances in design, analysis and application of complex synthetic systems in future gene therapy initiatives.,Nucleic acids research, 38 (8),
Contributors:Howan Leung, Cannon X.L. Zhu, Danny T.M. Chan, Wai S. Poon, Lin Shi, Vincent C.T. Mok, Lawrence K.S. Wong
High-frequencyoscillations... An example of the implantation schedule (patient #1) demonstrating areas with conventional frequency ictal patterns, ictal high-frequencyoscillations, hyperexcitability, and radiological lesions.
... An example of the implantation schedule (patient #7) demonstrating areas with conventional frequency ictal patterns, ictal high-frequencyoscillations, hyperexcitability, and radiological lesions.
... High-frequencyoscillations (HFOs, 80–500Hz) from intracranial electroencephalography (EEG) may represent a biomarker of epileptogenicity for epilepsy. We explored the relationship between ictal HFOs and hyperexcitability with a view to improving surgical outcome.... Summary table for statistical analysis. HFO=high frequencyoscillations, CFIP=conventional frequency ictal patterns.
Contributors:Iori Ito, Maxim Bazhenov, Rose Chik-ying Ong, Baranidharan Raman, Mark Stopfer
Odor Concentration Determines Oscillation Coherence, Not Frequency
(A) EAG traces revealed total ORN output increased with odor concentration. Example from one antenna; horizontal bar: 4 s.
(B) Summary. EAG amplitude (first 1 s, see bracket in A) evoked by a range of odor concentrations. Mean ± SE; n = 8; two-way ANOVA: fodor_concentration = 16.84, p oscillations. Initial portions of the odor response are shown. Scale bar: 50 ms.
(D) The frequency of fast oscillation changed not at all or only slightly across a broad range of odor concentrations. All results are shown (dots); bar graph shows means, n = 9. Leftmost bars: basal oscillatory power in absence of odorant. Hexanol: two-way ANOVA: fhexanol_concentration = 6.16, p 0.25, ns.
... Odors Evoked LFP Oscillations in the Moth MB and AL
(A) Recording site for LFP: center of the calyx in the MB. MB, mushroom body; mnsc, medial neurosecretory cells; OL, optic lobe; AL, antennal lobe.
(B) LFP oscillations (black traces) with simultaneously recorded electroantennogram (EAG, green traces) evoked by different pulse durations of 1% benzyl alcohol, a plant volatile. Black horizontal bars: odor pulses. Color bars: time windows (500 ms) used to calculate the power spectra in (D).
(C) Brief odor pulses evoked fast oscillations; lengthy pulses evoked first fast, then slow oscillations. Normalized, average spectrograms from 18 trials obtained from six animals with three trials each (see Experimental Procedures). Black horizontal bars above each spectrogram: odor pulses.
(D) Power spectra of oscillatory LFP responses averaged from 22 moths and eight odors, total of 820 trials. Color brackets: 14 Hz-wide bands used to calculate the total oscillatory powers of fast (red, 30–44 Hz) and slow (blue, 10–24 Hz) oscillations in (E).
(E) Total oscillatory power of fast and slow LFP shifted significantly over lengthy odor pulses. Twenty trials tested for each odor were averaged before pooling, mean ± SE, n = 41; two-way ANOVA: fwindow(2) = 26.62, p oscillations); fwindow(2) = 9.09, p oscillations). Asterisks: significant differences (Tukey-Kramer multiple comparisons).
(F) LFP oscillations in the AL and MB were highly coherent. (Left) Example of odor-evoked LFP oscillations recorded simultaneously in the AL and MB; odorant: 1% cyclohexanone (4 s). Areas a and b are expanded in insets. Horizontal red (0.25–1 s) and blue (1–4 s) bars: times used for coherence analysis at right. (Right) Magnitude squared coherence between the AL and MB. Thin black line: coherence of the response shown. Thick black and dotted lines: average coherence and its one standard deviation range (five AL-MB combinations in four preparations, 20 trials each of two odorants), respectively.
... PN and LN Responses Were Strongly Phase Locked to the LFP
(A) Example simultaneous intracellular recordings from PN and LN, with LFP recorded in the MB. First 2 s after the odor onset shown; brackets: portions expanded beneath. Odorant: 1% benzyl alcohol.
(B) Subthreshold oscillations: five-trial average sliding window cross-correlograms show reliable LFP and subthreshold membrane potential oscillations for the PN (top) and LN (bottom) in (A). Spikes were clipped. Vertical bars: odor pulses.
(C) Spike-LFP phase relationships: Polar histograms show phase position, relative to LFP, of spikes recorded in PNs (n = 14) and LNs (n = 30) for fast and slow oscillations. Concentric circles: firing probability. Black arrows: mean direction.
(D) All recorded neurons were filled with dye and later morphologically identified. Example of PN and LN morphology. An Alexa Fluor-633 (red) filled PN and an Alexa Fluor-568 (yellow) filled LN are shown. Scale bar: 50 μm. AN: antennal nerve.
... In many species, sensory stimuli elicit the oscillatory synchronization of groups of neurons. What determines the properties of these oscillations? In the olfactory system of the moth, we found that odors elicited oscillatory synchronization through a neural mechanism like that described in locust and Drosophila. During responses to long odor pulses, oscillations suddenly slowed as net olfactory receptor neuron (ORN) output decreased; thus, stimulus intensity appeared to determine oscillationfrequency. However, changing the concentration of the odor had little effect upon oscillatory frequency. Our recordings in vivo and computational models based on these results suggested that the main effect of increasing odor concentration was to recruit additional, less well-tuned ORNs whose firing rates were tightly constrained by adaptation and saturation. Thus, in the periphery, concentration is encoded mainly by the size of the responsive ORN population, and oscillationfrequency is set by the adaptation and saturation of this response.... Spiking in KCs Is Sparse, Odor Specific, and Tightly Phase Locked to the LFP
(A) KCs showed odor-elicited subthreshold membrane potential fluctuations that were tightly correlated with LFP oscillations. Example: top, gray: LFP; bottom, black: simultaneous intracellular record of a KC. Bottom: details of fast and slow periods during oscillatory response. Odor: 4 s, 1% benzyl alcohol. Gray broken line: resting potential.
(B) Cross-correlations between LFP oscillations and KC subthreshold activity. Cross-correlation was calculated for times bracketed in (A). Black lines: correlation for the trial shown in (A); gray lines: 21 other trials from this cell. All eight KCs showing subthreshold oscillations revealed similarly shaped correlation functions, three with coefficients >0.3.
(C) Polar histograms show strong phase locking between spikes in KCs and the LFP oscillations. Histograms show spikes recorded from 20 KCs during fast and slow oscillations. Arrows: mean phase position.
(D) Example of KC morphology; posterior view of MB; KC filled with Alexa Fluor-633. Scale bar: 50 μm. Arrow: soma; CaM: medial calyx; CaL: lateral calyx.
... Odor-Evoked Oscillations in Model of Moth AL
(A) Full-scale, map-based model included randomly connected populations of 820 PNs and 360 LNs. Odor pulse input was simulated by external currents delivered to a subset of neurons.
(B) Amplitude of the input was set to resemble the EAG (bottom). LFP (top) and neuronal (middle) responses resembled those recorded in vivo. The input to the model was tuned to match results of our physiological recordings and corresponded to points “1” and “2” in the parameter space shown in (E).
(C) Raster plots show spikes in all PNs (top) and all LNs (bottom) evoked by one odor pulse (applied from 500 to 2500 ms).
(D) Interspike interval (ISI) distributions during fast and slow phases of LFP oscillations. Many PNs fired two spikes in a single oscillatory cycle (ISI frequency was typically limited to the LFP frequency.
(E) Frequency of LFP, PN, and LN oscillations as a function of input from ORNs to PNs and LNs. Sweeping the points between “1” and “2” in parameter space mimicked the changes in the ISI distribution (compare D and Figure S7) and the abrupt change in oscillatory frequency (compare B and the Figure 1C) we observed in vivo.
Contributors:Li Wang, Qingmei Kong, Ke Li, Yunai Su, Yawei Zeng, Qinge Zhang, Wenji Dai, Mingrui Xia, Gang Wang, Zhen Jin, Xin Yu, Tianmei Si
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.
... Low-frequencyoscillation... Frequency dependence... We conducted this fMRI study to examine whether the alterations in amplitudes of low-frequencyoscillation (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.)
Contributors:Eric Maris, Marieke van Vugt, Michael Kahana
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.... 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.... 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.
... 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-frequencyoscillation (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.
... 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-frequencyoscillations 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-frequencyoscillations. 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).
... 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.
Contributors:Bassam V. Atallah, Massimo Scanziani
Excitation Balanced by Proportional Inhibition during Gamma Oscillations In Vivo
(A) Whole-cell recording of EPSCs in CA3 cell (red) and simultaneously recorded LFP (black, positivity is up) during gamma oscillations in anesthetized rat. IPSCs (cyan) and inverted LFP recorded from the same cell. Note correlated fluctuations in the amplitude of LFP and synaptic currents.
(B) Coherence between LFP and IPSCs (cyan) or EPSC (red); jack-knifed 95% confidence interval (thin lines); arrows mark peak coherence frequencies. Summary of peak coherence frequency (bottom) and peak coherence (right). Average shown as a vertical or horizontal bar (n = 7 cells).
(C) Oscillation triggered average (OTA) of EPSC (red), IPSC (cyan) and LFP. LFP was recorded simultaneously with EPSCs, IPSC (black and dotted traces, respectively). EPSC is inverted for illustration purposes. Overlaid POTH (green, data from Figure 1D, aligned to the LFP also in green) illustrates spike timing during oscillation cycle. Note that maximal spiking precedes peak of inhibition. (Bottom) Summary of EPSC-IPSC lag during an oscillation cycle; vertical bar is average.
(D) OTA of EPSCs (red), IPSCs (cyan) computed for four different bins of LFP oscillation amplitude (black; dotted and solid traces were recorded simultaneously with IPSC and EPSCs, respectively, same cell as A–C).
(E) Summary of correlation between average inhibitory (gI) and excitatory (gE) conductance in vivo; individual cells are each represented by a different color linear regression. Note, although excitation and inhibition are proportional, the inhibitory conductance is approximately five times larger (dotted line is at unity).
(F) OTA of IPSC (middle) computed for four different bins of LFP oscillation interevent interval (top). Vertical arrows illustrate IPSC amplitude and horizontal arrows the correlated changes in the time to the next oscillation event (IEI). (Bottom) IPSC amplitude during an oscillation event correlated with the time to the next oscillation in the LFP (IEI); blue dots correspond to the four OTA shown above.
... Gamma Oscillation Amplitude Predicts Latency to Next Oscillation Cycle
(A) (Top) Broadband (gray) and gamma-band filtered local field potential (LFP, 5–100 Hz) recorded in the stratum radiatum of area CA3 of an anesthetized rat. Raster plot marks the peak of each oscillation cycle. (Bottom, left) Autocorrelation of LFP and power spectral density of gamma-band LFP. (Bottom, right) Histograms of oscillation amplitude and interevent interval (IEI). (Inset) LFP recording illustrating the measurement of peak-to-peak amplitude and IEI (expansion of the recording marked by a horizontal bracket in the top panel). Positivity is up.
(B) (Top) IEI correlated against amplitude of the previous cycle illustrated in histogram. Note the correlation between oscillation amplitude and IEI. (Bottom) Summary of correlations, n = 6 rats. Vertical bar is average.
(C) Broadband extracellular recording (top), gamma-band LFP (middle, 5–100 Hz band-pass), multiunit spiking (green, 0.2–2 kHz) from stratum pyramidale of area CA3. Negativity is up.
(D) Oscillation triggered average of LFP, peri-oscillation spike-time histogram (POTH), and local linear fit to POTH (green).
(E) (Left) Average LFP and POTH fit calculated separately for large (mean amplitude = 313 μV) and small (99 μV, dotted) oscillation cycles. Arrows illustrate the increased latency between spiking events after large-amplitude cycles. (Inset) Small POTH scaled to the peak of the large POTH. (Right) Summary of full-width at half-maximum (FWHM) of POTH for large (solid) and small (open) oscillation cycles (n = 6 rats). Averages are illustrated with horizontal bars. Note that spiking occurs in a narrow time window during each oscillation cycle independent of oscillation amplitude.
... Larger, Longer Hyperpolarization of Pyramidal Cells following Large-Amplitude Oscillation Cycles
(A) LFP and simultaneously recorded membrane potential (Vm; whole-cell current-clamp configuration: IC) during in vitro gamma oscillations (dotted line is mean Vm). Positivity is up.
(B) Oscillation cycles were binned according to LFP amplitude and the oscillation triggered average (OTA) of Vm computed for each bin (different colors): average time course of LFP in four bins of increasing amplitude (top) and corresponding (color coded) Vm averages (middle). Note that Vm undergoes larger and longer hyperpolarization during large-amplitude oscillation cycles. (Bottom, left) Cycle-by-cycle correlation between the peak hyperpolarization and LFP amplitude. Bins in upper panels are illustrated with solid dots of respective colors. (Bottom, right) Summary of correlation (n = 11 cells).
(C) (Top) Oscillation cycles were binned according to LFP interevent interval and the OTA of membrane potential computed for each bin (different cell than A and B). Arrows illustrate “recovery time,” i.e., time from onset of oscillation cycle till membrane potential recovers to mean Vm (horizontal dotted line). (Bottom) LFP interevent interval plotted as a function of Vm recovery time. Colored dots and black line correspond to the above cell, other cells shown in gray. Note, mean slope, m = 1.16; SD = 0.3, suggesting that changes in the time for recovery from hyperpolarization in individual cells can account for the entire range of oscillation intervals observed in the LFP.
... Excitation Instantaneously Balanced by Proportional Inhibition during Each Gamma Oscillation Cycle
(A) (Top) Broadband (gray) and gamma-band filtered (black) LFP recorded in the stratum radiatum of area CA3 in acute hippocampal slice. Raster plot marks the peak of each oscillation cycle. (Bottom, left) Autocorrelation of LFP and power spectral density of gamma-band LFP. (Bottom, right) Histograms of oscillation amplitude and interevent interval (IEI). (Inset) LFP recording illustrating the measurement of peak-to-peak amplitude and IEI (expansion of the recording marked by a horizontal bracket in the top panel). Positivity is up.
(B) (Top) IEI correlated against amplitude of the previous cycle. (Bottom) Summary of correlations, n = 6 slices. Vertical bar is the average. Note the correlation between oscillation amplitude and IEI.
(C) Dual patch-clamp recording from two neighboring CA3 pyramidal cells. Oscillations are monitored with an LFP electrode (black, positivity is up). EPSCs (red) and IPSCs (cyan) simultaneously recorded by holding two cells at the reversal potential for inhibition (−3 mV) and excitation (−87 mV), respectively. Note the correlated fluctuations in the amplitude of excitation and inhibition.
(D) (Left) Average time course of EPSC and IPSC (same cell as C) during an oscillation cycle recorded in the LFP, i.e., oscillation triggered average. EPSC is inverted for illustration purposes. LFPs recorded simultaneously with EPSCs and IPSCs are shown as black and gray traces, respectively. (Right) Summary of EPSC-IPSC lag during an oscillation cycle. Horizontal bar is the average.
(E) (Top) Cycle-by-cycle correlation between excitatory and inhibitory conductances recorded in the pair shown in (C). Summary of correlation between excitation and inhibition (bottom) and ratio of mean excitatory and inhibitory conductances (right) (n = 8 pairs). Vertical and horizontal bars illustrate respective averages.
... Correlated Amplitude and Frequency in Simple Model of CA3 Circuit
(A) Average excitatory (gE, red) and inhibitory (gI, cyan) synaptic conductance received by model pyramidal cells. LFP (black) is approximated as the sum of the two conductances.
(B) (Top) Autocorrelation and power spectrum of simulated LFP. (Bottom) Interevent interval correlated against amplitude of the previous cycle.
(C) The membrane potential (Vm) of an individual pyramidal cell in modeled circuit (spike truncated), gE (red) and gI (cyan); dotted line illustrates the average Vm.
(D) Oscillation cycles were binned according to gI amplitude and the oscillation triggered amplitude of Vm computed for each bin (different colors): average time course of gI in four bins of increasing amplitude (middle) and corresponding (color coded) Vm averages (top). The arrows illustrate that it takes longer for Vm to recover to the average potential (horizontal dotted line) after large-amplitude cycles. (Bottom) Cycle-by-cycle correlation between Vm hyperpolarization and the gI. Bins in upper panels are illustrated with solid dots of respective colors.
... Neurons recruited for local computations exhibit rhythmic activity at gamma frequencies. The amplitude and frequency of these oscillations are continuously modulated depending on stimulus and behavioral state. This modulation is believed to crucially control information flow across cortical areas. Here we report that in the rat hippocampus gamma oscillation amplitude and frequency vary rapidly, from one cycle to the next. Strikingly, the amplitude of one oscillation predicts the interval to the next. Using in vivo and in vitro whole-cell recordings, we identify the underlying mechanism. We show that cycle-by-cycle fluctuations in amplitude reflect changes in synaptic excitation spanning over an order of magnitude. Despite these rapid variations, synaptic excitation is immediately and proportionally counterbalanced by inhibition. These rapid adjustments in inhibition instantaneously modulate oscillationfrequency. So, by rapidly balancing excitation with inhibition, the hippocampal network is able to swiftly modulate gamma oscillations over a wide band of frequencies.
Contributors:Maeike Zijlmans, Julia Jacobs, Rina Zelmann, François Dubeau, Jean Gotman
High frequencyoscillations (HFOs) have been associated with epileptogenicity. In rats, the extent of HFOs (>200Hz) is correlated with seizure frequency. We studied whether the same applies to patients with focal epilepsy. Thirty-nine patients with intracerebral EEG sampled at 2000Hz were studied for interictal ripples (80–250Hz), fast ripples (FR, 250–500Hz) and spikes. Seizure frequency before implantation was compared to numbers of channels with HFOs (>1/min). Analyses were repeated for HFO rates of >5, >10 and >20. Separate analyses were done for 25 patients with temporal lobe epilepsy only and for a selection of similar unilateral temporal channels in 12 patients. No linear correlation or trend was found relating the number of channels with HFOs and seizure frequency. There was a linear positive correlation between the number of channels with more than 20 FRs/min and seizure frequency. The hypothesis that the more tissue generating HFOs, the higher the seizure frequency, was not confirmed, though there might be a correlation for high FR rates.... The relation between seizure frequency per month and number of channels with (A) ripples (>1/min), (B) fast ripples (>1/min), and (C) more than 20 fast ripples per minute. There were no patients with 0 channels with ripples (>1/min; A), but there were patients with 0 channels with fast ripples (>1 or >20/min; B and C). The seizure frequency was shown on a logarithmic scale, because of the distribution. As indicated in the text, there was no correlation between seizure frequency per month and the number of channels with more than 1 ripple or fast ripple per minute, but there was a positive correlation between seizure frequency and more than 20 fast ripples per minute.
... This table shows the correlation coefficients Rho for different alternative comparisons: seizure frequency (seizures/month) compared to the number and percentage of channels with ripples, fast ripples, spikes and ripples and fast ripples without spikes (first two lines), seizure frequency compared to number of channels with higher rates of ripples and fast ripples (>5, >10 and >20, lines 3–5) and number of seizure-days/month compared to channels with ripples and fast ripples. All comparisons were done for all patients, all patients with temporal lobe epilepsy and all patients with unilateral mesiotemporal seizure onset.
Contributors:George M. Ibrahim, Simeon M. Wong, Ryan A. Anderson, Gabrielle Singh-Cadieux, Tomoyuki Akiyama, Ayako Ochi, Hiroshi Otsubo, Tohru Okanishi, Taufik A. Valiante, Elizabeth Donner, James T. Rutka, O. Carter Snead III, Sam M. Doesburg
Quantification of amplitude-to-phase cycle relationship. (A) Mean amplitudes of high frequencyoscillations, sorted by concurrent low-frequency phase into 60 bins of 0.105rad, for an example time window during a seizure; (B) an ideal cosine; and (C) a sine is modeled. (D) Phasor demonstrating the amplitude–phase cycle relationship. (E) The argument (angle) of the example phasor is a single contribution to be incremented onto a cumulative polar histogram spanning multiple subjects for one of ten given time periods in the seizure.
... Cross-frequency coupling... Modulation of high frequency amplitude by low-frequency phase. In the seizure-onset zone, significant modulation of high-frequency amplitude (40–300Hz) is observed, mainly by the phase of theta and alpha oscillations during the ictal period. In the interictal period, no specific CFC with slower oscillations is observed. There is also less cross-frequency coupling in the early propagation zone during seizures and no significant coupling is noted in the non-epileptogenic cortex. The z-axis demonstrates the modulation of amplitudes of different narrow-band frequencies (x-axis) by the phases of other narrow-band frequencies (y-axis). Lower and upper planes represent uncorrected and corrected statistical thresholds at p<0.05, respectively.
... High frequencyoscillations... Simulated data demonstrating expected polar histogram distribution. The high frequency amplitude is represented by the blue solid line, whereas the low-frequency phase is represented by the black dashed line. When the high frequency amplitude is maximal at the peak and trough of the low frequency phase, the polar histograms will indicate pi and 0, respectively.
... Individual seizure short-time Fourier transform spectrograms. The seizure onset zone contained predominantly low-frequency power, which was fairly heterogenous across the population. High frequency activity was also evident in all seizures as bursts of high power oscillatory activity.
... Pathological high frequencyoscillations (pHFOs) have been proposed to be robust markers of epileptic cortex. Oscillatory activity below this frequency range has been shown to be modulated by phase of lower frequencyoscillations. Here, we tested the hypothesis that dynamic cross-frequency interactions involving pHFOs are concentrated within the epileptogenic cortex. Intracranial electroencephalographic recordings from 17 children with medically-intractable epilepsy secondary to focal cortical dysplasia were obtained. A time-resolved analysis was performed to determine topographic concentrations and dynamic changes in cross-frequency amplitude-to-phase coupling (CFC). CFC between pHFOs and the phase of theta and alpha rhythms was found to be significantly elevated in the seizure-onset zone compared to non-epileptic regions (pfrequency oscillations at which pHFO amplitudes were maximal was inconsistent at seizure initiation, yet consistently at the trough of the low frequency rhythm at seizure termination. Amplitudes of pHFOs were most significantly modulated by the phase of alpha-band oscillations (p<0.01). These results suggest that increased CFC between pHFO amplitude and alpha phase may constitute a marker of epileptogenic brain areas and may be relevant for understanding seizure dynamics.... Topographic mapping of cross-frequency interactions in a representative subject. (A) Intraoperative image of grid demonstrating seizure onset and early propagation zones. (B) Fast-ripple amplitudes sorted by alpha phase for all grid electrodes. Cosine wave represents alpha phase from −π to π. Increased pHFO-to-low-frequency coupling occurs in the resected cortex (black borders). Values normalized by 95% confidence interval such values above 0 are significant at pfrequency modulation index for all electrodes, where the X-axis represents low frequency phase (1 to 40Hz; left to right), and the Y-axis denotes envelope amplitude (1 to 300Hz; top to bottom). Values exceeding Bonferroni correction threshold shown. Significant modulation of pHFO amplitudes by low-frequency phase is observed in the epileptogenic cortex.
... To characterize the ictal dynamics of relations between pHFO amplitude and low frequency phase, we measured the preferred slow oscillatory phase at which high amplitude pHFOs occurred at various times throughout the seizure. When the preferred phases from all bins from all subjects were plotted cumulatively on polar histograms, it was observed that pathological fast-ripple amplitudes preferentially occurred during the trough of alpha oscillations, whereas pathological ripple amplitudes preferentially occurred between 0rad and π/2rad of alpha and theta oscillatory cycles (Fig. 5; poscillations (p=0.14 and p=0.68, respectively). At seizure termination (i.e. the last bin), pHFO amplitudes occurred at the trough of the alpha oscillatory cycle (pathological ripple amplitude: p<0.01; pathological fast-ripple amplitude: p=0.03). Ripple amplitude maxima were also found at the peak of delta phase irrespective of the seizure progression (Supplementary Fig. S9). To ensure that differences in bin length did explain the measures of CFC, a reanalysis of the data with fixed length segments comprised of the first and last 2000ms of seizures, revealed the same pattern.