Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer’s intention to stimulate a rat’s brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer’s intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.
Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the population response. This underlying structure explains many of the confusing features of individual neural responses.
Cervical © and ocular (o) vestibular evoked myogenic potentials (VEMPs) provide important tools for measuring otolith function. However, two major drawbacks of this method are encountered in clinical practice. First, recording of oVEMPs is compromised by small n10 amplitudes. Second, VEMP analysis is currently based on the averaging technique, resulting in a loss of information compared to single sweep analysis. Here, we (1) developed a novel electromotive trigger mechanism for evoking VEMPs by bone-conducted vibration to the forehead and (2) established maximum entropy extraction of complex wavelet transforms for calculation of phase synchronization between VEMP single sweeps. Both c- and oVEMPs were recorded for n=10 healthy individuals. The oVEMP n10 amplitude was consistently higher (right: 24:849:71 V ; left: 27:4014:55 V ) than previously described. Stable VEMP signals were reached after a smaller number of head taps (oVEMPs < 6; cVEMPs < 11) compared to current recommendations. Phase synchronization vectors and phase shift values were successfully determined for simulated and clinically recorded VEMPs, providing information about the impact of noise and phase jitter on the VEMP signal. Thus, the proposed method constitutes an easy-to-use approach for the fast detection and analysis of VEMPs in clinical practice.
- The Journal of neuroscience : the official journal of the Society for Neuroscience
- Published almost 8 years ago
Recent reports converge to the idea that high-frequency oscillations in local field potentials (LFPs) represent multiunit activity. In particular, the amplitude of LFP activity above 100 Hz-commonly referred to as “high-gamma” or “epsilon” band-was found to correlate with firing rate. However, other studies suggest the existence of true LFP oscillations at this frequency range that are different from the well established ripple oscillations. Using multisite recordings of the hippocampus of freely moving rats, we show here that high-frequency LFP oscillations can represent either the spectral leakage of spiking activity or a genuine rhythm, depending on recording location. Both spike-leaked, spurious activity and true fast oscillations couple to theta phase; however, the two phenomena can be clearly distinguished by other key features, such as preferred coupling phase and spectral signatures. Our results argue against the idea that all high-frequency LFP activity stems from spike contamination and suggest avoiding defining brain rhythms solely based on frequency range.
We present the first microwave photonic phase shifter using stimulated Brillouin scattering (SBS) on-chip. The unique ability of SBS to generate both narrowband gain and loss resonances allows us to achieve low ±1.5 dB amplitude fluctuations, which is a record for integrated devices, along with 240° continuously tunable phase shift. Contrary to previous SBS-based approaches, the phase shift tuning mechanism relies on tuning the power, not the frequency, of two SBS pumps, making it more suited to on-chip implementations. We finally demonstrate that SBS pump depletion leads to amplitude response fluctuations, as well as increasing the insertion loss of the phase shifter. Advantageously, shorter integrated platforms possess higher pump depletion thresholds compared to long fibers, thus offering greater potential for reducing the insertion loss.
Sperm motion near surfaces plays a crucial role in fertilization, but the nature of this motion has not been resolved. Using total internal reflection fluorescence microscopy, we selectively imaged motile human and bull sperm located within one micron of a surface, revealing a distinct two-dimensional (2D) ‘slither’ swimming mode whereby the full cell length (50-80 μm) is confined within 1 μm of a surface. This behaviour is distinct from bulk and near-wall swimming modes where the flagellar wave is helical and the head continuously rotates. The slither mode is intermittent (∼1 s, ∼70 μm), and in human sperm, is observed only for viscosities over 20 mPa·s. Bull sperm are slower in this surface-confined swimming mode, owing to a decrease in their flagellar wave amplitude. In contrast, human sperm are ∼50% faster-suggesting a strategy that is well suited to the highly viscous and confined lumen within the human fallopian tube.
We present two-step phase-shifting differential-recording digital holographic microscopy (TPD-DH in microscopy) for phase imaging of microscopic transparent elements. Two CCDs are employed to record two interferograms at two different defocusing distances. The interferograms on the two CCD cameras are shifted for a phase retarder 0 and π via an all-optics phase shifting unit. A novel algorithm is proposed to reconstruct both amplitude and phase distributions of the object wave from the recorded interferograms. This method has the same spectrum bandwidth and measurement accuracy with those of conventional four-step phase-shifting interferometry (FS-PSI), whereas it reduces the measurement time by half.
This study aimed to determine if a quantifiable relationship exists between the peak sound amplitude and peak vertical ground reaction force (vGRF) and vertical loading rate during running. It also investigated whether differences in peak sound amplitude, contact time, lower limb kinematics, kinetics and foot strike technique existed when participants were verbally instructed to run quietly compared to their normal running. A total of 26 males completed running trials for two sound conditions: normal running and quiet running. Simple linear regressions revealed no significant relationships between impact sound and peak vGRF in the normal and quiet conditions and vertical loading rate in the normal condition. t-Tests revealed significant within-subject decreases in peak sound, peak vGRF and vertical loading rate during the quiet compared to the normal running condition. During the normal running condition, 15.4% of participants utilised a non-rearfoot strike technique compared to 76.9% in the quiet condition, which was corroborated by an increased ankle plantarflexion angle at initial contact. This study demonstrated that quieter impact sound is not directly associated with a lower peak vGRF or vertical loading rate. However, given the instructions to run quietly, participants effectively reduced peak impact sound, peak vGRF and vertical loading rate.
Recently there has been a strong interest in cross-frequency coupling, the interaction between neuronal oscillations in different frequency bands. In particular, measures quantifying the coupling between the phase of slow oscillations and the amplitude of fast oscillations have been applied to a wide range of data recorded from animals and humans. Some of the measures applied to detect phase-amplitude coupling have been criticized for being sensitive to nonsinusoidal properties of the oscillations and thus spuriously indicate the presence of coupling. While such instances of spurious identification of coupling have been observed, in this commentary we give concrete examples illustrating cases when the identification of cross-frequency coupling can be trusted. These examples are based on control analyses and empirical observations rather than signal-processing tools. Finally, we provide concrete advice on how to determine when measures of phase-amplitude coupling can be considered trustworthy.
Cerebral autoregulation (CA) is an important vascular control mechanism responsible for relatively stable cerebral blood flow despite changes of systemic blood pressure (BP). Impaired CA may leave brain tissue unprotected against potentially harmful effects of BP fluctuations. It is generally accepted that CA is less effective or even inactive at frequencies >∼0.1 Hz. Without any physiological foundation, this concept is based on studies that quantified the coupling between BP and cerebral blood flow velocity (BFV) using transfer function analysis. This traditional analysis assumes stationary oscillations with constant amplitude and period, and may be unreliable or even invalid for analysis of nonstationary BP and BFV signals. In this study we propose a novel computational tool for CA assessment that is based on nonlinear dynamic theory without the assumption of stationary signals. Using this method, we studied BP and BFV recordings collected from 39 patients with chronic ischemic infarctions and 40 age-matched non-stroke subjects during baseline resting conditions. The active CA function in non-stroke subjects was associated with an advanced phase in BFV oscillations compared to BP oscillations at frequencies from ∼0.02 to 0.38 Hz. The phase shift was reduced in stroke patients even at > = 6 months after stroke, and the reduction was consistent at all tested frequencies and in both stroke and non-stroke hemispheres. These results provide strong evidence that CA may be active in a much wider frequency region than previously believed and that the altered multiscale CA in different vascular territories following stroke may have important clinical implications for post-stroke recovery. Moreover, the stroke effects on multiscale cerebral blood flow regulation could not be detected by transfer function analysis, suggesting that nonlinear approaches without the assumption of stationarity are more sensitive for the assessment of the coupling of nonstationary physiological signals.