Automatic objective thresholding to detect neuronal action potentials

A simple method for efficient spike detection in multiunit recordings

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also propagation of suprathreshold spiking activity involves populations of neurons. Empirical studies addressing cortical function directly thus require recordings from populations of neurons with high resolution. Here we describe an optical method and a deconvolution algorithm to record neural activity from up to 100 neurons with single-cell and single-spike resolution. This method relies on detection of the transient increases in intracellular somatic calcium concentration associated with suprathreshold electrical spikes (action potentials) in cortical neurons. High temporal resolution of the optical recordings is achieved by a fast random-access scanning technique using acousto-optical deflectors (AODs). Two-photon excitation of the calcium-sensitive dye results in high spatial resolution in opaque brain tissue. Reconstruction of spikes from the fluorescence calcium recordings is achieved by a maximum-likelihood method. Simultaneous electrophysiological and optical recordings indicate that our method reliably detects spikes (>97% spike detection efficiency), has a low rate of false positive spike detection (< 0.003 spikes/sec), and a high temporal precision (about 3 msec). This optical method of spike detection can be used to record neural activity in vitro and in anesthetized animals in vivo.

Currently, during the operation in shallow waters, scanning systems, such as multibeam systems, are capable of collecting thousands of points in a short time, promoting a greater coverage of the submerged bottom, with consequent increase in the detection capacity of objects. Although there has been an improvement in the accuracy of the depths collected, traditional processing, that is, manual, is still required. However, mainly due to the increased mass of data collected, manual processing has become extremely time-consuming and subjective, especially in the detection and elimination of spikes. Several algorithms are used to perform this task, but most of them are based on statistical assumptions hardly met and/or verified, such as spatial independence and normality. In this sense, the goal of this study is to present the SODA (Spatial Outlier Detection Algorithm) methodology, a new method for detection of spikes designed to treat bathymetric data collected through swath bathymetry systems. From computational simulation, promising results were obtained. SODA, in some cases, was capable to identify even 90% of spikes inserted on simulation, showing that the methodology is efficient and substantial to the bathymetric data treatment.

Signals from extracellular electrodes in neural systems record voltages resulting from activity in many neurons. Detecting action potentials (spikes) in a small number of specific (target) neurons is difficult because many neurons, both near and more distant, contribute to the signal at the electrode. We consider some nearby neurons as target neurons (providing a signal) and all the other contributions to the signal as noise. A new algorithm for spike detection has been developed: this applies a cepstrum of bispectrum (CoB) estimated inverse filter to provide blind equalization. This technique is based on higher order statistics, and seeks to find a sequence of event times or delta sequence. We show that the CoB-based technique can achieve a 98% hit rate on an extracellular signal containing three spike trains at up to 0 dB SNR. Threshold setting for this technique is discussed, and we show the application of the technique to some real signals. We compare performance with four established techniques and report that the CoB-based algorithm performs best.

Recordings of extracellular spikes have been widely used in various fields ranging from basic neuroscientific research to clinical applications. However, in the extracellular recording system, how to accurately detect spikes from the recorded signal in real time is still a major challenging work. Although the existing algorithms for online spike detection have made great progress, there still remains much room for improvement in terms of accuracy. In this paper, we propose a new method for high accuracy and real time spike detection. Concretely, differential operator is firstly employed to accentuate spikes in the signal for its simplicity and strong capacity to detect significant changes. Then, by exploiting the structural features of spikes, the resolution parameter is introduced to improve the performance of differential operator. Finally, a simple and effective measure is utilized to further reduce the influence of background noise, which makes spike detection more accurate. The results of simulated and real data show that the proposed method is able to precisely detect spikes while maintaining low computational complexity.

According to the characteristics of interferogram, we design a new spike detection method, which firstly filters an interferogram with two different ways and then detects spikes based on both results. Theoretical analysis and computer simulation shows that this algorithm performs well in detecting spikes in any position of an interferogram with high accuracy, and can be easily implemented in hardware.

Detecting and classifying sharp transients in EEG (Electroencephalograph) recording by visual screening is a laborious and time-consuming task. That is why, there is an urgent need to construct a computer algorithm to detect automatically that type of EEG transient phenomena. The use of an artificial neural network as a classifier and wavelet analysis as pre-processing give promising results to answer that need. This paper proposes to develop a new method for the automatic detection of epileptic spikes based on Wavelet Neural Networks (WNN). A proper selection of scaling in WNN is introduced to overcome the problem of very long time duration during training. The result shows that proper selection of wavelet scaling can decrease training duration without decreasing WNN performance.

Objective. Translational efforts on spike-signal-based implantable brain-machine interfaces (BMIs) are increasingly aiming to minimise bandwidth while maintaining decoding performance. Developing these BMIs requires advances in neuroscience and electronic technology, as well as using low-complexity spike detection algorithms and high-performance machine learning models. While some state-of-the-art BMI systems jointly design spike detection algorithms and machine learning models, it remains unclear how the detection performance affects decoding. Approach. We propose the co-design of the neural decoder with an ultra-low complexity spike detection algorithm. The detection algorithm is designed to attain a target firing rate, which the decoder uses to modulate the input features preserving statistical invariance in long term (over several months). Main results. We demonstrate a multiplication-free fixed-point spike detection algorithm with an average detection accuracy of 97% across different noise levels on a synthetic dataset and the lowest hardware complexity among studies we have seen. By co-designing the system to incorporate statistically invariant features, we observe significantly improved long-term stability, with decoding accuracy degrading by less than 10% after 80 days of operation. Our analysis also reveals a nonlinear relationship between spike detection and decoding performance. Increasing the detection sensitivity improves decoding accuracy and long-term stability, which means the activity of more neurons is beneficial despite the detection of more noise. Reducing the spike detection sensitivity still provides acceptable decoding accuracy whilst reducing the bandwidth by at least 30%. Significance. Our findings regarding the relationship between spike detection and decoding performance can provide guidance on setting the threshold for spike detection rather than relying on training or trial-and-error. The trade-off between data bandwidth and decoding performance can be effectively managed using appropriate spike detection settings. We demonstrate improved decoding performance by maintaining statistical invariance of input features. We believe this approach can motivate further research focused on improving decoding performance through the manipulation of data itself (based on a hypothesis) rather than using more complex decoding models.

This focus issue of Physiological Measurement follows the 38th Annual International Computing in Cardiology (CinC) Conference, hosted in Hangzhou, China in September 2011 by Zhejiang University. Each year, the NIH-sponsored PhysioNet resource (http://physionet.org/) runs an open competition lasting several months, aimed at encouraging the development of solutions to an unsolved or poorly solved problem in biomedicine, in most cases making use of relevant clinical and experimental data provided freely by PhysioNet. Participants in these annual challenges discuss their diverse approaches to the Challenge problems during dedicated scientific sessions at CinC. The topics of these PhysioNet/CinC Challenges range from physiologic signal processing and analysis to forecasting and modelling clinically important events and processes.

Deep brain stimulation (DBS) has been successfully used to treat a variety of brain diseases in clinic. Recent investigations have suggested that high frequency stimulation (HFS) of electrical pulses used by DBS might change pathological rhythms in action potential firing of neurons, which may be one of the important mechanisms of DBS therapy. However, experimental data are required to confirm the hypothesis. In the present study, 1 min of 100 Hz HFS was applied to the Schaffer collaterals of hippocampal CA1 region in anaesthetized rats. The changes of the rhythmic firing of action potentials from pyramidal cells and interneurons were investigated in the downstream CA1 region. The results showed that obvious θ rhythms were present in the field potential of CA1 region of the anesthetized rats. The θ rhythms were especially pronounced in the stratum radiatum. In addition, there was a phase-locking relationship between neuronal spikes and the θ rhythms. However, HFS trains significantly decreased the phase-locking values between the spikes of pyramidal cells and the θ rhythms in stratum radiatum from 0.36 ± 0.12 to 0.06 ± 0.04 ( P < 0.001, paired t-test, N = 8). The phase-locking values of interneuron spikes were also decreased significantly from 0.27 ± 0.08 to 0.09 ± 0.05 ( P < 0.01, paired t-test, N = 8). Similar changes were obtained in the phase-locking values between neuronal spikes and the θ rhythms in the pyramidal layer. These results suggested that axonal HFS could eliminate the phase-locking relationship between action potentials of neurons and θ rhythms thereby changing the rhythmic firing of downstream neurons. HFS induced conduction block in the axons might be one of the underlying mechanisms. The finding is important for further understanding the mechanisms of DBS.

This paper presents an experiment based comparison of absolute threshold (AT) and non-linear energy operator (NEO) spike detection algorithms in intra-cortical Brain Machine Interfaces (iBMIs). Results show an average increase in decoding performance of ≈5% in monkey A across 28 sessions recorded over 6 days and ≈2% in monkey B across 35 sessions recorded over 8 days when using NEO over AT. To the best of our knowledge, this is the first ever reported comparison of spike detection algorithms in an iBMI experimental framework involving two monkeys. Based on the improvements observed in an experimental setting backed by previously reported improvements in simulation studies, we advocate switching from state of the art spike detection technique - AT to NEO.

Hearing impairment in patients with acoustic neuroma—analysis by electrocochleography

Optical methods thatrely on fluorescence for mapping changes inneuronal membrane potential in the brains of awake animals providea powerful way to interrogate the activity of neurons that underlieneural computations ranging from sensation and perception to learningand memory. To achieve this goal, fluorescent indicators should bebright, highly sensitive to small changes in membrane potential, nontoxic,and excitable with infrared light. We report a new class of fluorescent,voltage-sensitive dyes: sulfonated rhodamine voltage reporters (sRhoVR),synthetic fluorophores with high voltage sensitivity, excellent two-photonperformance, and compatibility in intact mouse brains. sRhoVR dyesare based on a tetramethyl rhodamine fluorophore coupled to a phenylenevinylenemolecular wire/diethyl aniline voltage-sensitive domain. When appliedto cells, sRhoVR dyes localize to the plasma membrane and respondto membrane depolarization with a fluorescence increase. The bestof the new dyes, sRhoVR 1, displays a 44% ΔF/F increase in fluorescence per 100 mV change, emitsat 570 nm, and possesses excellent two-photon absorption of approximately200 GM at 840 nm. sRhoVR 1 can detect action potentials in culturedrat hippocampal neurons under both single- and two-photon illuminationwith sufficient speed and sensitivity to report on action potentialsin single trials, without perturbing underlying physiology or membraneproperties. The combination of speed, sensitivity, and brightnessunder two-photon illumination makes sRhoVR 1 a promising candidatefor in vivo imaging in intact brains. We show sRhoVRpowerfully complements electrode-based modes of neuronal activityrecording in the mouse brain by recording neuronal transmembrane potentialsfrom the neuropil of layer 2/3 of the mouse barrel cortex in concertwith extracellularly recorded local field potentials (LFPs). sRhoVRimaging reveals robust depolarization in response to whisker stimulation;concurrent electrode recordings reveal negative deflections in theLFP recording, consistent with the canonical thalamocortical response.Importantly, sRhoVR 1 can be applied in mice with chronic opticalwindows, presaging its utility in dissecting and resolving voltagedynamics using two-photon functional imaging in awake, behaving animals.

Spiking Irregularity and Frequency Modulate the Behavioral Report of Single-Neuron Stimulation

Predictive factors in infraorbital sensitivity disturbances following zygomaticomaxillary fractures