Adult mouse dorsal root ganglia neurons form aberrant glutamatergic connections in dissociated cultures.

Cultured sensory neurons can exhibit complex activity patterns following stimulation in terms of increased excitability and interconnected responses of multiple neurons. Although these complex activity patterns suggest a network-like configuration, research so far had little interest in synaptic network formation ability of the sensory neurons. To identify interaction profiles of Dorsal Root Ganglia (DRG) neurons and explore their putative connectivity, we developed an in vitro experimental approach. A double transgenic mouse model, expressing genetically encoded calcium indicator (GECI) in their glutamatergic neurons, was produced. Dissociated DRG cultures from adult mice were prepared with a serum-free protocol and no additional growth factors or cytokines were utilized for neuronal sensitization. DRG neurons were grown on microelectrode arrays (MEA) to induce stimulus-evoked activity with a modality-free stimulation strategy. With an almost single-cell level electrical stimulation, spontaneous and evoked activity of GCaMP6s expressing neurons were detected under confocal microscope. Typical responses were analyzed, and correlated calcium events were detected across individual DRG neurons. Next, correlated responses were successfully blocked by glutamatergic receptor antagonists, which indicated functional synaptic coupling. Immunostaining confirmed the presence of synapses mainly in the axonal terminals, axon-soma junctions and axon-axon intersection sites. Concisely, the results presented here illustrate a new type of neuron-to-neuron interaction in cultured DRG neurons conducted through synapses. The developed assay can be a valuable tool to analyze individual and collective responses of the cultured sensory neurons.

Directional dual-tree complex wavelet packet transforms for processing quadrature signals.

Quadrature signals containing in-phase and quadrature-phase components are used in many signal processing applications in every field of science and engineering. Specifically, Doppler ultrasound systems used to evaluate cardiovascular disorders noninvasively also result in quadrature format signals. In order to obtain directional blood flow information, the quadrature outputs have to be preprocessed using methods such as asymmetrical and symmetrical phasing filter techniques. These resultant directional signals can be employed in order to detect asymptomatic embolic signals caused by small emboli, which are indicators of a possible future stroke, in the cerebral circulation. Various transform-based methods such as Fourier and wavelet were frequently used in processing embolic signals. However, most of the times, the Fourier and discrete wavelet transforms are not appropriate for the analysis of embolic signals due to their non-stationary time-frequency behavior. Alternatively, discrete wavelet packet transform can perform an adaptive decomposition of the time-frequency axis. In this study, directional discrete wavelet packet transforms, which have the ability to map directional information while processing quadrature signals and have less computational complexity than the existing wavelet packet-based methods, are introduced. The performances of proposed methods are examined in detail by using single-frequency, synthetic narrow-band, and embolic quadrature signals.

Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.

In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.

Optimization of coil geometries for bone fracture healing via dielectrophoretic force stimulation - a simulation study.

In this paper we propose a novel technique for shortening fracture healing times based on the use of dielectrophoretic forces (DEPFs). If a non-uniform electromagnetic field is applied around a fracture site, red blood cells within the blood will be polarized; creating electrical dipoles. The dielectrophoretic forces resulting from the interaction of these dipoles and the electromagnetic field, can be used to manipulate blood flow at a fracture site, promote vascularization, increase transmembrane signaling, increase supply of nutrients, necessary hormones and growth factors at the fracture site and thus may help bone healing. For the generation of non-uniform fields we considered three different coil designs (linear, parabolic and square root) and using Mathcad numerically studied the dielectrophoretic forces for a long bone fracture where the main arteries are vertically-oriented and the blood flow is downward. The gravitational force and the drag force on the red blood cells determine the steady state blood flow. The dielectrophoretic force added to the force balance is functional in increasing the blood flow. The ratio of the velocity in the presence of dielectrophoresis to the velocity without dielectrophoresis (called here as the Dielectrophoretic Force Factor, K(DEpF)) is a good measure of the performance of the dielectrophoresis, since it indicates the increase in blood flow. It was found that the dielectorophoretic force reaches peak levels at a frequency range between 5-15 Hz. At 5 Hz, the average value of dielectrophoretic force factor is 1.90, 2.51 and 1.61 for the linear, parabolic and the square root coils, respectively. The parabolic coil results in the best DEPF and therefore would be the configuration to use in an experimental study to determine if DEPF is useful for bone healing.

Symmetrical directional dual-tree complex wavelet packet transform.

In this study, a symmetrical directional complex discrete wavelet packet transform, which can be applied directly to the quadrature format signals and has the ability of mapping directional information during decomposition stage, is proposed. With the proposed symmetrical directional complex wavelet packet transform, traditional symmetrical phasing filter technique, which is used for quadrature signal to directional signal conversion, is eliminated and the computational complexity of whole process is reduced. The performance of proposed method is examined in detail using real quadrature embolic signals.

Directional dual-tree rational-dilation complex wavelet transform.

Dyadic discrete wavelet transform (DWT) has been used successfully in processing signals having non-oscillatory transient behaviour. However, due to the low Q-factor property of their wavelet atoms, the dyadic DWT is less effective in processing oscillatory signals such as embolic signals (ESs). ESs are extracted from quadrature Doppler signals, which are the output of Doppler ultrasound systems. In order to process ESs, firstly, a pre-processing operation known as phase filtering for obtaining directional signals from quadrature Doppler signals must be employed. Only then, wavelet based methods can be applied to these directional signals for further analysis. In this study, a directional dual-tree rational-dilation complex wavelet transform, which can be applied directly to quadrature signals and has the ability of extracting directional information during analysis, is introduced.

Directional dual-tree complex wavelet packet transform.

Doppler ultrasound systems, which are widely used in cardiovascular disorders detection, have quadrature format outputs. Various types of algorithms were described in literature to process quadrature Doppler signals (QDS), such as phasing filter technique (PFT), fast Fourier transform method, frequency domain Hilbert transform method and complex continuous wavelet transform. However for the discrete wavelet transform (DWT) case, which becomes a common method for processing QDSs, there was not a direct method to recover flow direction from quadrature signals. Traditionally, to process QDSs with DWT, firstly directional signals have to be extracted and later two DWTs must be applied. Although there exists a complex DWT algorithm called dual tree complex discrete wavelet transform (DTCWT), it does not provide directional signal decoding during analysis because of the unwanted energy leaks into its negative frequency bands. Modified DTCWT, which is a combination of PFT and DTCWT, has the capability of extracting directional information while decomposing QDSs into different frequency bands, but it uses an additional Hilbert transform filter and it increases the computational complexity of whole transform. Discrete wavelet packet transform (DWPT), which is a generalization of the ordinary DWT allowing subband analysis without the constraint of dyadic decomposition, can perform an adaptive decomposition of the frequency axis. In this study, a novel complex DWPT, which maps directional information while processing QDSs, is proposed. The success of proposed method will be measured by using simulated quadrature signals.

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study.

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.