The dynamics of single spike-evoked adenosine release in the cerebellum

The purine adenosine is a potent neuromodulator in the brain, with roles in a number of diverse physiological and pathological processes. Modulators such as adenosine are difficult to study as once released they have a diffuse action (which can affect many neurones) and, unlike classical neurotransmitters, have no inotropic receptors. Thus rapid postsynaptic currents (PSCs) mediated by adenosine (equivalent to mPSCs) are not available for study. As a result the mechanisms and properties of adenosine release still remain relatively unclear. We have studied adenosine release evoked by stimulating the parallel fibres in the cerebellum. Using adenosine biosensors combined with deconvolution analysis and mathematical modelling, we have characterised the release dynamics and diffusion of adenosine in unprecedented detail. By partially blocking K+ channels, we were able to release adenosine in response to a single stimulus rather than a train of stimuli. This allowed reliable sub-second release of reproducible quantities of adenosine with stereotypic concentration waveforms that agreed well with predictions of a mathematical model of purine diffusion. We found no evidence for ATP release and thus suggest that adenosine is directly released in response to parallel fibre firing and does not arise from extracellular ATP metabolism. Adenosine release events showed novel short-term dynamics, including facilitated release with paired stimuli at millisecond stimulation intervals but depletion-recovery dynamics with paired stimuli delivered over minute time scales. These results demonstrate rich dynamics for adenosine release that are placed, for the first time, on a quantitative footing and show strong similarity with vesicular exocytosis

Objective auditory brainstem response classification using machine learning

The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: ‘clear response’, ‘inconclusive’ or ‘response absent’. A deep convolutional neural network was constructed and fine-tuned using stratified 10-fold cross-validation on 190 paired ABR waveforms. The final model was evaluated on a test set of 42 paired waveforms. The full dataset comprised 232 paired ABR waveforms recorded from eight normal-hearing individuals. The dataset was obtained from the PhysioBank database. The paired waveforms were independently labelled by two audiological scientists in order to train the network and evaluate its performance. The trained neural network was able to classify paired ABR waveforms with 92.9% accuracy. The sensitivity and the specificity were 92.9% and 96.4%, respectively. This neural network may have clinical utility in assisting clinicians with waveform classification for the purpose of hearing threshold estimation. Further evaluation using a large clinically obtained dataset would provide further validation with regard to the clinical potential of the neural network in diagnostic adult testing, newborn testing and in automated newborn hearing screening

A Novel Method for Characterization of Peripheral Nerve Fiber Size Distributions by Group Delay Measurements and Simulated Annealing Optimization

The ability to determine the characteristics of peripheral nerve fiber size distributions would provide additional information to clinicians for the diagnosis of specific pathologies of the peripheral nervous system. Investigation of these conditions, using electro-diagnostic techniques, is advantageous in the sense that such techniques tend to be minimally invasive yet provide valuable diagnostic information. One of the principal electro-diagnostic tools available to the clinician is the nerve conduction velocity test. While the peripheral nerve conduction velocity test can provide useful information to the clinician regarding the viability of the nerve under study, it is a single parameter test that yields no detailed information about the characteristics of the functioning nerve fibers within the nerve trunk. In this study we present a technique based on a decomposition of the maximal compound evoked potential and subsequent determination of the group delay of the contributing nerve fibers. The fiber group delay is then utilized as an initial estimation of the nerve fiber size distribution and the concomitant temporal propagation delays of the associated single fiber evoked potentials to a reference electrode. Subsequently the estimated single fiber evoked potentials are optimized against the template maximal compound evoked potential using a simulated annealing algorithm. Simulation studies, based on deterministic single fiber action potential functions, are used to demonstrate the robustness of the proposed technique in the presence of noise associated with variations in distance between the nerve fibers and the recording electrodes between the two recording sites

Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.published_or_final_versio

THE EFFECT OF FIBER DEPTH ON THE ESTIMATION OF PERIPHERAL NERVE FIBER DIAMETER USING GROUP DELAY AND SIMULATED ANNEALING OPTIMIZATION

Peripheral neuropathy refers to diseases of or injuries to the peripheral nerves in the human body. The damage can interfere with the vital connection between the central nervous system and other parts of the body, and can significantly reduce the quality of life of those affected. In the US, approximately between 15 and 20 million people over the age of 40 have some forms of peripheral neuropathy. The diagnosis of peripheral neuropathy often requires an invasive operation such as a biopsy because different forms of peripheral neuropathy can affect different types of nerve fibers. There are non-invasive methods available to diagnose peripheral neuropathy such as the nerve conduction velocity test (NCV). Although the NCV is useful to test the viability of an entire nerve trunk, it does not provide adequate information about the individual functioning nerve fibers in the nerve trunk to differentiate between the different forms of peripheral neuropathy. A novel technique was proposed to estimate the individual nerve fiber diameters using group delay and simulated annealing optimization. However, this technique assumed that the fiber depth is always constant at 1 mm and the fiber activation due to a stimulus is depth independent. This study aims to incorporate the effect of fiber depth into the fiber diameter estimation technique and to make the simulation more realistic, as well as to move a step closer to making this technique a viable diagnostic tool. From the simulation data, this study found that changing the assumption of the fiber depth significantly impacts the accuracy of the fiber diameter estimation. The results suggest that the accuracy of the fiber diameter estimation is dependent on whether the type of activation function is depth dependent or not, and whether the template fiber diameter distribution contains mostly large fibers or both small and large fibers, but not dependent on whether the fiber depth is constant or variable

MEG, PSYCHOPHYSICAL AND COMPUTATIONAL STUDIES OF LOUDNESS, TIMBRE, AND AUDIOVISUAL INTEGRATION

Natural scenes and ecological signals are inherently complex and understanding of their perception and processing is incomplete. For example, a speech signal contains not only information at various frequencies, but is also not static; the signal is concurrently modulated temporally. In addition, an auditory signal may be paired with additional sensory information, as in the case of audiovisual speech. In order to make sense of the signal, a human observer must process the information provided by low-level sensory systems and integrate it across sensory modalities and with cognitive information (e.g., object identification information, phonetic information). The observer must then create functional relationships between the signals encountered to form a coherent percept. The neuronal and cognitive mechanisms underlying this integration can be quantified in several ways: by taking physiological measurements, assessing behavioral output for a given task and modeling signal relationships. While ecological tokens are complex in a way that exceeds our current understanding, progress can be made by utilizing synthetic signals that encompass specific essential features of ecological signals. The experiments presented here cover five aspects of complex signal processing using approximations of ecological signals : (i) auditory integration of complex tones comprised of different frequencies and component power levels; (ii) audiovisual integration approximating that of human speech; (iii) behavioral measurement of signal discrimination; (iv) signal classification via simple computational analyses and (v) neuronal processing of synthesized auditory signals approximating speech tokens. To investigate neuronal processing, magnetoencephalography (MEG) is employed to assess cortical processing non-invasively. Behavioral measures are employed to evaluate observer acuity in signal discrimination and to test the limits of perceptual resolution. Computational methods are used to examine the relationships in perceptual space and physiological processing between synthetic auditory signals, using features of the signals themselves as well as biologically-motivated models of auditory representation. Together, the various methodologies and experimental paradigms advance the understanding of ecological signal analytics concerning the complex interactions in ecological signal structure

Perturbation Based Decomposition for Motor Unit Number Estimation

Motor unit number estimation (MUNE) is of clinical significance because information associated with motor unit populations and the loss of motor units over time are crucial to the diagnosis and monitoring of neuromuscular disorders. The responses of individual motor units can be extracted through incremental stimulation of a muscle group of interest and further analyzed through various methods to compute the MUNE. In this work, the utility of the perturbation-based decomposition by Szlavik will be investigated for this application and compared to the original MUNE approach developed by McComas. Successful decomposition may suggest an alternative method for motor unit number estimation. The decomposition was applied to synthetic incremental twitch tension waveforms and yielded satisfactory approximations of the contributions of individual motor units towards the compound muscle response