Advances in point process filters and their application to sympathetic neural activity

This thesis is concerned with the development of techniques for analyzing the sequences of stereotypical electrical impulses within neurons known as spikes. Sequences of spikes, also called spike trains, transmit neural information; decoding them often provides details about the physiological processes generating the neural activity. Here, the statistical theory of event arrivals, called point processes, is applied to human muscle sympathetic spike trains, a peripheral nerve signal responsible for cardiovascular regulation. A novel technique that uses observed spike trains to dynamically derive information about the physiological processes generating them is also introduced. Despite the emerging usage of individual spikes in the analysis of human muscle sympathetic nerve activity, the majority of studies in this field remain focused on bursts of activity at or below cardiac rhythm frequencies. Point process theory applied to multi-neuron spike trains captured both fast and slow spiking rhythms. First, analysis of high-frequency spiking patterns within cardiac cycles was performed and, surprisingly, revealed fibers with no cardiac rhythmicity. Modeling spikes as a function of average firing rates showed that individual nerves contribute substantially to the differences in the sympathetic stressor response across experimental conditions. Subsequent investigation of low-frequency spiking identified two physiologically relevant frequency bands, and modeling spike trains as a function of hemodynamic variables uncovered complex associations between spiking activity and biophysical covariates at these two frequencies. For example, exercise-induced neural activation enhances the relationship of spikes to respiration but does not affect the extremely precise alignment of spikes to diastolic blood pressure. Additionally, a novel method of utilizing point process observations to estimate an internal state process with partially linear dynamics was introduced. Separation of the linear components of the process model and reduction of the sampled space dimensionality improved the computational efficiency of the estimator. The method was tested on an established biophysical model by concurrently computing the dynamic electrical currents of a simulated neuron and estimating its conductance properties. Computational load reduction, improved accuracy, and applicability outside neuroscience establish the new technique as a valuable tool for decoding large dynamical systems with linear substructure and point process observations

Sodium-activated potassium channels shape peripheral auditory function and activity of the primary auditory neurons in mice

Potassium (K+) channels shape the response properties of neurons. Although enormous progress has been made to characterize K+ channels in the primary auditory neurons, the molecular identities of many of these channels and their contributions to hearing in vivo remain unknown. Using a combination of RNA sequencing and single molecule fluorescent in situ hybridization, we localized expression of transcripts encoding the sodium-activated potassium channels K(Na)1.1(SLO2.2/Slack) and K(Na)1.2 (SLO2.1/Slick) to the primary auditory neurons (spiral ganglion neurons, SGNs). To examine the contribution of these channels to function of the SGNs in vivo, we measured auditory brainstem responses in K(Na)1.1/1.2 double knockout (DKO) mice. Although auditory brainstem response (wave I) thresholds were not altered, the amplitudes of suprathreshold responses were reduced in DKO mice. This reduction in amplitude occurred despite normal numbers and molecular architecture of the SGNs and their synapses with the inner hair cells. Patch clamp electrophysiology of SGNs isolated from DKO mice displayed altered membrane properties, including reduced action potential thresholds and amplitudes. These findings show that K(Na)1 channel activity is essential for normal cochlear function and suggest that early forms of hearing loss may result from physiological changes in the activity of the primary auditory neurons

Algorithms for Neural Prosthetic Applications

abstract: In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

An application of an auditory periphery model in speaker identification

The number of applications of automatic Speaker Identification (SID) is growing due to the advanced technologies for secure access and authentication in services and devices. In 2016, in a study, the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR FAC) cochlear model achieved the best performance among seven recent cochlear models to fit a set of human auditory physiological data. Motivated by the performance of the CAR-FAC, I apply this cochlear model in an SID task for the first time to produce a similar performance to a human auditory system. This thesis investigates the potential of the CAR-FAC model in an SID task. I investigate the capability of the CAR-FAC in text-dependent and text-independent SID tasks. This thesis also investigates contributions of different parameters, nonlinearities, and stages of the CAR-FAC that enhance SID accuracy. The performance of the CAR-FAC is compared with another recent cochlear model called the Auditory Nerve (AN) model. In addition, three FFT-based auditory features – Mel frequency Cepstral Coefficient (MFCC), Frequency Domain Linear Prediction (FDLP), and Gammatone Frequency Cepstral Coefficient (GFCC), are also included to compare their performance with cochlear features. This comparison allows me to investigate a better front-end for a noise-robust SID system. Three different statistical classifiers: a Gaussian Mixture Model with Universal Background Model (GMM-UBM), a Support Vector Machine (SVM), and an I-vector were used to evaluate the performance. These statistical classifiers allow me to investigate nonlinearities in the cochlear front-ends. The performance is evaluated under clean and noisy conditions for a wide range of noise levels. Techniques to improve the performance of a cochlear algorithm are also investigated in this thesis. It was found that the application of a cube root and DCT on cochlear output enhances the SID accuracy substantially

A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces

The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting

Denervation does not induce muscle atrophy through oxidative stress

Denervation leads to the activation of the catabolic pathways, such as the ubiquitin-proteasome and autophagy, resulting in skeletal muscle atrophy and weakness. Furthermore, denervation induces oxidative stress in skeletal muscle, which is thought to contribute to the induction of skeletal muscle atrophy. Several muscle diseases are characterized by denervation, but the molecular pathways contributing to muscle atrophy have been only partially described. Our study delineates the kinetics of activation of oxidative stress response in skeletal muscle following denervation. Despite the denervation-dependent induction of oxidative stress in skeletal muscle, treatments with anti-oxidant drugs do not prevent the reduction of muscle mass. Our results indicate that, although oxidative stress may contribute to the activation of the response to denervation, it is not responsible by itself of oxidative damage or neurogenic muscle atrophy

Progranulin contributes to endogenous mechanisms of pain defense after nerve injury in mice

Progranulin haploinsufficiency is associated with frontotemporal dementia in humans. Deficiency of progranulin led to exaggerated inflammation and premature aging in mice. The role of progranulin in adaptations to nerve injury and neuropathic pain are still unknown. Here we found that progranulin is up-regulated after injury of the sciatic nerve in the mouse ipsilateral dorsal root ganglia and spinal cord, most prominently in the microglia surrounding injured motor neurons. Progranulin knockdown by continuous intrathecal spinal delivery of small interfering RNA after sciatic nerve injury intensified neuropathic pain-like behaviour and delayed the recovery of motor functions. Compared to wild-type mice, progranulin-deficient mice developed more intense nociceptive hypersensitivity after nerve injury. The differences escalated with aging. Knockdown of progranulin reduced the survival of dissociated primary neurons and neurite outgrowth, whereas addition of recombinant progranulin rescued primary dorsal root ganglia neurons from cell death induced by nerve growth factor withdrawal. Thus, up-regulation of progranulin after neuronal injury may reduce neuropathic pain and help motor function recovery, at least in part, by promoting survival of injured neurons and supporting regrowth. A deficiency in this mechanism may increase the risk for injury-associated chronic pain