The basal ganglia (BG) is a complex set of heavily interconnected nuclei located in the central part of the brain that receives inputs from the several areas of the cortex and projects via the thalamus back to the prefrontal and motor cortical areas. Despite playing a significant part in multiple brain functions, the physiology of the BG and associated disorders like dystonia remain poorly understood. Dystonia is a devastating condition characterized by ineffective, twisting movements, prolonged co-contractions and contorted postures. Evidences suggest that it occurs due to abnormal discharge patterning in BG-thalamocortocal (BGTC) circuitry. The central purpose of this study was to understand the electrophysiology of BGTC circuitry and its role in motor control and dystonia. Toward this goal, an advanced multi-target multi-unit recording and analysis system was utilized, which allows simultaneous collection and analysis of multiple neuronal units from multiple brain nuclei. Over the cause of this work, neuronal data from the globus pallidus (GP), subthalamic nucleus (STN), entopenduncular nucleus (EP), pallidal receiving thalamus (VL) and motor cortex (MC) was collected from normal, lesioned and dystonic rats under awake, head restrained conditions. The results have shown that the neuronal population in BG nuclei (GP, STN and EP) were characterized by a dichotomy of firing patterns in normal rats which remains preserved in dystonic rats. Unlike normals, neurons in dystonic rat exhibit reduced mean firing rate, increased irregularity and burstiness at resting state. The chaotic changes that occurs in BG leads to inadequate hyperpolarization levels within the VL thalamic neurons resulting in a shift from the normal bursting mode to an abnormal tonic firing pattern. During movement, the dystonic EP generates abnormally synchronized and elongated burst duration which further corrupts the VL motor signals. It was finally concluded that the loss of specificity and temporal misalignment between motor neurons leads to corrupted signaling to the muscles resulting in dystonic behavior. Furthermore, this study reveals the importance of EP output in controlling firing modes occurring in the VL thalamus.

Sudden Cardiac Death (SCD) is the largest cause of natural deaths in the USA, accounting for over 300,000 deaths annually. The major reason for SCD is Ventricular Arrhythmia (VA). Therefore, there is need for exploration of approaches to predict increased risk for VA. Alternans of the T wave in the ECG (TWA) is widely investigated as a potential predictor of VA, however, clinical trials show that TWA has high negative predictive value but poor positive predictive value. A possible reason that TWA has a large number of false positives is that a pattern of alternans known as concordant alternans, may not be as arrhythmogenic as another pattern which is discordant alternans. Currently, it is not possible to discern the pattern of alternans using clinical ECGs. Prior studies from our group have showed that alternans of the maximum rate of depolarization of an action potential also can occur when Action Potential Duration (APD) alternans occurs and the relationship between these two has the potential to create spatial discord. These results suggest that exploration of the co-occurrence of depolarization and repolarization alternans has the potential to stratify the outcome of TWA tests. In order to investigate the link between depolarization alternans and changes in ECGs, we used a mathematical model created previously in our research group which simulated ECGs from the cellular level changes observed in our experimental studies. These results suggest that the changes in ECGs should appear as alternating pattern of the amplitude of the R wave. Because there are a variety of factors which may also cause the R wave amplitude to change, we used signal analysis and statistical modeling to determine the link between the observed changes in R wave amplitude and depolarization alternans. Results from ECGs recorded from patients show that amplitude of the R wave can change as predicted by our experimental results and mathematical model. Using TWA as the marker of repolarization alternans and R Wave Amplitude Alternans (RWAA) as the marker of depolarization alternans, we investigated the phase relation between depolarization and repolarization alternans in clinical grade ECG and observed that this relationship does change spontaneously, consistent with our prior results from animal studies. Results of the present study support further investigation of the use of RWAA as a complementary method to TWA to improve its positive predictive value.

Neurological disorders require varying types and degrees of treatments depending on the symptoms and underlying causes of the disease. Patients suffering from medication-refractory symptoms often undergo further treatment in the form of brain stimulation, e.g. electroconvulsive therapy (ECT), transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), or transcranial magnetic stimulation (TMS). These treatments are popular and have been shown to relieve various symptoms for patients with neurological conditions. However, the underlying effects of the stimulation, and subsequently the causes of symptom-relief, are not very well understood. In particular, TMS is a non-invasive brain stimulation therapy which uses time-varying magnetic fields to induce electric fields on the conductive parts of the brain. TMS has been FDA-approved for treatment of major depressive disorder for patients refractory to medication, as well as symptoms of migraine. Studies have shown that TMS has relieved severe depressive symptoms, although researchers believe that it is the deeper regions of the brain which are responsible for symptom relief. Many experts theorize that cortical stimulation such as TMS causes brain signals to propagate from the cortex to these deep brain regions, after which the synapses of the excited neurons are changed in such a way as to cause plasticity. It has also been widely observed that stimulation of the cortex causes signal firing at the deeper regions of the brain. However, the particular mechanisms behind TMS-caused signal propagation are unknown and understudied. Due to the non-invasive nature of TMS, this is an area in which investigation can be of significant benefit to the clinical community. We posit that a deeper understanding of this phenomenon may allow clinicians to explore the use of TMS for treatment of various other neurological symptoms and conditions. This thesis project seeks to investigate the various effects of TMS in the human brain, with respect to brain tissue stimulation as well as the cellular effects at the level of neurons. We present novel models of motor neuron circuitry and fiber tracts that will aid in the development of deep brain stimulation modalities using non-invasive treatment paradigms.

Chitosan is widely investigated for biomedical applications due to its excellent properties, such as biocompatibility, biodegradability, bioadhesivity, antibacterial, etc. In the field of neural engineering, it has been extensively studied in forms of film and hydrogel, and has been used as scaffolds for nerve regeneration in the peripheral nervous system and spinal cord. One of the main issues in neural engineering is the incapability of neuron to attach on biomaterials. The present study, from a new aspect, aims to take advantage of the bio-adhesive property of chitosan to develop chitosan-based materials for neural engineering, specifically in the fields of brain repair and neuroprosthetics. Neuronal responses to the developed biomaterials will also be investigated and discussed. In the first part of this study (Chapter II), chitosan was blended with a well-studied hydrogel material (agarose) to form a simply prepared hydrogel system. The stiffness of the agarose gel was maintained despite the inclusion of chitosan. The structure of the blended hydrogels was characterized by light microscopy and scanning electron microscopy. In vitro cell studies revealed the capability of chitosan to promote neuron adhesion. The concentration of chitosan in the hydrogel had great influence on neurite extension. An optimum range of chitosan concentration in agarose hydrogel, to enhance neuron attachment and neurite extension, was identified based on the results. A “steric hindrance” effect of chitosan was proposed, which explains the origin of the morphological differences of neurons in the blended gels as well as the influence of the physical environment on neuron adhesion and neurite outgrowth. This chitosan-agarose (C-A) hydrogel system and its multi-functionality allow for applications of simply prepared agarose-based hydrogels for brain tissue repair. In the second part of this study (Chapter III), chitosan was blended with graphene to form a series of graphene-chitosan (G-C) nanocomposites for potential neural interface applications. Both substrate-supported coatings and free standing films could be prepared by air evaporation of precursor solutions. The electrical conductivity of graphene was maintained after the addition of chitosan, which is non-conductive. The surface characteristic of the films was sensitively dependent on film composition, and in turn, influenced neuron adhesion and neurite extension. Biological studies showed good cytocompatibility of graphene for both fibroblast and neuron. Good cell-substrate interactions between neurons and G-C nanocomposites were found on samples with appropriate compositions. The results suggest this unique nanocomposite system may be a promising substrate material used for the fabrication of implantable neural electrodes. Overall, these studies confirmed the bio-adhesive property of chitosan. More importantly, the developed chitosan-based materials also have great potential in the fields of neural tissue engineering and neuroprosthetics.

Chitosan is widely investigated for biomedical applications due to its excellent properties, such as biocompatibility, biodegradability, bioadhesivity, antibacterial, etc. In the field of neural engineering, it has been extensively studied in forms of film and hydrogel, and has been used as scaffolds for nerve regeneration in the peripheral nervous system and spinal cord. One of the main issues in neural engineering is the incapability of neuron to attach on biomaterials. The present study, from a new aspect, aims to take advantage of the bio-adhesive property of chitosan to develop chitosan-based materials for neural engineering, specifically in the fields of brain repair and neuroprosthetics. Neuronal responses to the developed biomaterials will also be investigated and discussed.In the first part of this study (Chapter II), chitosan was blended with a well-studied hydrogel material (agarose) to form a simply prepared hydrogel system. The stiffness of the agarose gel was maintained despite the inclusion of chitosan. The structure of the blended hydrogels was characterized by light microscopy and scanning electron microscopy. In vitro cell studies revealed the capability of chitosan to promote neuron adhesion. The concentration of chitosan in the hydrogel had great influence on neurite extension. An optimum range of chitosan concentration in agarose hydrogel, to enhance neuron attachment and neurite extension, was identified based on the results. A “steric hindrance” effect of chitosan was proposed, which explains the origin of the morphological differences of neurons in the blended gels as well as the influence of the physical environment on neuron adhesion and neurite outgrowth. This chitosan-agarose (C-A) hydrogel system and its multi-functionality allow for applications of simply prepared agarose-based hydrogels for brain tissue repair.In the second part of this study (Chapter III), chitosan was blended with graphene to form a series of graphene-chitosan (G-C) nanocomposites for potential neural interface applications. Both substrate-supported coatings and free standing films could be prepared by air evaporation of precursor solutions. The electrical conductivity of graphene was maintained after the addition of chitosan, which is non-conductive. The surface characteristic of the films was sensitively dependent on film composition, and in turn, influenced neuron adhesion and neurite extension. Biological studies showed good cytocompatibility of graphene for both fibroblast and neuron. Good cell-substrate interactions between neurons and G-C nanocomposites were found on samples with appropriate compositions. The results suggest this unique nanocomposite system may be a promising substrate material used for the fabrication of implantable neural electrodes. Overall, these studies confirmed the bio-adhesive property of chitosan. More importantly, the developed chitosan-based materials also have great potential in the fields of neural tissue engineering and neuroprosthetics.

Epilepsy is a neurological disorder characterized by recurrent seizures. Sleep problems can cooccur with epilepsy, and adversely affect seizure diagnosis and treatment. In fact, the relationship between sleep and seizures in individuals with epilepsy is a complex one. Seizures disturb sleep and sleep deprivation aggravates seizures. Antiepileptic drugs may also impair sleep quality at the cost of controlling seizures. In general, particular vigilance states may inhibit or facilitate seizure generation, and changes in vigilance state can affect the predictability of seizures. A clear understanding of sleep-seizure interactions will therefore benefit epilepsy care providers and improve quality of life in patients. Notable progress in neuroscience research—and particularly sleep and epilepsy—has been achieved through experimentation on animals. Experimental models of epilepsy provide us with the opportunity to explore or even manipulate the sleep-seizure relationship in order to decipher different aspects of their interactions. Important in this process is the development of techniques for modeling and tracking sleep dynamics using electrophysiological measurements. In this dissertation experimental and computational approaches are proposed for modeling vigilance dynamics and their utility demonstrated in nonepileptic control mice. The general framework of hidden Markov models is used to automatically model and track sleep state and dynamics from electrophysiological as well as novel motion measurements. In addition, a closed-loop sensory stimulation technique is proposed that, in conjunction with this model, provides the means to concurrently track and modulate 3 vigilance dynamics in animals. The feasibility of the proposed techniques for modeling and altering sleep are demonstrated for experimental applications related to epilepsy. Finally, preliminary data from a mouse model of temporal lobe epilepsy are employed to suggest applications of these techniques and directions for future research. The methodologies developed here have clear implications the design of intelligent neuromodulation strategies for clinical epilepsy therapy.

Learning is central to infusing intelligence to any biologically inspired system. This study introduces a novel Cross-Correlated Delay Shift (CCDS) learning method for spiking neurons with the ability to learn and reproduce arbitrary spike patterns in a supervised fashion with applicability tospatiotemporalinformation encoded at the precise timing of spikes. By integrating the cross-correlated term,axonaland synapse delays, the CCDS rule is proven to be both biologically plausible and computationally efficient. The proposed learning algorithm is evaluated in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. The results indicate that the proposed CCDS learning rule greatly improves classification accuracy when compared to the standards reached with the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. Network structureis the crucial partforany application domain of Artificial Spiking Neural Network (ASNN). Thus, temporal learning rules in multilayer spiking neural networks are investigated. As extensions of single-layer learning rules, the multilayer CCDS (MutCCDS) is also developed. Correlated neurons are connected through fine-tuned weights and delays. In contrast to the multilayer Remote Supervised Method (MutReSuMe) and multilayertempotronrule (MutTmptr), the newly developed MutCCDS shows better generalization ability and faster convergence. The proposed multilayer rules provide an efficient and biologically plausible mechanism, describing how delays and synapses in the multilayer networks are adjusted to facilitate learning. Interictalspikes (IS) aremorphologicallydefined brief events observed in electroencephalography (EEG) records from patients with epilepsy. The detection of IS remains an essential task for 3D source localization as well as in developing algorithms for seizure prediction and guided therapy. In this work, we present a new IS detection method using the Wavelet Encoding Device (WED) method together with CCDS learning rule and a specially designed Spiking Neural Network (SNN) structure. The results confirm the ability of such SNN to achieve good performance for automatically detecting such events from multichannel EEG records.

Surface electromyography records the motor unit action potential signals in the vicinity of the electrode to reveal information on muscle activation. Decomposition of sEMG signals for characterization of constituent motor unit action potentials in terms of amplitude and firing times is useful for clinical research as well as diagnosis of neurological disorders. Successful decomposition of sEMG signals would allow for pertinent motor unit action potential information to be acquired without discomfort to the subject or the need for a well-trained operator (compared with intramuscular EMG). To determine amplitudes and firing times for motor unit action potentials in an sEMG recording, Szlavik's perturbation based decomposition may be applied. The decomposition was initially applied to synthetic sEMG signals and then to experimental data collected from the biceps brachii. Szlavik's decomposition estimator yields satisfactory results for synthetic and experimental sEMG signals with reasonable complexity.

Since the 1900s, the number of deaths attributable to cardiovascular disease has steadily risen. With the advent of antihypertensive drugs and non-invasive surgical procedures, such as intravascular stenting, these numbers have begun to level off. Despite this trend, the number of patients diagnosed with some form of cardiovascular disease has only increased. By 2030, prevalence of coronary heart disease is expected to increase approximately by 18% in the United States. By 2050, prevalence of peripheral arterial occlusive disease is expected to increase approximately by 98% in the U.S. No single drug or surgical intervention offers a complete solution to these problems. Thus, a multi-faceted regimen of lifestyle changes, medication, and device or surgical interventions is usually necessary. A potential adjunct therapy and cost-effective solution for treating cardiovascular disease that has been overlooked is neurostimulation. Recent studies show that using neurostimulation techniques, such as transcutaneous electrical nerve stimulation (TENS), can help to reduce ischemic pain, lower blood pressure, increase blood flow to the periphery, and decrease systemic vascular resistance. The mechanisms by which these hemodynamic changes occur is still under investigation. The primary aim of this thesis is to elucidate these mechanisms through a thorough synthesis of the existing literature on this subject. Neurostimulation, specifically TENS, is thought to modulate both the metaboreflex and norepinephrine release from sympathetic nerve terminals. To test the hypothesis that TENS increases local blood flow, decreases mean arterial pressure, and decreases cutaneous vascular resistance compared to placebo, in which the electrodes are attached but no electrical stimulation is applied, a protocol was developed to test the effect of neurostimulation on healthy subjects. Implementation of this protocol in a pilot study will determine if neurostimulation causes significant changes in blood flow using the most relevant perfusion measurement instrumentation. Before conducting this study, pre-pilot comparison studies of interferential current therapy (IFC) versus TENS, low frequency (4 Hz) TENS versus high frequency (100 Hz) TENS, and electrode placement on the back versus the forearm were conducted. The only statistically significant difference found was that the application of IFC on the back decreased the reperfusion time, meaning that the time required to reach the average baseline perfusion unit value after occlusion decreased. Further pre-pilot work investigating these different modalities and parameters is necessary to ensure that favorable hemodynamic changes can be detected in the pilot study.

This dissertation introduces a novel approach at gauging patterns of informa- tion flow using brain connectivity analysis and partial directed coherence (PDC) in epilepsy. The main objective of this dissertation is to assess the key characteristics that delineate neural activities obtained from patients with epilepsy, considering both focal and generalized seizures. The use of PDC analysis is noteworthy as it es- timates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and it ascertains the coefficients as weighted measures in formulating the multivariate autoregressive model (MVAR). The PDC is used here as a feature extraction method for recorded scalp electroencephalograms (EEG) as means to examine the interictal epileptiform discharges (IEDs) and reflect the phys- iological changes of brain activity during interictal periods. Two experiments were set up to investigate the epileptic data by using the PDC concept. For the investigation of IEDs data (interictal spike (IS), spike and slow wave com- plex (SSC), and repetitive spikes and slow wave complex (RSS)), the PDC analysis estimates the intensity and direction of propagation from neural activities gener- ated in the cerebral cortex, and analyzes the coefficients obtained from employing MVAR. Features extracted by using PDC were transformed into adjacency matrices using surrogate data analysis and were classified by using the multilayer Perceptron (MLP) neural network. The classification results yielded a high accuracy and pre- cision number. The second experiment introduces the investigation of intensity (or strength) of information flow. The inflow activity deemed significant and flowing from other regions into a specific region together with the outflow activity emanating from one region and spreading into other regions were calculated based on the PDC results and were quantified by the defined regions of interest. Three groups were considered for this study, the control population, patients with focal epilepsy, and patients with generalized epilepsy. A significant difference in inflow and outflow validated by the nonparametric Kruskal-Wallis test was observed for these groups. By taking advantage of directionality of brain connectivity and by extracting the intensity of information flow, specific patterns in different brain regions of interest between each data group can be revealed. This is rather important as researchers could then associate such patterns in context to the 3D source localization where seizures are thought to emanate in focal epilepsy. This research endeavor, given its generalized construct, can extend for the study of other neurological and neurode- generative disorders such as Parkinson, depression, Alzheimers disease, and mental illness.