Peripheral nerve stimulation (PNS) is commonly used to promote use-dependent cortical plasticity for rehabilitation of motor function in spinal cord injury. Pairing transcranial magnetic stimulation (TMS) with PNS has been shown to increase motor evoked potentials most when the two stimuli are timed to arrive in the cortex simultaneously. This suggests that a mechanism of timing-dependent plasticity (TDP) may be a more effective method of promoting motor rehabilitation. The following thesis is the result of applying a brain-computer interface to apply PNS in closed-loop simultaneously to movement intention onset as measured by EEG of the sensorimotor cortex to test whether TDP can be induced in incomplete spinal cord injured individuals with upper limb motor impairment. 4 motor incomplete SCI subjects have completed 12 sessions of closed-loop PNS delivered over 4-6 weeks. Benefit was observed for every subject although not consistently across metrics. 3 out of 4 subjects exhibited increased maximum voluntary contraction force (MVCF) between first and last interventions for one or both hands. TMS-measured motor map volume increased for both hemispheres in one subject, and TMS center of gravity shifted in 3 subjects consistent with studies in which motor function improved or was restored. These observations suggest that rehabilitation using similar designs for responsive stimulation could improve motor impairment in SCI.

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.

Compliant microelectrodes have been designed in a microcable geometry that can be used individually or in an array and either as a shank-style electrode or as a string-like electrode that can be threaded around features such as the peripheral nerve. The fabrication process, using spin-cast micromolding (SCuM), is simple and adaptable to different patterns. The microcables were fabricated using polydimethyl siloxane (PDMS) for the insulating substrate and thin-film gold for the conductive element. The thin, metal film and the low tensile modulus of the PDMS substrate created an electrode with a composite tensile modulus lower than other compliant electrodes described in the literature. The gold film increased the composite modulus approximately three-fold compared to the unaltered PDMS. The durability of the electrodes and tolerance for stretch was also tested. The microcables were found to be conductive up to 6% strain and to regain conductivity after release from multiple applications of 200% strain. The tolerance for high-strain shows that the electrodes can be deployed for use and stretched or pulled into place as needed without damaging the conductivity. The microcable electrode recording sites were electrically characterized using frequency-based impedance modeling and were tested for electrophysiological recording using a peripheral nerve preparation. A suitable insertion mechanism was designed to use the microcables as shank-style cortical electrodes. The microcables were coated on one side with fibrin, which, when dry, stiffens the microcables for insertion into cortical tissue. A 28-day implant study testing the inflammatory response to fibrin coated PDMS microcable electrodes showed a positive, but relatively low inflammatory response, as measured by glial fibrillary astrocytic protein (GFAP; indicating activated astrocytes) immediately at the tissue edge of the implant site. The response of the control, silicon shank-style electrodes, was varied, but also trended toward low levels of GFAP expression. The GFAP staining was possibly due to the clearance of the fibrin from the implant site in addition to the presence of the PDMS-based electrode. Implant studies extending beyond 28 days are necessary to determine whether and to what degree the inflammation persists at the implant site of PDMS-based electrodes.

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.

Le graphène, une couche monoatomique de carbone, est étudié comme matériau pourconstruire ou encapsuler des biocapteurs afin d’adresser les problèmes de durabilitérencontrés avec les implants intra-corticaux. Ces derniers sont des outils essentiels pour lesprojets médicaux de neuro-réhabilitation afin d’enregistrer les signaux de motoneuronesuniques dans le cerveau. Les implants actuels sont invasifs et leur efficacité est limitée dans letemps par la réaction de rejet des tissus. En combinant une synthèse de graphène optimiséeà cet usage (monocouche continue sur plusieurs cm²) et son intégration dans des capteursélectroniques ultra-sensibles, protégés par des polymères bioactifs, cette thèse propose unenouvelle approche pluridisciplinaire pour construire des implants offrant une meilleurebioacceptance. Au moyen de méthodes d’intégration innovantes et d’études ducomportement du graphène in-vivo et in-vitro, nous évaluons expérimentalement lafaisabilité d’intégration du graphène dans les futures interfaces cerveau machines pour desprojets médicaux au long terme. / Graphene, an atomically thin layer of carbon, is investigated as a biosensing andcoating material in order to address the long term durability issues of invasive intracorticalimplants. These devices are essential tools to record specific single motor neurons activity formedical applications aiming at healing neural injuries. Today’s implants suffer from their highinvasiveness. It is responsible for local inflammation that leads to the failure in unique neuronsactivity recordings in the motor cortex on a long term basis. By combining a monolayergraphene growth and transfer with an ultra-sensitive electronic integration and a biochemicalfunctionalization, this thesis proposes a new multidisciplinary approach to build intracorticalimplants with an improved bioacceptance. By using innovative methods of grapheneintegration in implants, and in-vitro and in-vivo studies to assess the reactions of living tissuesto graphene, we provide an overview of graphene’s potential contribution to future brainmachine interfaces for long term medical projects.

abstract: Brain-machine interfaces (BMIs) were first imagined as a technology that would allow subjects to have direct communication with prosthetics and external devices (e.g. control over a computer cursor or robotic arm movement). Operation of these devices was not automatic, and subjects needed calibration and training in order to master this control. In short, learning became a key component in controlling these systems. As a result, BMIs have become ideal tools to probe and explore brain activity, since they allow the isolation of neural inputs and systematic altering of the relationships between the neural signals and output. I have used BMIs to explore the process of brain adaptability in a motor-like task. To this end, I trained non-human primates to control a 3D cursor and adapt to two different perturbations: a visuomotor rotation, uniform across the neural ensemble, and a decorrelation task, which non-uniformly altered the relationship between the activity of particular neurons in an ensemble and movement output. I measured individual and population level changes in the neural ensemble as subjects honed their skills over the span of several days. I found some similarities in the adaptation process elicited by these two tasks. On one hand, individual neurons displayed tuning changes across the entire ensemble after task adaptation: most neurons displayed transient changes in their preferred directions, and most neuron pairs showed changes in their cross-correlations during the learning process. On the other hand, I also measured population level adaptation in the neural ensemble: the underlying neural manifolds that control these neural signals also had dynamic changes during adaptation. I have found that the neural circuits seem to apply an exploratory strategy when adapting to new tasks. Our results suggest that information and trajectories in the neural space increase after initially introducing the perturbations, and before the subject settles into workable solutions. These results provide new insights into both the underlying population level processes in motor learning, and the changes in neural coding which are necessary for subjects to learn to control neuroprosthetics. Understanding of these mechanisms can help us create better control algorithms, and design training paradigms that will take advantage of these processes. / Dissertation/Thesis / Doctoral Dissertation Bioengineering 2015