Electrical potentials are the fundamental currency of communication in the nervous system. The advanced executive functions of the prefrontal cortex and the motor commands delivered to the neuromuscular junction, though involved with very different aspects of behavior, both rely on time-varying electrical signals. It is possible to "listen to" the internal communications of the nervous system by measuring the electrical potentials in the extra-cellular space. However, this is only meaningful if there is some way to interpret these signals, which are incredibly complicated and information rich. This dissertation represents an attempt to decode some of these signals in order to reveal their significance for behavior and function. The first study is an investigation of the relationship between different elements of the local field potential in the prefrontal cortex and memory consolidation. It is shown that certain electrographic signatures of non-rapid eye movement sleep, namely K-complexes and low-voltage spindles, are correlated with neuronal replay of recent experiences. It is also shown that the global fluctuations of activity in the population of cells, known as up/down states, is correlated with neuronal replay. Finally, it is shown that high-voltage spindles are not correlated with memory replay, and are therefore functionally different from low-voltage spindles. The second study focuses on the relationship between movements of the upper limb and the coordinated neural control, as measured by the electromyogram (EMG), of the muscles generating that movement. We show that different probability-based models can be used to predict what the pattern of EMG in the different muscles will be for any given kinematic state of the hand. In the third study it is demonstrated that the kinematic output associated with a particular pattern of EMG can be reproduced with electrical stimulation. Thus, it is not only possible to understand the commands issued by the nervous system, it is also possible to issue commands by interfacing with the nervous system directly. Finally, the design for an experiment that would combine EMG prediction with translation of EMG into electrical stimulus patterns is presented. The objective of this study would be to use these methods to fully control the upper limb in a way that would be useful for a functional electrical stimulation-based neuroprosthetic for spinal cord injured patients.

The skin-implant interface of percutaneous devices is generally weak and can fail when excessive loading disrupts the sealing of the interface by dermal and epidermal cells and tissue. As such, the formation of a stable implant-skin junction is a major factor in determining percutaneous implant success. In this study, we used functionalized self-assembled monolayers (SAMs) with discrete surface properties as model systems to assess the effects of biomaterial surface properties on controlling fibronectin (FN) adsorption and keratinocyte spreading and adhesion. The surface properties investigated were charge (positive and negative) and wettability (hydrophobic and hydrophilic). Gold slides prepared with SAMs were incubated with FN overnight. The cell binding sites were quantified on each surface using an antibody that targets the synergy binding site of the cell binding domains (HFN7.1) and the topography of the FN on the surfaces was evaluated with atomic force microscopy. The topography data demonstrated that the availability of cell binding domains is dependent on surface-mediated FN binding orientation. Cell spreading was assessed using a lipid membrane stain, maleimide. The cells were imaged by fluorescence microscopy and the cell area calculated. The percentage of cell adhesion was determined using a centrifugal force assay. Both keratinocyte assays suggested that the charge of the surface was the prominent factor in determining cell function on the surface over the surface wettability. The findings of this study strongly suggest that a positively charged implant surface with a FN coating will enhance the strength of the cutaneous seal around percutaneous implants over an unmodified surface.

<p>Brain-machine interfaces (BMIs) offer the potential to assist millions of people worldwide suffering from immobility due to loss of limbs, paralysis, and neurodegenerative diseases. BMIs function by decoding neural activity from intact cortical brain regions in order to control external devices in real-time. While there has been exciting progress in the field over the past 15 years, the vast majority of the work has focused on restoring of motor function of a single limb. In the work presented in this thesis, I first investigate the expanded role of primary sensory (S1) and motor (M1) cortex during reaching movements. By varying target size during reaching movements, I discovered the cortical correlates of the speed-accuracy tradeoff known as Fitts' law. Similarly, I analyzed cortical motor processing during tasks where the motor plan is quickly reprogrammed. In each study, I found that parameters relevant to the reach, such as target size or alternative movement plans, could be extracted by neural decoders in addition to simple kinematic parameters such as velocity and position. As such, future BMI functionality could expand to account for relevant sensory information and reliably decode intended reach trajectories, even amidst transiently considered alternatives.</p><p> The second portion of my thesis work was the successful development of the first bimanual brain-machine interface. To reach this goal, I expanded the neural recordings system to enable bilateral, multi-site recordings from approximately 500 neurons simultaneously. In addition, I upgraded the experiment to feature a realistic virtual reality end effector, customized primate chair, and eye tracking system. Thirdly, I modified the tuning function of the unscented Kalman filter (UKF) to conjointly represent both arms in a single 4D model. As a result of widespread cortical plasticity in M1, S1, supplementary motor area (SMA), and posterior parietal cortex (PPC), the bimanual BMI enabled rhesus monkeys to simultaneously control two virtual limbs without any movement of their own body. I demonstrate the efficacy of the bimanual BMI in both a subject with prior task training using joysticks and a subject naïve to the task altogether, which simulates a common clinical scenario. The neural decoding algorithm was selected as a result of a methodical comparison between various neural decoders and decoder settings. I lastly introduce a two-stage switching model with a classify step and predict step which was designed and tested to generalize decoding strategies to include both unimanual and bimanual movements.</p> / Dissertation

abstract: In the past decade, research on the motor control side of neuroprosthetics has steadily gained momentum. However, modern research in prosthetic development supplements a focus on motor control with a concentration on sensory feedback. Simulating sensation is a central issue because without sensory capabilities, the sophistication of the most advanced motor control system fails to reach its full potential. This research is an effort toward the development of sensory feedback specifically for neuroprosthetic hands. The present aim of this work is to understand the processing and representation of cutaneous sensation by evaluating performance and neural activity in somatosensory cortex (SI) during a grasp task. A non-human primate (Macaca mulatta) was trained to reach out and grasp textured instrumented objects with a precision grip. Two different textures for the objects were used, 100% cotton cloth and 60-grade sandpaper, and the target object was presented at two different orientations. Of the 167 cells that were isolated for this experiment, only 42 were recorded while the subject executed a few blocks of successful trials for both textures. These latter cells were used in this study's statistical analysis. Of these, 37 units (88%) exhibited statistically significant task related activity. Twenty-two units (52%) exhibited statistically significant tuning to texture, and 16 units (38%) exhibited statistically significant tuning to posture. Ten of the cells (24%) exhibited statistically significant tuning to both texture and posture. These data suggest that single units in somatosensory cortex can encode multiple phenomena such as texture and posture. However, if this information is to be used to provide sensory feedback for a prosthesis, scientists must learn to further parse cortical activity to discover how to induce specific modalities of sensation. Future experiments should therefore be developed that probe more variables and that more systematically and comprehensively scan somatosensory cortex. This will allow researchers to seek out the existence or non-existence of cortical pockets reserved for certain modalities of sensation, which will be valuable in learning how to later provide appropriate sensory feedback for a prosthesis through cortical stimulation. / Dissertation/Thesis / M.S. Bioengineering 2011

No description available.

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.

The goal of the study was to use electrical signals from primary motor cortex to generate accurate predictions of the movement onset time of performed movements, for potential use in asynchronous brain-machine interface (BMI) systems. Four subjects, two with electroencephalogram and two with electrocorticogram electrodes, performed various movements while activity from their primary motor cortices was recorded. An analysis program used several criteria (change point, fractal dimension, spectral entropy, sum of differences, bandpower, bandpower integral, phase, and variance), derived from the neural recordings, to generate predictions of movement onset time, which it compared to electromyogram activity onset time, determining prediction accuracy by receiver operating characteristic curve areas. All criteria, excepting phase and change-point analysis, generated accurate predictions in some cases.

The goal of the study was to use electrical signals from primary motor cortex to generate accurate predictions of the movement onset time of performed movements, for potential use in asynchronous brain-machine interface (BMI) systems. Four subjects, two with electroencephalogram and two with electrocorticogram electrodes, performed various movements while activity from their primary motor cortices was recorded. An analysis program used several criteria (change point, fractal dimension, spectral entropy, sum of differences, bandpower, bandpower integral, phase, and variance), derived from the neural recordings, to generate predictions of movement onset time, which it compared to electromyogram activity onset time, determining prediction accuracy by receiver operating characteristic curve areas. All criteria, excepting phase and change-point analysis, generated accurate predictions in some cases.

abstract: Robust and stable decoding of neural signals is imperative for implementing a useful neuroprosthesis capable of carrying out dexterous tasks. A nonhuman primate (NHP) was trained to perform combined flexions of the thumb, index and middle fingers in addition to individual flexions and extensions of the same digits. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon action potential firing rates. The effect of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis, and Mutual Information Maximization was compared based on SVM classification performance. SVM classification was used to examine the functional parameters of (i) efficacy (ii) endurance to simulated failure and (iii) longevity of classification. The effect of using isolated-neuron and multi-unit firing rates was compared as the feature vector supplied to the SVM. The best classification performance was on post-implantation day 36, when using multi-unit firing rates the worst classification accuracy resulted from features selected with Wilcoxon signed-rank test (51.12 ± 0.65%) and the best classification accuracy resulted from Mutual Information Maximization (93.74 ± 0.32%). On this day when using single-unit firing rates, the classification accuracy from the Wilcoxon signed-rank test was 88.85 ± 0.61 % and Mutual Information Maximization was 95.60 ± 0.52% (degrees of freedom =10, level of chance =10%) / Dissertation/Thesis / Masters Thesis Bioengineering 2015