Understanding the complex information transfer process of our nervous system is one of the most urgent needs in the biomedical community. Neuromodulation is a technique that can artificially influence or modulate the activity of the target neurons. It's an inevitable tool in both the neuroscience study but also the clinical treatment of neurological diseases. The conventional method for neural modulation is the electrical stimulation using implantable electrodes. However, its intrinsic current leakage problem is an obstacle for further improving its performance in clinical scenarios because of the finite spatial resolution and recording artifacts. In general, an ideal method should be able to modulate neural activities with a high spatial, temporal and functionality specificity but without biocompatibility and reliability issues even in long term. Photoacoustic stimulation is an emerging light-mediated, non-genetic neural modulation method with high spatiotemporal resolution. Multiple devices have been designed in the past few years. But there are still several gaps to be filled to further expand its applications. One is the material mismatch, and another is that more function is needed, for example the capability of simultaneous recording. My research focused on the design and development of two new types of photoacoustic materials to expand the use of photoacoustic stimulation. A soft hydrogel film and a multifunctional fiber-based emitter for photoacoustic neuromodulation have been developed in my Ph.D. research. The study on these materials increased our knowledge to photoacoustic neurostimulation, also help us to investigate the effect of photoacoustic neuromodulation in the treatment of neurological and neurodegenerative diseases.

<p>Brain Machine Interfaces (BMI) strive to restore motor and sensory functions lost due to paralysis, amputation, and neurological diseases by interfacing brain circuitry to external actuators in form of a cursor on a computer screen or a robotic limb. There is a strong clinical need for sensory restoration as lack of somatosensory feedback leads to loss of fine motor control and one of the most common preferences for improvements according to individuals with upper-limb loss is the ability to require less visual attention to perform certain functions and to have a better control of wrist movement. One way to restore sensory functions is using electrical microstimulation of brain sensory areas as an artificial sensory channel; however, the ways of creating such artificial sensory inputs are poorly understood. </p><p>This dissertation presents the use of intracortical microstimulation (ICMS) to the primary somatosensory cortex (S1) to guide exploratory arm movements without visual feedback. Two rhesus monkeys were chronically implanted with multielectrode arrays in S1 and primary motor cortex (M1). The monkeys used a hand-held joystick to reach targets with a cursor on a computer screen. ICMS patterns were delivered to S1 when the cursor was placed over the target, mimicking the sense of touch. After the target or the cursor was made invisible, monkeys relied on ICMS feedback instead of vision to perform the task. For an invisible cursor, a random offset was added to the position of the invisible cursor to rule out the possibility that monkeys relied on joystick position felt through proprioception. Learning to perform these tasks was accompanied by changes in both the parameters of arm movements and representation of those parameters by M1 and S1 neurons at a population and individual neuronal levels. </p><p>Offline decoding of single neurons and population of neurons showed that overlapping, but not identical subpopulations of neurons represented movements when ICMS provided feedback instead of vision.</p><p>These results suggest that ICMS could be used as an essential source of sensation from prosthetic limbs.</p> / Dissertation

<p>Brain-machine interfaces (BMIs) use recordings from the nervous system to extract volitional and motor parameters for controlling external actuators, such as prosthetics, thereby bypassing or replacing injured tissue. As such, they show enormous promise for restoring mobility, dexterity, or communication in paralyzed patients or amputees. Recent advancements to the BMI paradigm have made the brain -- machine communication channel bidirectional, enabling the prosthetic to inform the user about touch, temperature, strain, or other sensory information; these devices are hence called brain-machine-brain interfaces (BMBIs). </p><p>In the first chapter an intraoperative BMI is investigated in human patients undergoing surgery for implantation of a deep brain stimulation (DBS) treatment electrodes. While the BMI was marginally effective, we found high levels of behavioral and tremor tuning among cells recorded from the surgical targets, the subthalamic nucleus (STN) and ventral intermediate nucleus (VIM) of the thalamus. Notably, this tremor or behavior tuning was not mutually exclusive with oscillatory behavior, suggesting that physiological tuning persists even in the face of pathological oscillations. We then used nonlinear means for extracting tremor tuning, and found a significant population, consistent with double-frequency or co-modulation to tremor within the basal ganglia. Synchrony was then assessed over long and short timescales between pairs of neurons, and it was found that tremor tuning implies synchrony: all units exhibiting tremor tuning showed synchrony to at least one other unit. </p><p>BMBIs rely on a host of both scientific knowledge and technology for effective function, and this technology is currently in intensive research. In this dissertation two technologies for BMBIs, corresponding to the two directions of communication, are designed, described, and tested. The first one is a high compliance, digitally controlled, high-side current-regulated microstimulator for intracortical microstimulation (ICMS). The device is validated on the bench, tested in monkeys, and used for multiple experimental setups. Due to careful control of parasitic charge injection, the microstimulator is ideally suited for interleaving stimulation and recording as employed in some BMBIs. </p><p>The second technology described is a wireless, scalable, 128 channel neural recording system. The device features aggressive digital filtering to maximize signal quality, has spike sorting and compression on the transceiver, can be fully configured over the air through a custom wireless bridge and client software, and can run for over 30 hours on one battery. This system has been tested in a monkey while in its home cage, where the wireless system permitted unfettered, continuous recording and continuous access to a simplified BMI. A full description of the development and device is described, as well as results showing convincing 1D and suggestive 2D BMI control.</p> / Dissertation

La mémoire spatiale est composée d'une phase d''encodage pendant l'éveil, suivi par une phase de consolidation pendant le sommeil, au cours de laquelle les séquences d'activation des cellules de lieu sont rejouées. Ces réactivations ont lieu pendant des oscillations à hautes fréquences du sommeil à ondes lentes, appelées les sharp-wave ripples (SPW-Rs) dont l'occurrence est coordonnées avec celle des autres rythmes corticaux (ondes delta 2-4Hz et spindles 10-15Hz). Ce modèle bien que largement accepté ne repose que sur des études corrélatives. De plus, les SWP-Rs et les ondes delta sont impliquées à la fois dans l'homéostasie du sommeil et dans la consolidation de la mémoire. Or l'interaction entre les deux phénomènes n'a jamais été caractérisée. Au moyen d'une interface cerveau machine, nous avons associé les réactivations spontanées d'une cellule de lieu pendant le sommeil à une stimulation électrique de récompense. Au réveil les souris allaient dans le champ de lieu du neurone démontrant la possibilité de créer des souvenirs artificiels pendant le sommeil. Ceci démontrait également le rôle causal des cellules de lieu dans la navigation spatiale ainsi que l'existence de réactivations d'informations spatiales pendant le sommeil. Dans un second temps, nous avons développé une deuxième interface cerveau-machine permettant de manipuler les ondes delta. Nous avons également montré que l'occurrence des SPW-Rs et des ondes delta diminuent avec le temps de sommeil en maintenant leur coordination. Enfin nous avons identifié une sous-population de neurones corticaux potentiellement impliquée dans la génération des ondes delta et leur régulation par la pression homéostatique de sommeil. / Spatial memory is composed of an encoding phase during wakefulness, followed by a consolidation phase during sleep, corresponding to the replay of sequences of activation of hippocampal place cells observed during wake. Those reactivations occur during slow wave sleep, mostly during hippocampal high frequency oscillations, called sharp-wave ripples (SPW-Rs). Moreover, SPW-Rs occurrence is coordinated with others cortical rhythms (delta waves 2-4Hz and spindles 10-15Hz). Although this theoretical framework is widely accepted, it is only based on correlative studies. Moreover, in addition to memory consolidation, SPW-Rs and delta waves are also involved in sleep homeostasis. Finally, a fine description of the interactions between the two phenomena is still lacking. By using a newly designed brain machine interface, we associated spontaneous reactivations of a single place cell during sleep to intracranial rewarding stimulations. At awakening, mice went and stayed within the place field of the related neuron, demonstrating the possibility to create artificial memories during sleep. It also demonstrated the causal role of place cells on spatial navigation, and that they still convey spatial information during sleep supporting the existence of sleep reactivation. We also developed a second brain machine interface in order to manipulate delta waves during sleep. We showed that the occurrence of both SPW-Rs and delta waves decrease during sleep, even though their coordination was maintained. Finally, we identify a sub-population of cortical neurons potentially involved both in the generation of delta waves and their modulation by the homeostatic pressure of sleep.

Silicon carbide (SiC) has been used for centuries as an industrial abrasive and has been actively researched since the 1960's as a robust material for power electronic applications. Despite being the first semiconductor to emit blue light in 1907, it has only recently been discovered that the material has crucial properties ideal for long-term, implantable biomedical devices. This is due to the fact that the material offers superior biocompatibility and hemocompatibility while providing rigid mechanical and chemical stability. In addition, the material is a wide-bandgap semiconductor that can be used for optoelectronics, light delivery, and optical sensors, which is the focus of this dissertation research. In this work, we build on past accomplishments of the USF-SiC Group to develop active SiC-based Brain Machine Interfaces (BMIs) and develop techniques for coating other biomaterials with amorphous SiC (a-SiC) to improve device longevity. The work is undertaken to move the state of the art in in vivo biomedical devices towards long term functionality. In this document we also explore the use of SiC in other bio photonics work, as demonstrated by the creation of the first reported photosensitive capacitor in semi-insulating 4H-SiC, thus providing the mechanism for a simple, biocompatible, UV sensor that may be used for biomedical applications. Amorphous silicon carbide coatings are extremely useful in developing agile biomaterial strategies. We show that by improving current a-SiC technology we provide a way that SiC biomaterials can coexist with other materials as a biocompatible encapsulation strategy. We present the development of a plasma enhanced chemical vapor deposition (PECVD) a-SIC process and include material characterization analysis. The process has shown good adhesion to a wide variety of substrates and cell viability tests confirm that it is a highly biocompatible coating whereby it passed the strict ISO 10993 standard tests for biomaterials and biodevices. In related work, we present a 64-channel microelectrode array (MEA) fabricated on a cubic 3C-SiC polytype substrate as a preliminary step in making more complex neurological devices. The electrode-electrolyte system electrical impedance is studied, and the device is tested against the model. The system is wire-bonded and packaged to provide a full neural test bed that will be used in future work to compare substrate materials during long-term testing. Expanding on this new MEA technology, we then use 3C-SiC to develop an active, implantable, BMI interface. New processes were developed for the dry etching of SiC neural probes. The developed 7 mm long implantable devices were designed to offer four channels of single-unit electrical recording with concurrent optical stimulation, a combination of device properties that is indeed at the state-of-the-art in neural probes at this time. Finally, work in SiC photocapacitance is presented as it relates to radio-frequency tuning circuits as well as bio photonics. A planar geometry UV tunable photocapacitor is fabricated to demonstrate the effect of below-bandgap optical tuning. The device can be used in a number of applications ranging from fluorescence sensing to the tuning of antennas for low-power communications. While technology exists for a wide variety of in vivo interfaces and sensors, few active devices last in the implantable environment for more than a few months. If these devices are going to reach a long-term implant capability, use of better materials and processing strategies will need to be developed. Potential devices and strategies for harnessing the SiC materials family for this very important application are reviewed and presented in this dissertation to serve as a possible roadmap to the development of advanced SiC-based biomedical devices.

<p>Brain-machine interfaces (BMIs) use neuronal activity to control external actuators. As such, they show great promise for restoring motor and communication abilities in persons with paralysis or debilitating neurological disorders.</p><p>While BMIs aim to enact normal sensorimotor functions, so far they have lacked afferent feedback in the form of somatic sensation. This deficiency limits the utility of current BMI designs and may hinder the translation of future clinical BMIs, which will need a means of delivering sensory signals from prosthetic devices back to the user. </p><p>This dissertation describes the development of brain-machine-brain interfaces (BMBIs) capable of bidirectional communication with the brain. The interfaces consisted of efferent and afferent modules. The efferent modules decoded motor intentions from the activity of populations of cortical neurons recorded with chronic multielectrode recording arrays. The activity of these ensembles was used to drive the movements of a computer cursor and a realistic upper-limb avatar. The afferent modules encoded tactile feedback about the interactions of the avatar with virtual objects through patterns of intracortical microstimulation (ICMS).</p><p>I first show that a direct intracortical signal can be used to instruct rhesus monkeys about the direction of a reach to make with a BMI. Rhesus monkeys placed an actuator over an instruction target and obtained, from the target's artificial texture, information about the correct reach path. Initially these somatosensory instructions took the form of vibrotactile stimulation of the hands. Next, ICMS of primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PPC) in another was substituted for this peripheral somatosensory signal. Finally, the monkeys made direct brain-controlled reaches using the activity of ensembles of primary motor cortex (M1) cells, conditional on the ICMS cues. The monkey receiving ICMS of S1 was able to achieve the same level of proficiency with ICMS as with the stimulus delivered to the skin of the hand. The monkey receiving ICMS of PPC was unable to perform the task above chance. This experiment indicates that ICMS of S1 can form the basis of an afferent prosthetic input to the brain for guiding brain-controlled prostheses.</p><p>I next show that ICMS of S1 can provide feedback about the interactions of a virtual-reality upper-limb avatar and virtual objects, enabling active touch. Rhesus monkeys initially controlled the avatar with the movements of their arms and used it to search through sets of up to three objects. Feedback in the form of temporal patterns of ICMS occurred whenever the avatar touched a virtual object. Monkeys learned to use this feedback to find the objects with particular artificial textures, as encoded by the ICMS patterns, and select those associated with reward while avoiding selecting the non-rewarded objects. Next, the control of the avatar was switched to direct brain-control and the monkeys continued to move the avatar with motor commands derived from the extracellular neuronal activity of M1 cells. The afferent and efferent modules of this BMBI were temporally interleaved, and as such did not interfere with each other, yet allowed effectively concurrent operation. Cortical motor neurons were measured while the monkey passively observed the movements of the avatar and were found to be modulated, a result that suggests that concurrent visual and artificial somatosensory feedback lead to the incorporation of the avatar into the monkey's internal brain representation.</p><p>Finally, I probed the sensitivity of S1 to precise temporal patterns of ICMS. Monkeys were trained to discriminate between periodic and aperiodic ICMS pulse trains. The periodic pulse-trains consisted of 200 Hz bursts at a 10 Hz secondary frequency. The aperiodic pulse trains had a distorted periodicity and consisted of 200 Hz bursts at a variable instantaneous secondary frequency. The statistics of the aperiodic pulse trains were drawn from a gamma distribution with equal mean inter-burst intervals to the periodic pulse trains. The monkeys were able to distinguish periodic pulse trains from aperiodic pulse trains with coefficients of variation of 0.25 or greater. This places an upper-bounds on the communication bandwidth that can be achieved with a single channel of temporal ICMS in S1.</p><p>In summary, rhesus monkeys were augmented with a bidirectional neural interface that allowed them to make reaches to objects and discriminate them by their textures--all without making actual movements and without relying on somatic sensation from their real bodies. Both action and perception were mediated by the brain-machine-brain interface. I probed the sensitivity of the afferent leg of the interface to precise temporal patterns of ICMS. Moreover, I describe evidence that the BMBI controlled avatar was incorporated into the monkey's internal brain representation. These results suggest that future clinical neuroprostheses could implement realistic feedback about object-actuator interactions through patterns of ICMS, and that these artificial somatic sensations could lead to the incorporation of the prostheses into the user's body schema.</p> / Dissertation

<p>The loss of the ability to walk as the result of neurological injury or disease critically impacts the mobility and everyday lifestyle of millions. The World Heath Organization (WHO) estimates that approximately 1% of the world's population needs the use of a wheelchair to assist their personal mobility. Advances in the field of brain-machine interfaces (BMIs) have recently demonstrated the feasibility of using neuroprosthetics to extract motor information from cortical ensembles for more effective control of upper-limb replacements. However, the promise of BMIs has not yet been brought to bear on the challenge of restoring the ability to walk. A future neuroprosthesis designed to restore walking would need two streams of information flowing between the user's brain and the device. First, the motor control signals would have to be extracted from the brain, allowing the robotic prosthesis to behave in the manner intended by the user. Second, and equally important would be the flow of sensory and proprioceptive information back to the user from the neuroprosthesis. Here, I contribute to the foundation of such a bi-directional brain machine interface for the restoration of walking in a series of experiments in two animal models, designed to show the feasibility of (1) extracting locomotor information from neuronal ensemble activity and (2) sending information back into the brain via cortical microstimulation. </p><p>In a set of experiments designed to investigate the extraction of locomotor parameters, I chronically recorded from ensembles of neurons in primary motor (M1) and primary somatosensory (S1) cortices in two adult female rhesus macaques as they walked bipedally, at various speeds, both forward and backward on a custom treadmill. For these experiments, rhesus monkeys were suitable because of their ability to walk bipedally in a naturalistic manner with training. I demonstrate that the kinematics of bipedal walking in rhesus macaques can be extracted from neuronal ensemble recordings, both offline and in real-time. The activity of hundreds of neurons was processed by a series of linear decoders to extract accurate predictions of leg joints in three dimensional space, as well as leg muscle electromyograms (EMGs). Using a multi-layered switching model allowed us to achieve increased extraction accuracy by segregating different behavioral modes of walking.</p><p>In a second set of experiments designed to investigate the usage of microstimulation as a potential artificial sensory channel, I instructed two adult female Aotus trivirgatus (owl monkeys) about the location of a hidden food reward using a series of cortical microstimulation patterns delivered to primary somatosensory (S1) cortex. The owl monkeys discriminated these microstimulation patterns and used them to guide reaching movements to one of two targets. Here, owl monkeys were used which were previously implanted with electrode arrays of high longevity and stability. These monkeys were previously trained on a somatosensory cued task, which allowed a quick transition to microstimulation cueing. The owl monkeys learned to interpret microstimulation patterns, and their skill and speed of learning new patterns improved over several months. Additionally, neuronal activity recorded on non-stimulated electrodes in motor (M1), premotor (PMD) and posterior parietal (PP) cortices allowed us to examine the immediate neural responses to single biphasic stimulation pulses as well as overall responses to the spatiotemporal pattern. Using this recorded neuronal activity, I showed the efficacy of several linear classification algorithms during microstimulation. </p><p>These results demonstrate that locomotor kinematic parameters can be accurately decoded from the activity of neuronal ensembles, that multichannel microstimulation is a viable information channel for sensorized prosthetics, and that the technical limitations of combining these techniques can be overcome. I propose that bi-directional BMIs integrating these techniques will one day restore the ability to walk to severely paralyzed patients.</p> / Dissertation

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.

A brain-machine interface (BMI) uses signals from the brain to control electronic devices. One application of this technology is the control of assistive devices to facilitate movement after paralysis. Ideally, the BMI would identify an intended movement and control an assistive device to produce the desired movement. To implement such a system, it is necessary to identify different movements involving a single limb and users must be able to issue commands at any instant instead of only during specific time windows determined by the BMI itself. A novel processing technique to identify voluntary movements using only four electrodes is presented. Histograms containing the spectral components of intracranial neural signals displaying power changes correlated with movement were unique for each of three movements performed with one limb. Off-line classification of the histograms allowed the identification of the performed movement with an accuracy of 89%. This movement identification system was interfaced with a neuroprosthesis for grasping, fitted to a tetraplegic individual. The user pressed a button triggering the random selection and classification of a brain signal previously recorded intracranially from a different person while performing specific arm movements. Correct identification of the movement triggered grasping functions. Movement identification accuracy was 94% allowing successful operation of the neuroprosthesis. Finally, two BMIs for the real-time asynchronous control of two-dimensional movements were created using a single electrode. One EEG-based system was tested by a healthy participant. A second system was implemented and tested using recordings from an individual undergoing clinical intracranial electrode implantation. The users modulated their 7 Hz-13 Hz oscillatory rhythm through motor imagery. A power decrease below a threshold activated a ``brain-switch''. This switch was coupled with a novel asynchronous control strategy to control a miniature remotely-controlled vehicle as well as a computer cursor. Successful operation of the EEG system required 6 hrs of training. ECoG control was achieved after only 15 minutes. The operation of the BMI was simple enough to allow users to focus on the task at hand rather than on the actual operation of the BMI.

A brain-machine interface (BMI) uses signals from the brain to control electronic devices. One application of this technology is the control of assistive devices to facilitate movement after paralysis. Ideally, the BMI would identify an intended movement and control an assistive device to produce the desired movement. To implement such a system, it is necessary to identify different movements involving a single limb and users must be able to issue commands at any instant instead of only during specific time windows determined by the BMI itself. A novel processing technique to identify voluntary movements using only four electrodes is presented. Histograms containing the spectral components of intracranial neural signals displaying power changes correlated with movement were unique for each of three movements performed with one limb. Off-line classification of the histograms allowed the identification of the performed movement with an accuracy of 89%. This movement identification system was interfaced with a neuroprosthesis for grasping, fitted to a tetraplegic individual. The user pressed a button triggering the random selection and classification of a brain signal previously recorded intracranially from a different person while performing specific arm movements. Correct identification of the movement triggered grasping functions. Movement identification accuracy was 94% allowing successful operation of the neuroprosthesis. Finally, two BMIs for the real-time asynchronous control of two-dimensional movements were created using a single electrode. One EEG-based system was tested by a healthy participant. A second system was implemented and tested using recordings from an individual undergoing clinical intracranial electrode implantation. The users modulated their 7 Hz-13 Hz oscillatory rhythm through motor imagery. A power decrease below a threshold activated a ``brain-switch''. This switch was coupled with a novel asynchronous control strategy to control a miniature remotely-controlled vehicle as well as a computer cursor. Successful operation of the EEG system required 6 hrs of training. ECoG control was achieved after only 15 minutes. The operation of the BMI was simple enough to allow users to focus on the task at hand rather than on the actual operation of the BMI.