Development of a Unique Whole-Brain Model for Upper Extremity Neuroprosthetic Control

Neuroprostheses are at the forefront of upper extremity function restoration. However, contemporary controllers of these neuroprostheses do not adequately address the natural brain strategies related to planning, execution and mediation of upper extremity movements. These lead to restrictions in providing complete and lasting restoration of function. This dissertation develops a novel whole-brain model of neuronal activation with the goal of providing a robust platform for an improved upper extremity neuroprosthetic controller. Experiments (N=36 total) used goal-oriented upper extremity movements with real-world objects in an MRI scanner while measuring brain activation during functional magnetic resonance imaging (fMRI). The resulting data was used to understand neuromotor strategies using brain anatomical and temporal activation patterns. The study\u27s fMRI paradigm is unique and the use of goal-oriented movements and real-world objects are crucial to providing accurate information about motor task strategy and cortical representation of reaching and grasping. Results are used to develop a novel whole-brain model using a machine learning algorithm. When tested on human subject data, it was determined that the model was able to accurately distinguish functional motor tasks with no prior knowledge. The proof of concept model created in this work should lead to improved prostheses for the treatment of chronic upper extremity physical dysfunction

Neuroprosthetic rehabilitation and translational mechanism after severe spinal cord injury

Traumatic SCIs have long-term health, economic and social consequences, stressing the urgency to develop interventions to improve recovery after such injuries. Today, the only proven effective interventions to enhance recovery after SCI are activity-based rehabilitation therapies, such as locomotor training. However, locomotor training shows no or very limited efficacy to improve function after a severe SCI that induces paralysis of the limbs. To mimic the outcome of severe but incomplete SCI in rodents, we developed a model of double opposite-side lateral hemisections termed staggered hemisection in adult rats. This model induced permanent paralysis below the level of injury but leaves an intervening gap of intact neural tissue that provides a substrate for recovery. We showed that this SCI leads to degradation of motor functions, which correlates with the formation of aberrant neuronal connections below the lesion. Robotic devices with a rehabilitative purpose should act as propulsive or postural neuroprosthesis allowing training under natural conditions. Our versatile robotic interface provides multidirectional bodyweight support during overground locomotion in rats. We next evaluated the effects of robot-assisted gait training enabled by electrochemical stimulation of spinal circuits to restore locomotion after staggered hemisection SCI. We found that after two months of daily training, paralyzed rats recovered the ability to initiate, sustain and adjust bipedal locomotion while supported in the robot under electrochemical stimulation. This recovery correlated with ubiquitous reorganization of corticospinal, brainstem, and intraspinal fibers. We next evaluated whether this treatment was capable of restoring supraspinal control of locomotion after a clinically relevant SCI. Rats received a severe contusion of the spinal cord that spared less than 10% of intact tissue. Robot-assisted rehabilitation restored weight-bearing locomotion in all the trained rats when stimulated electrochemicallay and in a subset of rats in the absence of any enabling factors which paralelled with the reorganization of axonal projections of reticulospinal fibers below the contusion. Virus-mediated silencing of reticulospinal neurons projecting to lumbar segments demonstrated that these inputs were necessary to initiate and sustain walking after training. When delaying the onset of training by two months, in the chronic stage, all the rats regained voluntary locomotor movements but the extent of the recovery was reduced compared to rats trained early after SCI. The results provide a strong rationale to evaluate the impact of neuroprosthetic training to improve motor functions in human patients with incomplete SCI. Translation of treatment paradigms developed in rodent models into effective clinical applications remains a major challenge in biomedical research. Here, we studied recovery of motor functions in more than 400 quadriplegic patients who presented various degree of spinal cord damage laterality. We found that recovery increases with the asymmetry of early motor deficits. We conclude that emergence of spinal cord decussating corticospinal fibers and bilateral motor cortex projections during mammalian evolution supports greater recovery after lateralized SCI primates compared to rodents. Novel experimental models and dedicated therapeutic strategies are necessary to take advantage of this powerful neuronal substrate for recovery after SCI

Neuroprosthetic system to restore locomotion after neuromotor disorder

Neuromodulation of spinal sensorimotor circuits improves motor control in animal models and humans with Spinal Cord Injury (SCI) and Parkinson disease. Stimulation parameters are tuned manually and remain constant during motor execution which is suboptimal to mediate maximum therapeutic effects. Here, I present a novel neuroprosthetic system that enabled adaptive changes of neuromodulation parameters during locomotion and allowed to restore high-fidelity control over leg movements in paralyzed rats. Beyond the therapeutic potential, these findings provide a conceptual and technical framework to personalize neuromodulation treatments for other neurological disorders. Several limitations have restricted the development of neuroprosthetic systems for closed loop neuromodulation. (1) First, it required a mechanistic understanding of the relationships between stimulation features and the recruitment of specific sensorimotor circuits. I found that electrical neuromodulation primarily recruits afferent reflex pathways that lead to coordinated activity of leg muscles during stepping. Moreover, the specific electrode location on the spinal cord could activate distinct reflex pathways and activate specific leg muscle groups of paralyzed rats. These results have been leveraged for the design of flexible and stretchable multi-electrode arrays for electrical and chemical spinal cord stimulation. (2) Second, it was necessary to perform comprehensive mapping experiments to characterize the effect of neuromodulation parameters on hind limb kinematics in order to establish stable and robust feedback signals for real time control. Step height and ground reaction forces emerged as the primary targets for the control of closed loop neuromodulation after spinal cord injury. (3) Third, implementation and optimization of closed-loop neuromodulation strategies necessitated the development of an advanced technological platform that combined feedback and feed-forward loops that match the natural flow of information in the modulated neural systems. These integrated developments allowed animals with complete spinal cord injury to perform over 1000 successive steps without failure, and to climb staircases of various heights and lengths with precision and fluidity. Moreover, the neuroprosthetic system was able to alleviate locomotor deficits in an alpha-synuclein rodent model of Parkinsonâs disease. Current knowledge of human spinal cord properties in response to electrical neuromodulation suggests that the developed control policies can translate into clinical applications to improve neurorehabilitation therapies. Moreover, the developed neuroprosthetic system can readily be interfaced with control signals from the brain to establish cortico-spinal neuroprostheses that are intended to promote activity-dependent plasticity during recovery from spinal cord injury

Neuroprosthetic Technologies to Evaluate and Train Leg Motor Control in Neurologically Impaired Individuals

Spinal cord injury (SCI) disrupts many essential sensorimotor and autonomic functions. Consequently, individuals with SCI can face decades with permanent disabilities. Advances in clinical management have decreased morbidity, but no clinical trial has yet demonstrated the efficacy of a repair strategy. In the past decade, Courtine lab has developed neurotechnologies that restored volitional control of locomotion in animal models of SCI. The intervention acts over two-time windows. In the short-term, the delivery of epidural electrical stimulation (EES) targeting the posterior lumbar roots with timing that mimics the natural activation of the spinal cord enables stepping in otherwise paralyzed rats. In the long-term, this targeted EES with intensive robot-assisted overground training triggers a reorganization of descending pathways that reestablished voluntary control of the paralyzed legs, even without EES. These results in animal models encouraged the transfer of these technologies and concepts to clinical applications. My contribution to this translational research program forms the core of my thesis. The first section presents a software that I developed in order to enable a comprehensive yet semi-automated analysis of kinematics and muscle activity underlying locomotor functions in humans. This toolbox allows to evaluate gait features of people with neuromotor deficits, quantify locomotor performance compared to healthy people or to monitor changes in different experimental conditions or over the time course of interventions, and automatically generate comprehensive gait reports directly understandable by scientists and clinicians. The second section introduces a paradigm shift in robotic postural assistance: the gravity-assist. We demonstrated the detrimental impact of high levels of body weight support on gravity-dependent interactions during standing and walking. We developed a gravity-assist algorithm that fine-tunes the forward and upward body weight support to reestablish these interactions based on each patientâs residual capacities. We validated the personalized gravity-assist in 30 individuals with SCI or stroke. Compared to other conditions of support, the gravity-assist enabled all the patients to improve their locomotion performance. This platform establishes refined conditions to empower and train overground locomotion in a safe yet ecological environment. The third section reports the development of targeted EES in patients with chronic SCI, and the impact of an intensive 5-month rehabilitation with gravity-assist and targeted EES on the recovery of motor functions. The key findings can be summarized as follows: We established procedures to configure targeted EES that immediately enabled voluntary control of weak or paralyzed muscles; Targeted EES boosts the residual supraspinal inputs to the lumbar spinal cord, enabling all the patients to adapt their gait to specific tasks; Locomotor performance improved during the rehabilitation; All the patients regained voluntary control over previously paralyzed muscles without EES. These combined results establish the proof-of-concept on the therapeutic potential of targeted EES and intensive, robot-assisted rehabilitation to restore locomotion after SCI. Together with similar results obtained in the US in patients with severe SCI, our findings are establishing a pathway towards the development of a viable treatment to support motor functions and improve recovery after SCI

Cortical motor prosthetics: the development and use for paralysis

The emerging research field of Brain Computer Interfaces (BCIs) has created an invasive type of BCI, the Cortical Motor Prosthetic (CMP) or invasive BCI (iBCI). The goal is to restore lost motor function via prosthetic control signals to individuals who have long-term paralysis. The development of the CMP consists of two major entities: the implantable, chronic microelectrode array (MEA) and the data acquisition hardware (DAQ) specifically the decoder. The iBCI's function is to record primary motor cortex (M1) neural signals via chronic MEA and translate into a motor command via decoder extraction algorithms that can control a prosthetic to perform the intended movement. The ultimate goal is to use the iBCI as a clinical tool for individuals with long-term paralysis to regain lost motor functioning. Thus, the iBCI is a beacon of hope that could enable individuals to independently perform daily activities and interact once again with their environment. This review seeks to accomplish two major goals. First, elaborate upon the development of the iBCI and focus on the advancements and efforts to create a viable system. Second, illustrate the exciting improvements in the iBCI's use for reaching and grasping actions and in human clinical trials. The ultimate goal is to use the iBCI as a clinical tool for individuals with long-term paralysis to regain movement control. Despite the promise in the iBCI, many challenges, which are described in this review, persist and must be overcome before the iBCI can be a viable tool for individuals with long-term. iBCI future endeavors aim to overcome the challenges and develop an efficient system enhancing the lives of many living with paralysis. Standard terms: Intracortical Brain Computer Interface (iBCI), Intracortical Brain Machine Interface (iBMI), Cortical Motor Prosthetic (CMP), Neuromotor Prostheses (NMP), Intracortical Neural Prosthetics, Invasive Neural Prosthetic all terms used interchangeabl

A Novel Framework of Online, Task-Independent Cognitive State Transition Detection and Its Applications

Complex reach, grasp, and object manipulation tasks require sequential, temporal coordination of a movement plan by neurons in the brain. Detecting cognitive state transitions associated with motor tasks from sequential neural data is pivotal in rehabilitation engineering. The cognitive state detectors proposed thus far rely on task-dependent (TD) models, i.e., the detection strategy exploits a priori knowledge of the movement goals to determine the actual states, regardless of whether these cognitive states actually depend on the movement tasks or not. This approach, however, is not viable when the tasks are not known a priori (e.g., the subject performs many different tasks) or when there is a paucity of neural data for each task. Moreover, some cognitive states (e.g., holding) are invariant to the tasks performs. I first develop an offline, task-dependent cognitive state transition detector and a kinematics decoder to show the feasibility of distinguishing between cognitive states based on their inherent features extracted via a hidden Markov model (HMM) based detection framework. The proposed framework is designed to decode both cognitive states and kinematics from ensemble neural activity. The proposed decoding framework is able to a) automatically differentiate between baseline, plan, and movement, and b) determine novel holding epochs of neural activity and also estimate the epoch-dependent kinematics. Specifically, the framework is mainly composed of a hidden Markov model (HMM) state decoder and a switching linear system (S-LDS) kinematics decoder. I take a supervised approach and use a generative framework of neural activity and kinematics. I demonstrate the decoding framework using neural recordings from ventral premotor (PMv) and dorsal premotor (PMd) neurons of a non-human primate executing four complex reach-to-grasp tasks along with the corresponding kinematics recording. Using the HMM state decoder, I demonstrate that the transitions between neighboring epochs of neural activity, regardless of the existence of any external kinematics changes, can be detected with high accuracy (>85%) and short latencies (<150 ms). I further show that the joint angle kinematics can be estimated reliably with high accuracy (mean = 88%) using a S-LDS kinematics decoder. In addition, I demonstrate that the use of multiple latent state variables to model the within-epoch neural activity variability can improve the decoder performance. This unified decoding framework combining a HMM state decoder and a S-LDS may be useful in neural decoding of cognitive states and complex movements of prosthetic limbs in practical brain-computer interface implementations. I then develop a real-time (online) task-independent (TI) framework to detect cognitive state transitions from spike trains and kinematic measurements. I applied this framework to 226 single-unit recordings collected via multi-electrode arrays in the premotor dorsal and ventral (PMd and PMv) regions of the cortex of two non-human primates performing 3D multi-object reach-to-grasp tasks, and I used the detection latency and accuracy of state transitions to measure the performance. I found that, in both online and offline detection modes, (i) TI models have significantly better performance than TD models when using neuronal data alone, however (ii) during movements, the addition of the kinematics history to the TI models further improves detection performance. These findings suggest that TI models may be able to more accurately detect cognitive state transitions than TD under certain circumstances. The proposed framework could pave the way for a TI control of prosthesis from cortical neurons, a beneficial outcome when the choice of tasks is vast, but despite that the basic movement cognitive states need to be decoded. Based on the online cognitive state transition detector, I further construct an online task-independent kinematics decoder. I constructed our framework using single-unit recordings from 452 neurons and synchronized kinematics recordings from two non-human primates performing 3D multi-object reach-to-grasp tasks. I find that (i) the proposed TI framework performs significantly better than current frameworks that rely on TD models (p = 0.03); and (ii) modeling cognitive state information further improves decoding performance. These findings suggest that TI models with cognitive-state-dependent parameters may more accurately decode kinematics and could pave the way for more clinically viable neural prosthetics

Proceedings XXII Congresso SIAMOC 2022

Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica dà l’occasione a tutti i professionisti, dell’ambito clinico e ingegneristico, di incontrarsi, presentare le proprie ricerche e rimanere aggiornati sulle più recenti innovazioni nell’ambito dell’applicazione clinica dei metodi di analisi del movimento, al fine di promuoverne lo studio e le applicazioni cliniche per migliorare la valutazione dei disordini motori, aumentare l’efficacia dei trattamenti attraverso l’analisi quantitativa dei dati e una più focalizzata pianificazione dei trattamenti, ed inoltre per quantificare i risultati delle terapie correnti

Neurophysiological mechanisms of sensorimotor recovery from stroke

Ischemic stroke often results in the devastating loss of nervous tissue in the cerebral cortex, leading to profound motor deficits when motor territory is lost, and ultimately resulting in a substantial reduction in quality of life for the stroke survivor. The International Classification of Functioning, Disability and Health (ICF) was developed in 2002 by the World Health Organization (WHO) and provides a framework for clinically defining impairment after stroke. While the reduction of burdens due to neurological disease is stated as a mission objective of the National Institute of Neurological Disorders and Stroke (NINDS), recent clinical trials have been unsuccessful in translating preclinical research breakthroughs into actionable therapeutic treatment strategies with meaningful progress towards this goal. This means that research expanding another NINDS mission is now more important than ever: improving fundamental knowledge about the brain and nervous system in order to illuminate the way forward. Past work in the monkey model of ischemic stroke has suggested there may be a relationship between motor improvements after injury and the ability of the animal to reintegrate sensory and motor information during behavior. This relationship may be subserved by sprouting cortical axonal processes that originate in the spared premotor cortex after motor cortical injury in squirrel monkeys. The axons were observed to grow for relatively long distances (millimeters), significantly changing direction so that it appears that they specifically navigate around the injury site and reorient toward the spared sensory cortex. Critically, it remains unknown whether such processes ever form functional synapses, and if they do, whether such synapses perform meaningful calculations or other functions during behavior. The intent of this dissertation was to study this phenomenon in both intact rats and rats with a focal ischemia in primary motor cortex (M1) contralateral to the preferred forelimb during a pellet retrieval task. As this proved to be a challenging and resource-intensive endeavor, a primary objective of the dissertation became to provide the tools to facilitate such a project to begin with. This includes the creation of software, hardware, and novel training and behavioral paradigms for the rat model. At the same time, analysis of previous experimental data suggested that plasticity in the neural activity of the bilateral motor cortices of rats performing pellet retrievals after focal M1 ischemia may exhibit its most salient changes with respect to functional changes in behavior via mechanisms that were different than initially hypothesized. Specifically, a major finding of this dissertation is the finding that evidence of plasticity in the unit activity of bilateral motor cortical areas of the reaching rat is much stronger at the level of population features. These features exhibit changes in dynamics that suggest a shift in network fixed points, which may relate to the stability of filtering performed during behavior. It is therefore predicted that in order to define recovery by comparison to restitution, a specific type of fixed point dynamics must be present in the cortical population state. A final suggestion is that the stability or presence of these dynamics is related to the reintegration of sensory information to the cortex, which may relate to the positive impact of physical therapy during rehabilitation in the postacute window. Although many more rats will be needed to state any of these findings as a definitive fact, this line of inquiry appears to be productive for identifying targets related to sensorimotor integration which may enhance the efficacy of future therapeutic strategies

Doctor of Philosophy

dissertationMedical intervention to restore motor function lost due to injury, stroke, or disease is increasingly common. Recent research in this field, known as functional electrical stimulation (FES), has produced a new generation of electrode devices that greatly enhance selectivity of access to neural populations, enabling-for the first time-restoration of motor function approaching what healthy humans enjoy. Research with these devices, however, has been severely hampered by the lack of a stimulation platform and control algorithms capable of exploring their full potential. The following dissertation presents the results of research aimed at addressing this problem. A major theme of this work is the use of software algorithms and analysis principles to facilitate both investigation and control of the motor system. Though many of the algorithms are well known in computer science, their application to the field of motor restoration is novel. Associated with use of these algorithms are important methodological considerations such as speed of execution, convergence, and optimality. The first phase of the research involved development of a hardware and software platform designed to support a wide range of closed-loop response mapping and control routines. Software routines to automate three time-consuming tasks-mapping stimulus thresholds, mapping stimulus-response recruitment curves, and mapping electrode pair excitation overlap- were implemented and validated in a cat model. Computer control, combined with the use of an efficient binary search algorithm, reduced the time need to complete required implant mapping tasks by a factor of 4 or more (compared to manual mapping), making feasible-for the first time-acute experiments investigating multi-array, multijoint experimental limb control. The second phase of the research involved investigating the influence of stimulus timing, within multielectrode trains, on the smoothness of evoked muscle responses. A model for predicting responses was developed and used, in conjunction with function optimization techniques, to identify stimulus timings that minimize response variation (ripple). In-vivo validation demonstrated that low-ripple timings can be identified, and that the influence of timing on ripple depends largely on the response kinetics of the motor unit pools recruited by constituent electrodes. The final phase of the research involved using the response prediction model to simulate the behavior of a feedback-based, stimulus-timing adjustment algorithm. Multiple simulations were executed to assess the influence of three algorithm parameters-filter bandwidth, error sampling delay, and timing adjustment gain-on two performance metrics- convergence time and percent reduction in ripple. Results show that all parameters have an influence on algorithm performance. Convergence speed is the metric most a↵ected by parameter adjustment, improving by a factor of more than 3 (13 cycles to approximately 4 cycles). Ripple reduction is also a↵ected-exhibiting a 17% reduction with appropriate selection of error sampling delay. These results demonstrate the value of using this simulation approach for parameter tuning

A MECHANISTIC APPROACH TO POSTURAL DEVELOPMENT IN CHILDREN

Upright standing is intrinsically unstable and requires active control. The central nervous system's feedback process is the active control that integrates multi-sensory information to generate appropriate motor commands to control the plant (the body with its musculotendon actuators). Maintaining standing balance is not trivial for a developing child because the feedback and the plant are both developing and the sensory inputs used for feedback are continually changing. Knowledge gaps exist in characterizing the critical ability of adaptive multi-sensory reweighting for standing balance control in children. Furthermore, the separate contributions of the plant and feedback and their relationship are poorly understood in children, especially when considering that the body is multi-jointed and feedback is multi-sensory. The purposes of this dissertation are to use a mechanistic approach to study multi-sensory abilities of typically developing (TD) children and children with Developmental Coordination Disorder (DCD). The specific aims are: 1) to characterize postural control under different multi-sensory conditions in TD children and children with DCD; 2) to characterize the development of adaptive multi-sensory reweighting in TD children and children with DCD; and, 3) to identify the plant and feedback for postural control in TD children and how they change in response to visual reweighting. In the first experiment (Aim 1), TD children, adults, and 7-year-old children with DCD are tested under four sensory conditions (no touch/no vision, with touch/no vision, no touch/with vision, and with touch/with vision). We found that touch robustly attenuated standing sway in all age groups. Children with DCD used touch less effectively than their TD peers and they also benefited from using vision to reduce sway. In the second experiment (Aim 2), TD children (4- to 10-year-old) and children with DCD (6- to 11-year-old) were presented with simultaneous small-amplitude touch bar and visual scene movement at 0.28 and 0.2 Hz, respectively, within five conditions that independently varied the amplitude of the stimuli. We found that TD children can reweight to both touch and vision from 4 years on and the amount of reweighting increased with age. However, multi-sensory fusion (i.e., inter-modal reweighting) was only observed in the older children. Children with DCD reweight to both touch and vision at a later age (10.8 years) than their TD peers. Even older children with DCD do not show advanced multisensory fusion. Two signature deficits of multisensory reweighting are a weak vision reweighting and a general phase lag to both sensory modalities. The final aim involves closed-loop system identification of the plant and feedback using electromyography (EMG) and kinematic responses to a high- or low-amplitude visual perturbation and two mechanical perturbations in children ages six and ten years and adults. We found that the plant is different between children and adults. Children demonstrate a smaller phase difference between trunk and leg than adults at higher frequencies. Feedback in children is qualitatively similar to adults. Quantitatively, children show less phase advance at the peak of the feedback curve which may be due to a longer time delay. Under the high and low visual amplitude conditions, children show less gain change (interpreted as reweighting) than adults in the kinematic and EMG responses. The observed kinematic and EMG reweighting are mainly due to the different use of visual information by the central nervous system as measured by the open-loop mapping from visual scene angle to EMG activity. The plant and the feedback do not contribute to reweighting