Emilio Bizzi - US grants

The main goal of this application is to investigate how the central nervous system (CNS) deals with posture, movement and the interactions with the environment. Our approach is based on the idea that in solving these problems, the CNS makes explicit use of the elastic and geometrical properties of muscles. The properties may allow the system to circumvent complex dynamics, kinematic and force distribution problems. The central idea underlying the experiments proposed here was first formulated in the simple context of single-joint movements. The results indicated the arm trajectory is achieved by the CNS through control signals that define a series of equilibrium positions. The experiments described here extend this idea to multi-joint arm behavior. Specifically, we will study: 1. The interplay of neural, mechanical and geometrical factors in the control of posture with the aim of understanding the way in which limb design contributes to stability. 2. The relationship among these three factors will be studied in the control of arm trajectory formation. To this end, we will develop a detailed computer model of the musculosketetal system and will use it to guide animal experimentation. 3. Constrained motions: We will investigate how forces arising from contact with the environment are controlled by the CNS. 4. Studies of dynamic adaptation: Our goal is to determine how the geometry and dynamics of a manipulated object are included in the planning of action.

The research efforts funded by this grant are directed at understanding the way in which the central nervous system controls arm movements. To accomplish this goal we have devised a series of investigations in monkeys and humans. The studies with monkeys will be directed towards ascertaining the role of proprioceptive feedback in arm trajectory formation. We plan to use animals deprived of sensory feedback (dorsal rhizotomy). In humans we will study the arm trajectory around obstacles. In addition, the role and the magnitude of coriolis forces and reactional torques will be studied.

The main goal of this application is to investigate how the central nervous system (CNS) deals with posture, movement and the interactions with the environment. Our approach is based on the idea that in solving these problems, the CNS makes explicit use of the elastic and geometrical properties of muscles. The properties may allow the system to circumvent complex dynamics, kinematic and force distribution problems. The central idea underlying the experiments proposed here was first formulated in the simple context of single-joint movements. The results indicated the arm trajectory is achieved by the CNS through control signals that define a series of equilibrium positions. The experiments described here extend this idea to multi-joint arm behavior. Specifically, we will study: 1. The interplay of neural, mechanical and geometrical factors in the control of posture with the aim of understanding the way in which limb design contributes to stability. 2. The relationship among these three factors will be studied in the control of arm trajectory formation. To this end, we will develop a detailed computer model of the musculosketetal system and will use it to guide animal experimentation. 3. Constrained motions: We will investigate how forces arising from contact with the environment are controlled by the CNS. 4. Studies of dynamic adaptation: Our goal is to determine how the geometry and dynamics of a manipulated object are included in the planning of action.

The main goal of this application is to investigate how the central nervous system (CNS) deals with posture, movement and the interactions with the environment. Our approach is based on the idea that in solving these problems, the CNS makes explicit use of the elastic and geometrical properties of muscles. The properties may allow the system to circumvent complex dynamics, kinematic and force distribution problems. The central idea underlying the experiments proposed here was first formulated in the simple context of single-joint movements. The results indicated the arm trajectory is achieved by the CNS through control signals that define a series of equilibrium positions. The experiments described here extend this idea to multi-joint arm behavior. Specifically, we will study: 1. The interplay of neural, mechanical and geometrical factors in the control of posture with the aim of understanding the way in which limb design contributes to stability. 2. The relationship among these three factors will be studied in the control of arm trajectory formation. To this end, we will develop a detailed computer model of the musculosketetal system and will use it to guide animal experimentation. 3. Constrained motions: We will investigate how forces arising from contact with the environment are controlled by the CNS. 4. Studies of dynamic adaptation: Our goal is to determine how the geometry and dynamics of a manipulated object are included in the planning of action.

This proposal addresses the fundamental issue of how movements are controlled. It is hypothesized that there exists a finite set of spinal neural networks, each of which generates forces for a particular posture, i.e., motor output is modularized. In addition, the force output of these modules follows a principle of vector summation. In this way, the finite set of force patterns may be viewed as representing an elementary alphabet from which, through superimposition, a vast number of actions could be fashioned by impulses conveyed by supraspinal pathways. Three sets of experiments will address this hypothesis of spinal motor modules. First, the organization of the spinal neural circuitry will be defined anatomically and electrophysiologically. By combining anatomical localization with physiological recordings in the in vitro frog spinal cord, the connectivity patterns of neurons comprising the spinal networks will be distinguished and related to particular postures. Second, the utilization of spinal motor modules by descending systems will be investigated. The probability that descending systems access spinal modules will be evaluated by comparing the force output generated by stimulation of different descending pathways to simulations of force output generated by model muscles and model spinal force fields. Third, theoretical and experimental studies will explore the control of movements by combinations of the outputs of motor modules. A computer model of the frog hindlimb will be developed to answer three questions: (1) What is the temporal course of the fields generated by the control modules in the motor system? (2) What are the dynamical effects of vector field summation? (3) How does kinematic redundancy affect the control of dynamic behavior? These findings should provide guidance for the development of new technologies or strategies for the rehabilitation of individuals suffering spinal cord injuries or stroke.

The experiments proposed here will examine properties of the dynamic stability of the movements produced by the nervous system. These experiments will focus on the movements produced by neural systems located within the spinal cord, characterizing first how the movements respond to dynamic perturbations and second how these neural systems are organized for the production of stable movements. The basic results from these experiments will therefore provide important information about the role of the spinal cord in the production of movement. By describing the limits and extent of the control of stability by spinal motor systems, we will gain a better understanding of how this control can be augmented and exploited to produce purposeful movements when the spinal cord is isolated. These experiments are designed primarily to provide such information. At the same time, these experiments are designed to examine an exciting and potentially powerful hypothesis of the production of movement by the nervous system. If these experiments find that spinal motor systems produce stable movements through the implementation of a version of sliding control, this motor systems produce stable movements through the implementation of a version of sliding control, this will be of fundamental importance in our understanding of the neural control of movement. However, we stress that the results from these experiments have been designed to provide important information on their own, regardless of whether or not they are consistent with this novel hypothesis.

DESCRIPTION (provided by applicant): This proposal describes a TeleRehabilitation system developed to provide motor retraining for stroke patients. The system allows a therapist in a remote location to conduct treatment sessions with a patient who is located at home. The patient and therapist are connected in real time via a network connection on a computer. Thus, the patient and therapist can see and hear each other in real time during the remotely operated treatment session. A special feature of the TeleRehabilitation system is the use of a virtual environment (VE) to provide augmented feedback to the patient during the motor retraining exercises which are displayed on the computer screen in the patient's home. The novel rehabilitation system includes training scenes (3-D "pictures" that are designed to elicit movements in a natural way by creating an environmental context and task goal for that movement), a "teacher" who shows the correct movement by representing the trajectory of the limb's end point (or entire arm), a scoring system, and multiple features which provide augmented feedback and knowledge of results. A therapeutic framework has been devised to group training tasks into functionally oriented movement categories. Treatment sessions will be tailored to each patient s motor recovery state and particular motor control problems through the selection of particular scenes (tasks) and difficulty levels within each category. Goals for this proposed project are: 1) to further develop the TeleRehabilitation prototype system, and to asses the feasibility of using it with stroke patients in their homes. Specifically, to conduct a pilot study with stroke patients in which to test the feasibility of using the system to provide instructions and motor training, to record the arm movements of patients, and to monitor patients' progress in their home, and 2) after feasibility has been established, to test the value of the system by conducting a single-blind, randomized controlled clinical trial with 44 stroke patients.

stroke therapy; human therapy evaluation; rehabilitation; telemedicine; computer assisted instruction; physical therapy; arm; psychomotor function; patient oriented research; medical rehabilitation related tag; human subject; clinical research;

DESCRIPTION (provided by applicant): The Challenge: Execution of voluntary movements relies critically on the functional integration of several motor cortical areas and the spinal circuitries. Surprisingly, after decades of research, how the motor cortices specify descending neural signals destined to the spinal neurons has remained obscure. Our quest for understanding motor cortical functions is made additionally challenging by the fact that successful production of any movement necessitates coordination of many muscles representing many degrees of freedom. Recently there has been evidence suggesting that the central nervous system may coordinate muscle activations through a linear combination of muscle synergies, each of which activates a group of muscles as a unit. Previous work has shown that muscle synergies may be encoded entirely by spinal circuitries. We therefore hypothesize that descending motor cortical signals may function to select and activate muscle synergies encoded downstream of the cortex. A critical technological challenge for testing the above hypothesis is that of developing a suitable computational method for identifying muscle synergies, objectively and efficiently, from experimentally derived electromyographic (EMG) data of multiple muscles. With this proposal we plan on addressing this challenge and performing experiments on stroke patients for testing this hypothesis. Specific Aims: To address the technological challenge described above, we plan on applying a recently developed multivariate analytic technique - the nonnegative matrix factorization (NMF) algorithm (Lee and Seung, 1999) - to identify muscle synergies embedded within the collected EMGs. Specifically, we will perform two experiments involving surface EMG recordings from patients with unilateral ischemic strokes in the motor cortices, which severely affect generation of voluntary actions;we will then identify muscle synergies from the collected EMGs using NMF to test our hypothesis of cortical activations of muscle synergies. In Experiment 1, we will test the robustness of muscle synergies in subacute stroke patients by recording EMGs (16 muscles) from each of the normal and stroke-affected arms during a variety of motor tasks. Our hypothesis predicts that cortical lesions should leave the muscular compositions of the synergies unaffected, and thus, the synergies of the normal and affected arms should be very similar to each other. In Experiment 2, we will characterize activation changes of muscle synergies during neurorehabilitation. We will record EMGs from the stroke-affected arm of subacute stroke patients as they undergo a 6-week neurorehabilitation therapy based on the Armeo system, a robotic training program that has been shown to be effective in improving the paretic arm's motor functions. Recordings will be conducted at 4 different time points along the course of therapy, and changes in the patient's clinical outcome will be correlated with changes in the activations of muscle synergies. This experiment will allow us to know whether EMG changes during post-stroke improvement of motor functions can be explained as changes in the activation pattern of selected synergies. Potential Scientific and Clinical Impact: This proposal offers and tests the new hypothesis that the human motor cortex produces voluntary movements by selecting and activating muscle synergies encoded downstream of the cortex. Our proposed view, if true, implies that the many previously observed correlations between motor cortical activities and various movement parameters may be phenomena secondary to synergy activations. This change in perspective from one based on movement parameters to one based on muscle synergies amounts to a paradigm shift in our understanding of the motor cortex. Clinically, the view that muscle synergies are basic units for movement execution suggests that a rehabilitation program focusing on those synergies whose activations are altered by the stroke lesions may lead to better treatment outcome than non-specific physical therapies or methods focusing on individual muscles. Our use of a factorization algorithm offers a means to objectively identify the troubled muscle synergy as a specific target for interventions such as neuromuscular electrical stimulation techniques or other biofeedback-based methods. This new neurorehabilitation possibility may lead to a better treatment efficacy for severe and/or chronic stroke patients. Considering that in the US, ~700,000 individuals suffer from a new or recurrent stroke every year, the potential clinical impact of this project is highly relevant to the goals of NIMH and NIH. PUBLIC HEALTH RELEVANCE: This proposal plans on studying how the human motor cortices translate motor intentions to commands for muscle activations, as well as how the brain restructures motor programs after a stroke injury for improvement of limb functions. Thus, the knowledge gained from this research is highly relevant to future designs and strategies for neurorehabilitation for patients suffering from severe and/or chronic stroke. Every year, there are approximately 700,000 new or recurrent cases of stroke in the US alone, and stroke has been the leading cause of long term disability;considering these statistics, this project is extremely relevant to public health and the missions of the NIH.

From throwing a baseball to playing the piano to typing on keyboards, human beings are constantly learning new sensorimotor skills. During learning, synaptic connections in the brain must be modified to form a motor memory. Further, this modification seems both permanent and robust: a sensorimotor skill, once learned, tends to persist throughout the course of a lifetime regardless of its salience (recall the old adage of never forgetting how to ride a bike). Despite the importance of motor memories, their distinctive features, and their ubiquity in vertebrate behavior, little is known about the computational principles and mechanisms that subserve the acquisition of sensorimotor skills. This US-Canadian collaborative project takes an interdisciplinary approach aimed at elucidating neural mechanisms of motor memory formation and unifying -- under a common theoretical principle -- the findings of single-neuron recording studies with established behavioral results. The theory that is proposed makes the following testable prediction: as the level of behavioral expertise in a specific task increases, the neural representation for that skill becomes more selective. By selective, it is meant that a neuron significantly recruited during the performance of the skill tends, with practice, to specialize by firing only when that skill is performed (and not when related skills are performed).

Central to the theory is a geometric interpretation of "biologically plausible" sensorimotor neural networks, in which neurons are modeled as noisy signal processors and synaptic change is modeled as a noisy morphological process. Because of the high noise levels, it is shown that the system must be "hyperplastic" -- that is, the learning rate must be unusually high in order to compensate for the noise and operate at an acceptable performance level. Geometrically, the solution for a skill can be represented as a manifold in the weight space of the network. To learn multiple skills, a network configuration must be attained such that the solution manifolds intersect. To learn multiple skills without noise leading to destructive interference, the network must arrive at a point where the intersecting solution manifolds are orthogonal. With this principle of orthogonality, the neurophysiological predictions described above can be explicitly formulated. These predictions will be tested with an experimental method -- involving floating microelectrode arrays and antidromic stimulation -- that enables the identifiably same neuron to be recorded from for multiple days/weeks, while a behaving animal learns a task. Finally, psychophysical predictions of the theory will also be tested.

This project is jointly funded by Collaborative Research in Computational Neuroscience and the OISE Americas program. A companion project is being funded by the Canadian Institutes of Health Research.

1159652/1159695
Brown/Bizzi

The brain controls movements of the body by means of neural signals transmitted through the spinal cord. Researchers in the field of Motor Neural Prosthetics attempt to tap into these neural signals and use them to control artificial actuators, such as a robotic arm or computer cursor, or native limbs that have been paralyzed. The long-term goal of such research is to help restore function to a variety of patient populations for whom the normal spinal pathways of movement control have been disrupted due to neurological disease, brain and or spinal cord injury. At present, however, the movements generated by neuroprosthetic devices lack the smoothness, and fluidity of natural movement. Furthermore, although Brain-Machine Interfaces would appear to provide a unique opportunity for studying brain function, the technology of neural prosthetics has not yet made major contributions to an improved understanding of how the circuits in the brain that control movement work.

This grant proposes the construction of a novel Brain-Body interface in animals for which elbow function is reversibly paralyzed. By connecting the output of recording electrodes placed in the brain to stimulating electrodes placed near the paralyzed elbow muscles, a pathway is created to re-establish control of the lost motor function. Unlike most research in the field, this research requires that all of the learning takes place within the brain and none of the learning takes place through a "Decoder" - that is, a computer-based machine-learning algorithm that attempts to read the mind of the user and extract the desired signals to control the movement. Thus, our Brain-Body interface is fully autonomous and requires no outside intervention. Initially, performance may be inadequate. Just as an infant requires considerable time to establish control over motor pathways, so too will a subject require time to learn to control this entirely new pathway. However, given adequate learning time, this architecture is likely to lead to superior performance, because all of the control resides within the brain and because the brain has a remarkable adapt and learn.

There are two key intellectual merits to the proposed research. The first is to use this novel preparation to the study natural systems-level mechanisms of learning present in the brain, so that how our brains self-organize during development and how our brains adapt to injury or disease are better understood. The second is to transform the fields of Brain-Machine and Brain-Body Interfacing by exploring the level of control attainable when the native brain does all of the learning rather than computer algorithms. The research has additional broader significance. In particular, through this work it is hoped that a better-performing Brain-Body interface can be developed to help restore movement function in those suffering from neurological disorders or brain injury. Moreover, this work further cultivates the methodology of Direct Brain Control (a particular form of biofeedback) for the design of rehabilitative devices. In so doing, it enhances the existing research infrastructure by bringing together clinical anesthesiologists, neurophysiologists, and engineers to forge a cross-disciplinary solution to this important problem.

The PIs and Co-PIs of grants supported through the NSF-NIH-BMBF Collaborative Research in Computational Neuroscience (CRCNS) program meet annually. This ninth meeting of CRCNS investigators brings together a broad spectrum of computational neuroscience researchers supported by the program, and includes poster presentations, talks, plenary lectures, and discussions. The meeting is scheduled for June 9-11, 2013 and is hosted by the Massachusetts Institute of Technology.