Jose L. Contreras-Vidal - US grants

One of the fundamental properties of the human brain is its ability to adapt to changing intrinsic (e.g., growth) and extrinsic (e.g., changing environment) conditions. Adaptive sensory-motor behavior usually requires the transformation and integration of information from different modalities and different coordinate systems. For example, in visually- guided reaching, the location of an object in visual coordinates needs to be mapped to motor commands that move the arm to the visual target. This transformation, known as the visuomotor map, needs to be adapted if environmental conditions change, e.g., due to perturbations of the visual input. Recent data suggest that aging degrades visuomotor adaptation in humans. However, little is known about the mechanisms underlying age- related changes in sensory-motor processing and adaptation. In the proposed research, two major hypotheses will be systematically investigated using behavioral and computational neuroscience methods: 1) The rate of visuomotor adaptation is reduced in the elderly (reduced plasticity) and 2) Increased motor variability (e.g., reduced . signal-to- noise ratios) in aging contributes to reduced visuomotor adaptation. This theory-driven research has both theoretical and clinical relevance, as the proposed experiments will provide a detailed quantitative description of the effects of aging on visuomotor adaptation, whereas the computational model will help the understanding of these age-related changes. Overall this research will elucidate which interventions are likely to prevent or modify these changes.

[unreadable] DESCRIPTION (provided by applicant): Human movement has been a large window into the functioning of the nervous system. Behavioral scientists have had major accomplishments, such as documenting movement milestones in human development and establishing a relationship between brain and behavior in typical and atypical populations. These measurements are performed today with a cornucopia of sophisticated techniques, ranging from infrared and video to wireless sensor networks. However, despite the tremendous progress on measuring human movement, we still don't fully understand, for example, motor decline in elderly people or Parkinson's disease during daily living activities at home and the workplace; or how atypical social interaction in autism or developmental coordination disorder are manifested in body gestures. Why can't we yet deal with problems of such nature? It is clear that the problems mentioned above have characteristics that are beyond the state of the art or any single discipline. Thus, we propose a novel, interdisciplinary, and multi-level motion understanding tool to extract multi-scaled, nested representations of transitive and intransitive actions and communicative actions at different levels of abstraction at the "individual" and "workgroup" levels. Our specific aim is to develop a Human Action Language (HAL) tool, a tool for describing and understanding human actions. The underlying premise is that the space of human actions is characterized by a language; this new language has its own phonemes (primitives), its own morphemes (words/actions) and its own syntax, semantics and pragmatics. Although previous research has concentrated on finding primitives in very often isolated types of human action, the innovation here is the use of large amounts of human motion data in ecologically valid settings and in conjunction with modern data mining and grammatical induction techniques. To validate the HAL tool, we will apply it to assess atypical movement in Developmental Coordination Disorder (DCD) and Parkinson's disease (PD). Specifically, we propose to extract the DCD grammar and the PD grammar and compare them with the grammars from the control populations, investigating relationships between the corresponding grammars at the individual and workgroup levels. Our interdisciplinary team consists of a computational scientist, a behavioral scientist (motor development) , and a computational neuroscientist (motor control and learning). The proposed tool will extend the scope of behavioral sciences (grounding of language, imitation, and gesture-based social communication) and facilitate interdisciplinary research bringing together movement disorders specialists, behavioral scientists, physical or occupational therapists and computer scientists. Several NIH Institutes would benefit from the availability of such a tool, including NIA/NINDS, NIMH and NICHD. The ultimate goal is to better understand human action production and understanding, and to develop optimal diagnostic and intervention tools for populations with atypical movement patterns. The proposed tool will extend the scope of behavioral sciences (grounding of language, imitation, and gesture- based social communication) and facilitate interdisciplinary research bringing together movement disorders specialists, behavioral scientists, physical or occupational therapists and computer scientists. Several NIH Institutes would benefit from the availability of such a tool, including NIA/NINDS - for understanding motor decline in the elderly and neurological populations in single and group-based daily living activities, NIMH - for understanding stereotypical behaviors in populations affected with mental disorders, and NICHD - for understanding developmental aspects of cognitive motor behavior in children at school or home. The ultimate goal is to better understand human action production and understanding, and developing optimal diagnostic and intervention tools for populations with atypical movement patterns. [unreadable] [unreadable] [unreadable]

The broad, long-term goal of this project is to develop, validate and study a large-scale dynamic neural network model of the human brain areas comprising the human mirror neuron system (MNS). This system is thought to enable an individual's understanding of the meaning of acfions performed by others, and the potential imitation and learning of those actions, and recent studies implicate dysfunction of the MNS system in autism. However, little is known about the development and the plasficity of the MNS in infants and children, how these infants come to understand and acquire their first actions, and the degree of plasticity of the system in adults. To address these gaps, this project will use large-scale computer model simulations of the MNS to deepen our understanding of the basic neurobiological mechanisms and computafional algorithms that underlie the development and plasticity of the MNS. By closely interacting with the Companion Projects, the model will integrate human and non-human primate data from various modalities including single cell recordings, scalp electroencephalogram (EEG), and behavior, and use these data to validate the model at various stages of development. Moreover, by using simulated developmental abnormalities reported in the literature, in a systematic fashion, we will assess the adequacy of the model to account for behavioral and EEG data reported in autism, and to increase our understanding of the funcfions and roles of the MNS in three fundamental abilities central to adaptive human functioning: 1) the ability to deploy actions strategically in service of goals, 2) the ability to infer the goals or acfions of one's social partners, and 3) the ability to learn via imitation. In summary, this research proposes a series of experiments integrating behavioral, electrophysiological, and mathematical modeling methods to investigate the basic neurobiological mechanisms that underlie the emergence of the MNS in infants and its plasticity in adults.

Objectives Core B, the electrophysiology and computational core, will develop standard methods for processing and analysis of EEG data from both human and non-human primate studies that can be used to identify mirror neuron system (MNS) activity. Both standard nonparametric spectral estimation methods and more recent methods based on parametric modeling, wavelet analysis, and independent component analysis of brain activity will be used to identify mu like responses in infants, children and adults and in the non-human primate studies. Moreover, cortical source analysis will also provide a window to the generators of the mu activity as described below. This core will provide foundational support to each of the Projects for collecfing electrophysiological data examining the mirror system during observafion and performance of action in human infants (Projects I and II), human children (Project I and IV) and human adults (Projects I and IV) as well as non-human primate infants (Projects I and III) and non-human primate adults (Project III).

DESCRIPTION (provided by applicant): The broad, long-term goal of this project is to develop novel noninvasive neuroprosthetics for restoration and/or rehabilitation of bipedal locomotion in patients with spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS), subcortical stroke or lower limb amputations. The control of bipedal locomotion is of great interest to the fields of brain machine interfaces (BMIs), i.e. devices that utilize neural activity to control limb prosthesis and gait rehabilitation. Since locomotion deficits are commonly associated with SCI and neurodegenerative diseases, there is also a need to investigate new potential therapies to restore gait control in such patients. While the feasibility of a BMI for upper limbs has been demonstrated in studies in monkeys and humans, neural decoding of bipedal locomotion in humans has not yet been demonstrated. This project builds upon findings from non-invasive neural decoding of movements in our laboratory, and follows a principled, step-by-step, experimental and computational approach to neural decoding of human bipedal locomotion from scalp EEG and the development of brain-computer interfaces for gait rehabilitation. The specific aims of this project are: 1) to investigate what gait parameters are best predicted from brain activity acquired with scalp EEG;2) to examine longitudinally the changes in the cortical representation of gait during adaptation to virtual cortical lesions or virtual perturbations of gait kinematics using a closed-loop BCI environment. This will be the first time-resolved examination of how cortical networks may adapt to changes in the neural representation of gait in healthy subjects, and may have implications for studying cortical plasticity after brain injury or physical disability, and for the development of BMIs for gait restoration. This research is clinically significant to patients with impaired gait function, as in the case of stroke patients, Parkinson's disease, SCI and lower-limb amputees, as BMIs may one day help restore gait function. PUBLIC HEALTH RELEVANCE: In the United States, there are approximately 1.7 million people persons living with limb loss (2008 National Limb Loss Information Center). In addition, spinal cord injury, ALS and stroke affect gait capabilities of about 2 million people in the USA. This research will provide the foundations for the development of noninvasive neuroprosthetics for restoration and rehabilitation of gait thereby increasing the quality of life of patients while reducing the socioeconomic burden of lower limb disabilities.

DESCRIPTION (provided by applicant): This research aims to accelerate the development, efficacy and use of robotic rehabilitation after stroke by capitalizing on the benefits of patient intent and real-time assessment of impairment. Validation will occur using the MAHI EXO-II exoskeleton robot at The Institute for Rehabilitation and Research (TIRR) in Houston, Texas. Robotic rehabilitation is an effective platform for sensorimotor training in stroke patients. A robotic device enables accurate positioning of the impaired limb while simultaneously providing assistance & resistance forces and collection of motion data that can be used to characterize the quality of the patient's movements. The MAHI EXO-II, a physical human-robot interface, will be augmented with a non-invasive brain-machine interface (BMI) to include the patient in the control loop, thereby making the therapy 'active' and engaging patients across a broad spectrum of impairment severity in the rehabilitation tasks. This approach capitalizes on the known benefits of patient intent in movement initiation observed in other clinical studies of robotic rehabilitation and on the beneficial effects of BMI use on cortical plasticity. Robotic measures of motor impairment, derived from real-time data acquired from sensors on the MAHI EXO-II and from the BMI, will drive patient-specific therapy sessions adapted to the capabilities ofthe individual, with the robot providing assistance or challenging the participant as appropriate, in order to maximize rehabilitation outcomes. Assist-as-needed paradigms in robotic rehabilitation have been shown to be efficacious; however, such paradigms are passive and driven by performance metrics that have not been sufficiently validated and verified. Additionally, intense practice and continual 'challenge' during therapy is known to improve rehabilitation outcomes. Key contributions: 1) Adapting most advanced EEG- BMI methods to stroke patients and developing a BMI for the control of the MAHI EXO-II that will a) increase upper limb function, b) advance understanding of brain plasticity, and c) innovate rehabilitation; 2) Determining appropriate robotic and electrophysiological measures of motor impairment and associated control algorithms for patient-specific therapy; and 3) Clinical validation in pilot studies to evaluate the proposed approach.

Project Summary: The broad, long-term goal of this project is to develop a wearable high performance MEG system that can operate without external shielding that will lead to Advances in Human Neuroscience and transformative advances in our understanding of the Human Brain ?in Action and in Context?, which are currently unachievable via imaging technologies in live persons. There are two specific aims: Aim 1 is a Small- scale, proof-of-concept development of uMEG Sensors to validate a novel non-invasive contactless uncooled unshielded magnetic sensor system based on total-field optically-pumped magnetometers (tOPM). Aim 2 is a small-scale human study to generate preliminary results with the uMEG system. Here, we will design and validate a closed-loop uMEG-based brain-computer interface (BCI) system in healthy adults. The proof-of- principle prototype system will be the first flexible array of total-field optically-pumped magnetometers (OPMs) that has the potential to enable a truly wearable MEG system for behaviorally active human neuroimaging that allows for movement in space/place during imaging in more natural environments while maintaining high resolution. This system will lead to next generation high-performance non-invasive closed-loop wearable neural interfaces that can be used to control prosthetics, computers and other assistive and therapeutic devices to study, diagnose, repair, augment, or restore cognitive-motor capabilities after brain injury or neurological disease.