Eugene Tunik - US grants

DESCRIPTION (provided by applicant): A number of the behavioral sequelae that occur after damage to the brain, such as stroke or traumatic injury, can be explained by abnormalities in the integration of sensory information and motor commands. This project uses brain imaging and non-invasive brain stimulation, combined with novel perturbations of grasping movements with robotics and virtual environments to study the roles played by frontoparietal brain areas in different stages of sensorimotor integration and to identify specific contributions of brain networks subserving goal-directed grasping. The overarching goal of this project is use a healthy-human lesion model to make causal inferences regarding the roles played by frontoparietal cortices in two major stages of sensorimotor integration: during motor planning when visual information of the target goal and haptic information of the hand is transformed into motor commands, and during movement updating when estimates of the sensory consequences of motor commands occur (generation of a forward model). We leverage the time-tested approaches involving perturbations to the target goal and the arm motor plant (perturbations of extrinsic and intrinsic space) that have been used to study sensorimotor integration in the reach system, and apply them to study the grasp circuit, which is controlled by a unique and segregated neural network that is comparatively understudied. This is the first systematic study in humans to test the unique contributions of frontoparietal cortices in planning and forward modeling of hand shape for grasping, and how these processes interact in the context of intrinsic versus extrinsic reference frames. The central goal of Aim 1 is to use functional magnetic resonance imaging (fMRI) to identify the neural bases of motor planning in external vs. internal space and accordingly test the causal involvement of these regions with fMRI-neuronavigated transient lesions elicited with transcranial magnetic stimulation. Aim 2 complements the previous aim by testing the causal involvement of grasp-related frontoparietal areas in online updating of grasp. Here, we leverage multivariate analyses of kinematic responses to novel perturbations of hand configuration and movement goals during grasping to dissociate the involvement of these areas in generation of forward models for hand shape versus integrating sensory information into the motor plan.

Project Abstract This application seeks funding to continue our on-going investigation into the effects of intensive, high dosage task and impairment based training of the hemiparetic hand, using haptic robots integrated with complex gaming and virtual reality simulations. A growing body of work suggests that there is a time-limited period of post-ischemic heightened neuronal plasticity during which intensive training may optimally affect the recovery of gross motor skills, indicating that the timing of rehabilitation is as important as the dosing. However, recent literature indicates a controversy regarding both the value of intensive, high dosage as well as the optimal timing for therapy in the first two months after stroke. Our study is designed to empirically investigate this controversy. Furthermore, current service delivery models in the United States limit treatment time and length of hospital stay during this period. In order to facilitate timely discharge from the acute care hospital or the acute rehabilitation setting, the initial priority for rehabilitation is independence in transfers and ambulation. This has negatively impacted the provision of intensive hand and upper extremity therapy during this period of heightened neuroplasticity. It is evident that providing additional, intensive therapy during the acute rehabilitation stay is more complicated to implement and difficult for patients to tolerate, than initiating it in the outpatient setting, immediately after discharge. Our pilot data show that we are able to integrate intensive, targeted hand therapy into the routine of an acute rehabilitation setting. Our system has been specifically designed to deliver hand training when motion and strength are limited. The system uses adaptive algorithms to drive individual finger movement, gain adaptation and workspace modification to increase finger range of motion, and haptic and visual feedback from mirrored movements to reinforce motor networks in the lesioned hemisphere. We will translate the extensive experience gained in our previous studies on patients in the chronic phase, to investigate the effects of this type of intervention on recovery and function of the hand, when the training is initiated within early period of heightened plasticity. We will integrate the behavioral, the kinematic/kinetic and neurophysiological aspects of recovery to determine: 1) whether early intensive training focusing on the hand will result in a more functional hemiparetic arm; (2) whether it is necessary to initiate intensive hand therapy during the very early inpatient rehabilitation phase or will comparable outcomes be achieved if the therapy is initiated right after discharge, in the outpatient period; and 3) whether the effect of the early intervention observed at 6 months post stroke can be predicted by the cortical reorganization evaluated immediately after the therapy. This proposal will fill a critical gap in the literature and make a significant advancement in the investigation of putative interventions for recovery of hand function in patients post-stroke. Currently relatively little is known about the effect of very intensive, progressive VR/robotics training in the acute early period (5-30 days) post-stroke. This proposal can move us past a critical barrier to the development of more effective approaches in stroke rehabilitation targeted at the hand and arm.

This project addresses a question that has vexed scientists for more than a century: how does the motor cortex (the part of the brain where nerve impulses initiate voluntary muscular activity) represent and coordinate multiple muscles in order to produce a vast range of movements? To answer this question, this project will harness the unique strengths of non-invasive, navigated, transcranial magnetic stimulation (TMS) mapping to establish causal links between brain physiology and behavior. TMS is achieved by placing a coil of wires near the scalp, which when activated with an electrical current will create a magnetic field across the scalp and skull to stimulate the brain. TMS is the only non-invasive method available to stimulate the brain like invasive stimulation. However, to use TMS-based motor mapping to understand multi-muscle physiology and control, innovations in three areas are critically needed: 1) drastically improving the efficiency, efficacy and reliability of the TMS-based motor cortex mapping processes, 2) characterizing and validating TMS-based mapping as a probe for understanding the relationship between multi-muscle activation and voluntary movement, and 3) applying a neural network computational method to improve understanding of motor control and organization. Enhanced understanding of motor cortex physiology through TMS mapping of motor representations has the potential to better map the brain in applications such as surgical removal of tumors, assessing brain injury due to concussions or stroke, and identifying cortical networks needed for successful brain-machine interactions for controlling prostheses. Students involved with this project will be trained to address multidisciplinary challenges at the intersection of neuroscience, non-invasive brain stimulation, software design, control theory, machine-learning, statistical signal processing, data dimensionality reduction and visualization. Partnership with Boston-based leaders in the technology industry will provide state-of-the-art training to undergraduate, graduate, and post-graduate trainees. Through cooperative educational programming at Northeastern University and internships with Mass General Hospital, STEM-based learning opportunities will be provided for middle- and high-school students, inspiring a diverse body of students to pursue STEM careers. To promote STEM careers and demonstrate impact, the team will reach out to local venues that promote public awareness and appreciation of science, such as science fairs and the Boston Museum of Science.

The goal of this collaborative project is to develop a deeper mechanistic understanding of the role of the motor cortex (M1) in controlling single muscles and synergies in producing complex movements. This will be accomplished by developing several innovations in the use of non-invasive transcranial magnetic stimulation (TMS) to map the spatial distribution of synergies and single muscles. Transformative computational advances will be used to extract more accurate information about brain interaction with other physiological systems outside the motor domain and increase the rigor of analysis and data visualization to enhance interpretability, and repeatability. An enhanced understanding of corticomotor organization of complex movement will pave the way to studying motor system development across the lifespan, the basis of human performance enhancement, and the basis and characterization of neuromotor diseases. The research plan is organized under 3 aims. AIM 1 is to accelerate acquisition of TMS-based maps by developing an active learning process based on a Gaussian Process Model (GPM) of Muscle Evoked Potentials (MEPs) as a function of 2D spatial coordinates on the scalp. The developed Active-GMP learning algorithm is expected to speed up the mapping process by diverting time spent on loci with null data to loci where the model needs more samples to improve certainty. The efficacy and the accuracy of the new algorithm will be compared to three existing alternatives. AIM 2 is to test the behavioral relevance of synergies derived from human multi-muscle TMS mapping, i.e., to biologically validate the technical methods developed in Aim 1. Specifically, TMS and Voluntary (VOL) EMG data will be collected from 16 hand-arm muscles in healthy participants while subjects mimic hand postures for static letters and numbers of the American Sign Language alphabet. Non-negative matrix factorization-extracted synergies from VOL data and TMS data will be compared to determine if the TMS-elicited synergies match those utilized during movement production and if the adaptive Active-GMP and user-guided approaches more closely match synergies derived from VOL data compared to other approaches. AIM 3 is to develop generative and inverse topographic imaging models that allow forward modeling of M1 control and reverse mapping of M1 organization, respectively, of muscles and synergies. Hybrid models combining subject-specific FE modeling of TMS-induced cortical electric fields with neural network models trained to predict evoked muscle responses will be used to answer key questions: Q1) Are synergies dominant features of motor control? Q2) Do direct M1 motorneuron projections augment a synergy model of control? and Q3) Are muscles and synergies discretely organized in M1?

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The research objective of this project is to improve the fluidity of human-robot interactions wherein robots and humans can seamlessly pass objects between one another. Object handover is critical to everyday interactions, whether in ordinary environments or in high-stakes circumstances such as operating rooms. Seemingly effortless object handover results from successful inference and anticipation of shared intentions and actions. Manual object transfer between humans and robots will become increasingly important as robots become more common in the workplace and at home. The project team will perform human subject experiments investigating human-human and human-robot interactions within the context of object handover tasks to identify characteristics of dyadic coordination that allow people to understand their collaborator's intentions, to anticipate their actions, and to coordinate movements leading to task success. The team will use that new knowledge to develop robots that people can collaborate with on physical tasks as readily as they do other humans. Broader Impacts of the project include training opportunities for high school, undergraduate, and graduate students, with efforts to increase participation of underrepresented groups.

This project explores human and robotic perception, behavior, and intent inference in a bidirectional and integrated manner within the context of object handover. Three specific aims are planned. The first uses motion capture, eye tracking, and electroencephalography data to build models of human intent and action during object handover. A novelty of the models is that they are sensitive to an individual's role (giver vs. receiver; leader vs. follower), to the presence or absence of communicative gaze, and to the degree of predictability of certain aspects of handover, such as grasp type, locus of handover, gaze conditions, and dyadic role. The second aim will develop a real-time intent inference engine that uses recursive Bayesian state estimation to obtain a probabilistic assessment of human intent as the handover action evolves in time. The output of this model will inform the robot's trajectory planner, thereby enabling it to make short time horizon predictions of human actions and to adjust robot motion plans accordingly in real-time. The third aim will examine how specific choices in the high-level planning and low-level control of robot motion impact human inference of robotic intent and action during object handover. If successful, this project will advance understanding of robot manipulation during human-robot handover and yield algorithms for achieving advanced autonomy during human collaboration with humanoid robots.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Understanding the brain’s role in behaviors such as movement, cognition and emotion is paramount to progress in science and engineering, and to advancing improvements in health and wellness. Invasive approaches in animals cannot be readily adapted to humans, creating a technological barrier to causal study of the brain in awake behaving humans. One promising approach in humans, transcranial magnetic stimulation, uses magnetic pulses to noninvasively and safely modulate brain activity. However, stimulators modulate brain cells (neurons) indiscriminately, which prevents studying how distinct neurons drive behavior. This award will facilitate the acquisition of a cutting-edge stimulator that allows scientists to modulate specific neuron populations in the brain. The system includes an integrated positioning robot for precise localization and recording devices that read physiological signals from the brain or muscles to objectively quantify the effects on different neural populations and behavior. This instrumentation will enable discoveries that will catalyze new research in the study of brain and behavior. Crucially, the instrumentation paired with the proposed education plan will create unique training opportunities for students in STEM and health science, lowering the barrier of entry for underrepresented students, including persons of color and women. The project leverages Northeastern University’s experiential education model and various diversity/inclusion initiatives to support research by diverse (under)graduate and K-12 students and teachers.

The project proposes the acquisition of a controllable-pulse transcranial magnetic stimulator capable of differentially modulating specific neural populations in the human brain, with integrated robotic positioning and electroencephalography and electromyography recording. This instrumentation will be the only such system in the Northeastern US. As part of the Northeastern University Non-Invasive Brain Stimulation Center, it will enable unprecedented basic science research into human neurophysiology and brain-behavior relationships, and significant advances in fundamental engineering research in stimulator development, automated robotic positioning, stimulation-induced artifact removal in physiological recordings, closed-loop stimulation, and artificial intelligence / machine learning algorithms. Allowing researchers to control stimulus waveforms and to differentially activate distinct neural populations will enable a scientific scope of work that will transcend multiple disciplines including motor and affective neuroscience, cognition, memory, development, aging, and biophysical modeling of brain physiology. The proposed diversity and education plan enabled by this instrument will lower barriers for underrepresented minority students to engage in cutting-edge experiential STEM education. The project’s impact will be profound on science and technology innovation, as well as in training a new, diverse, interdisciplinary workforce to drive this field forward.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.