Wilsaan M. Joiner, Ph.D. - US grants

PROJECT SUMMARY Stable visual perception is maintained despite the frequent saccadic eye-movements that disrupt the visual input. One hypothesis for this compensation is that advanced knowledge of the impending saccade is provided by an internal copy, a corollary discharge (CD) signal, of the motor command. This copy is utilized to distinguish self-generated sensations from environmental disturbances and reflects properties of the original motor command. However, the influence these mirrored characteristics exert in determining the source of a disturbance and its impact on visual perception is currently unknown. The research objective of this application is to determine the consequences of CD variability on the ability to make accurate perceptual judgments of trans-saccadic changes in the visual scene, and establish that defects in CD transmission underlie the visual perceptual disorders in neurological disease. The central hypothesis of this research is that (1) the CD of saccadic eye-movements of different amplitudes and directions reflect the properties of the original motor command and (2) patients with CD transmission disruption, such as in Schizophrenia, will demonstrate a reduced ability to perceive trans-saccadic visual scene variations. The research uses human psychophysics and the known neuroanatomy of the saccadic eye-movement system to dissect the way the brain continuously incorporates the changing sensory information that results from our own actions into a stable visual percept. The hypothesis of the application has been formulated on the basis of strong preliminary data that show the ability to utilize CD to detect trans-saccadic visual scene alterations in normal human subjects weakens with increasing variance in the CD signal and is also influenced by the direction of the saccadic eye-movement, as predicted from the source of the CD and motor command. The long-term goal of the research is to understand the role of corollary discharge in visual perception and movement control with the objective of contributing to the diagnosis and treatment of diseases that result from CD transmission deficits. The expected outcomes of the research is to provide evidence that perceptual judgments.; of trans-saccadic changes is influenced by the variability reflected in the CD signal, and that this detection ability is diminished when CD transmission is degraded in Schizophrenia. This contribution is significant because in addition to diagnostics, the majority of the internal signals in the brain that do not represent sensory or motor information are presently inaccessible, and it is likely that diseases that impair higher cognitive function affect these circuits, as in Schizophrenia. CD is one of the few internal signals that is experimentally accessible and, through its study, an enhanced understanding of these signals and their transmission can be achieved.

PROJECT SUMMARY Psychosis is a mental disorder that manifests as a range of symptoms, including hallucinations and delusions. These symptoms reflect a diminished ability to accurately recognize self-generated sensations from externally caused actions or thoughts. This difficulty in differentiating and classifying actions and thoughts as self-initiated or external has been collectively referred to as a disturbance in sense of agency (SoA). One theory is that disruptions in corollary discharge (CD, the internally generated copy of issued motor commands) underlie the SoA defects demonstrated in psychosis. However, due to the inherent subjectivity of quantifying self-experience there is difficulty in objectively evaluating abnormal experiences of agency across patient populations. This difficulty subsequently prevents defining the role of CD in agency, as well as identifying the disrupted neural circuits that convey these signals. The research objective of this application is to develop a sensitive, quantitative perceptual assessment of CD utilization in order to define the continuum of abnormalities in SoA in psychotic disorders, and isolate the principal neural circuit disruptions associated with the behavioral abnormalities. The central hypothesis of the application is that (1) deficits in CD transmission contribute to visual perception impairments in patient populations that experience psychosis and (2) a neural circuit, previously characterized in primates, that conveys the saccade CD underlies the visual perception impairments observed in human psychotic disorders. The research uses human psychophysics, clinical evaluations, statistical analysis and brain imaging to isolate the continuum of neural circuit disruptions associated with the range of observed behavioral abnormalities. The hypothesis of the application has been formulated on the basis of strong preliminary data that (1) demonstrate that a visual perception behavioral paradigm can isolate and quantitatively assess CD utilization, (2) identify the associated CD neural circuitry in monkeys, (3) probe the perceptual consequences when this circuit is reversibly disrupted and (4) confirm similar perceptual deficits in schizophrenia patients. The long-term goal of the research is to understand the role of CD in SoA, and how the disruption of these signals contribute to the clinical symptoms of psychosis. The expected outcome of the research is to provide detailed information, from behavior to the neural pathways, on the contribution of CD disruption to SoA disorders. This contribution is significant because it will provide a precise neurobehavioral model to develop analytical diagnostics and treatment strategies for abnormal self-experience.

The overall research objective of this collaborative project is to create an embodied, intelligent robotic system that can induce meaningful long-term change in human motor function by providing personalized, adaptive feedback and noninvasive neural stimulation designed to induce desirable neuromotor plasticity. The motor behavior targeted for enhancement is plantarflexor power during the push-off phase of gait; stroke survivors often produce diminished plantarflexor power and rely instead on an inappropriate hip flexion "pull-off" compensation, thereby limiting the quality of their gait and quality of life. Personalized learning methods will be employed to model and optimize behavioral responses to changes in performance feedback provided by an intelligent mobile robotic coach, which will guide gait training. The project will lay the foundation to determine whether training based solely on principles of motor learning suffice to induce meaningful increases in plantarflexor power that are retained over time, or whether simultaneous targeted changes in brain excitability are required. This project advances the NSF mission to promote the progress of science and advance the national health by developing an adaptive motor learning algorithm embedded within an interactive mobile robot to induce meaningful long-term changes in human motor function through human-robot interaction. Broader impacts of the project include efforts to enhance research reproducibility and rigor, and to broaden participation in STEM for women, minorities, and persons with disabilities.

The overall objective of this research is to create an embodied, intelligent system that provides personalized, adaptive feedback to induce neuromotor plasticity, mediate motor adaptation, and promote meaningful, lasting increases in plantarflexor power, which is diminished during walking in many stroke survivors. Three sets of human subject experiments are researched. The first will identify critical parameters of performance feedback that facilitate the desired behavioral change. The second will use a novel learning paradigm to model and optimize behavioral responses to changes in performance feedback provided by an intelligent robotic coach. The third will use single-pulse transcranial magnetic stimulation (TMS) and paired associative stimulation (PAS) to harness neuroplastic effects in humans such that desired behavioral changes induced by optimized feedback training are made persistent through Hebbian learning mechanisms. The envisioned system will involve bi-directional learning between the human and machine intelligences to determine how to control important, but subject-specific, variables critical for maintaining and promoting motor function across the life and health span. Understanding these bi-directional relationships within the context of neurorehabilitation may provide insights that can further advance human-robot teaming in a range of application domains, including healthcare, manufacturing, and personal transportation.

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.

Children born with upper limb deficiencies face several unique challenges when operating prosthetic limbs, for example their affected muscles will never have moved a complete hand. So, while many prosthetic limbs are operated by measuring activity in these muscles, the degree to which children can purposefully control the affected muscles or how best to measure this muscle activity for effective prosthetic operation is still not fully understood, which is one of the reasons advanced robotic prosthetic limbs are not widely available for children even though many "hand-like" systems are available for adults. This research will investigate how well children born with upper limb deficiencies can control their affected muscles, and will then use that information to develop AI algorithms to recognize the movements a child wishes to achieve with their missing hand. The long-term goal is to better understand the capabilities of these children so as to enable creation of more helpful prosthetic limbs that are tailored to relevant factors such as age, gender, and learning. Project outcomes will include datasets, algorithms, and a deeper understanding of the capabilities of children born with upper limb deficiencies, which will ultimately help medical professionals decide on prosthetic treatment options and will also lead to control techniques for other robotic devices for children, such as exoskeletons. Additional broad impact will derive from the fact that this project will support annual involvement in a multi-day summer camp program designed to help children with upper limb deficiencies learn about their capabilities.

This project will capture muscle activity in children's affected limbs by measuring muscle movements below the skin's surface and the electrical activity of these same muscles. Two human-technology interfaces will be employed: sonomyography, which uses a small ultrasound sensor, image processing, and machine learning to infer the user's intended missing-hand movements from the affected muscle deformations; and electromyography (sEMG) pattern recognition, which uses machine learning to infer the user's intended missing-hand movements from multiple sensors measuring the electrical activity of the affected muscles. The capacity of children ages 5-17yrs to control their affected muscles will first be characterized using ultrasound imaging and sEMG measures. Post-hoc analyses of this data will then be performed to fine-tune machine learning algorithms that extract classifiable missing hand movement data from the ultrasound imaging and sEMG signals. Finally, the real-time performance of sonomyography and sEMG pattern recognition will be characterized as well as participant learning effects, as subjects perform videogame activities with these systems across multiple testing sessions.

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.