Siddhartha Sikdar - US grants

The objective of this research is to investigate complex dynamic interactions between the musculoskeletal, circulatory and nervous systems involved in common, yet poorly understood, muscle disorders. The approach is to develop novel dynamic ultrasound imaging modes for quantifying anisotropic muscle kinematics, viscoelastic tissue properties and blood flow, and integrate these novel measures with conventional measures of tissue oxygenation, electrical activation, strength, and range of motion to characterize the underlying physiological systems.

Intellectual Merit: Real-time ultrasound imaging is uniquely suited for dynamic muscle function studies because it is cost-effective, portable and can be integrated with other measurements. However, lack of quantitative dynamic measures and challenges due to anisotropy of muscle have been barriers to widespread use of ultrasound. The proposed research is designed to overcome these barriers. The technical contributions are the theoretical and experimental investigation of novel ultrasound beam configurations, imaging modes and signal and image processing algorithms for quantitative imaging of anisotropic tissue motion and viscoelastic tissue properties.

Broader Impacts: This research will provide enabling tools for understanding functional limitations in musculoskeletal disorders and measuring treatment efficacy, potentially leading to more effective therapies for this significant public health problem. Research and educational objectives are integrated to engage graduate, undergraduate and high school students as part of a new bioengineering curriculum. Outreach efforts include summer research programs for high school students, a bioengineering demonstration kit encouraging students to pursue careers in science and engineering, and engaging the local K-12 community by presenting state-of-the-art research on muscle disorders affecting school-age children.

DESCRIPTION (provided by applicant): Chronic soft-tissue (or myofascial) pain is a significant public health problem. Despite its high prevalence, the underlying mechanisms are poorly understood. In particular, very little is known about the pathophysiology and soft tissue environment of a myofascial trigger point (MTrP). MTrPs are palpable, localized painful nodules in a taut band of skeletal muscle that are a characteristic finding in myofascial pain syndrome (MPS). MTrPs are associated with spontaneous referred pain in symptomatic patients, and are the target for current management strategies for MPS, such as dry needle therapy. Recently, our research group has developed new ultrasound imaging methods to visualize and characterize the physiology and physical properties of the MTrPs and their surrounding soft tissue;and microanalytic techniques to assay the local biochemical milieu. These innovative methodological advances provide a unique opportunity to integrate the physical, physiological and biochemical findings to achieve a more comprehensive understanding of the abnormalities associated with MTrPs (e.g., muscle, fascia, blood vessels);and to correlate these findings with clinical assessments to better understand the role of MTrPs in chronic pain. Our ultimate goal is to develop a working model of the underlying mechanisms of MTrPs and translate the findings to objective clinical outcome measures using office-based technology. The specific aims of the project are: 1) To determine the mechanical tissue properties, vascular physiology and biochemical milieu of the affected soft tissue neighborhood of active MTrPs in patients with chronic neck pain compared to asymptomatic control subjects with/without palpable MTrPs;and 2) To determine the effect of a physical perturbation caused by dry needle therapy, a widely accepted method of treatment, on the soft tissue environment and biochemical milieu of active MTrPs in symptomatic subjects. Our working hypothesis is that MTrPs are sites of muscle injury where local biochemical changes lead to sustained muscle contracture, compression of blood vessels and a local energy crisis that causes tissue hypoxia. This condition perpetuates the release of inflammatory cytokines and nociceptive (pain-inducing) substances. To test this hypothesis, we will correlate ultrasound imaging scores, analyte levels and functional clinical measures in our specific aims. To translate these findings into clinical outcome measures that can be used in an office-based setting, we will adapt a reliable and inexpensive 3D Tactile Imaging instrument for quantifying mechanical soft tissue changes associated with MTrPs. PUBLIC HEALTH RELEVANCE: Chronic pain is a significant public health concern. This proposal aims to identify anatomical and physiological abnormalities of muscle, fascia and blood flow in painful areas of the trapezius and neck associated with myofascial trigger points (MTrPs), which are a characteristic finding in myofascial pain syndrome (MPS). Demonstrating which tissues are involved (e.g., muscle, fascia, vessels), and which biochemicals are abnormal in MTrPs, will help develop appropriate preventive and therapeutic strategies, establish diagnostic criteria and potential outcome measures that can be used in treatment trials. Our approach would also be broadly applicable to elucidating the underlying mechanisms in other chronic musculoskeletal pain disorders, such low back pain.

The problem of controlling biomechatronic systems, such as multiarticulating prosthetic hands, involves unique challenges in the science and engineering of Cyber Physical Systems (CPS), requiring integration between computational systems for recognizing human functional activity and intent and controlling prosthetic devices to interact with the physical world. Research on this problem has been limited by the difficulties in noninvasively acquiring robust biosignals that allow intuitive and reliable control of multiple degrees of freedom (DoF). The objective of this research is to investigate a new sensing paradigm based on ultrasonic imaging of dynamic muscle activity. The synergistic research plan will integrate novel imaging technologies, new computational methods for activity recognition and learning, and high-performance embedded computing to enable robust and intuitive control of dexterous prosthetic hands with multiple DoF. The interdisciplinary research team involves collaboration between biomedical engineers, electrical engineers and computer scientists. The specific aims are to: (1) research and develop spatio-temporal image analysis and pattern recognition algorithms to learn and predict different dexterous tasks based on sonographic patterns of muscle activity (2) develop a wearable image-based biosignal sensing system by integrating multiple ultrasound imaging sensors with a low-power heterogeneous multicore embedded processor and (3) perform experiments to evaluate the real-time control of a prosthetic hand.
The proposed research methods are broadly applicable to assistive technologies where physical systems, computational frameworks and low-power embedded computing serve to augment human activities or to replace lost functionality. The research will advance CPS science and engineering through integration of portable sensors for image-based sensing of complex adaptive physical phenomena such as dynamic neuromuscular activity, and real-time sophisticated image understanding algorithms to interpret such phenomena running on low-power high performance embedded systems. The technological advances would enable practical wearable image-based biosensing, with applications in healthcare, and the computational methods would be broadly applicable to problems involving activity recognition from spatiotemporal image data, such as surveillance.

This research will have societal impacts as well as train students in interdisciplinary methods relevant to CPS. About 1.6 million Americans live with amputations that significantly affect activities of daily living. The proposed project has the long-term potential to significantly improve functionality of upper extremity prostheses, improve quality of life of amputees, and increase the acceptance of prosthetic limbs. This research could also facilitate intelligent assistive devices for more targeted neurorehabilitation of stroke victims. This project will provide immersive interdisciplinary CPS-relevant training for graduate and undergraduate students to integrate computational methods with imaging, processor architectures, human functional activity and artificial devices for solving challenging public health problems. A strong emphasis will be placed on involving undergraduate students in research as part of structured programs at our institution. The research team will involve students with disabilities in research activities by leveraging an ongoing NSF-funded project. Bioengineering training activities will be part of a newly developed undergraduate curriculum and a graduate curriculum under development.

The synergistic research plan has been designed to advance CPS science and engineering through the development of new computational methods for dynamic activity recognition and learning from image sequences, development of novel wearable imaging technologies including high-performance embedded computing, and real-time control of a physical system. The specific aims are to:
(1) Research and develop spatio-temporal image analysis and pattern recognition algorithms to learn and predict different dexterous tasks based on sonographic patterns of muscle activity. The first aim has three subtasks designed to collect, analyze and understand image sequences associated with functional tasks. (2) Develop a wearable image-based biosignal sensing system by integrating multiple ultrasound imaging sensors with a low-power heterogeneous multicore embedded processor. The second aim has two subtasks designed to integrate wearable imaging sensors with a real-time computational platform. (3) Perform experiments to evaluate the real-time control of a prosthetic hand. The third aim will integrate the wearable image acquisition system developed in Aim 2, and the image understanding algorithms developed in Aim 1, for real-time evaluation of the control of a prosthetic hand interacting with a virtual reality environment.

Successful completion of these aims will result in a real-time system that acquires image data from complex neuromuscular activity, decodes activity intent from spatiotemporal image data using computational algorithms, and controls a prosthetic limb in a virtual reality environment in real time. Once developed and validated, this system can be the starting point for developing a new class of sophisticated control algorithms for intuitive control of advanced prosthetic limbs, new assistive technologies for neurorehabilitation, and wearable real-time imaging systems for smart health applications.

With the rapid advances in small, low-cost wearable computing technologies, there is a tremendous opportunity to develop personal health monitoring devices capable of continuous vigilant monitoring of physiological signals. Wearable biomedical devices have the potential to reduce the morbidity, mortality, and economic cost associated with many chronic diseases by enabling early intervention and preventing costly hospitalizations. These low power systems require to have the capacity to provide fast and accurate processing and interpretation of vast amounts of data and generate smart alarms only when warranted. The objective of this project is to build the foundation of the next generation of heterogeneous biomedical signal processing platforms that can address the current and future generation energy-efficiency requirements and computational demands. The PIs start with understanding the specific characteristics of emerging biomedical signal and imaging applications on off-the-shelf embedded low power multicore CPU, GPU and FPGA platforms to accurately understand the trade-offs they offer and the bottlenecks they have. Based on these results, the PIs will design and architect a domain-specific manycore accelerator in hardware and integrate it with an off-the-shelf embedded processor that together combine performance, scalability, programmability, and power efficiency requirements for these applications. The PIs will implement the proposed heterogeneous architecture in hardware and will evaluate its performance and power efficiency with a number of real-life biomedical workloads including seizure detection, handheld ultrasound spectral Doppler and imaging, tongue drive assistive device and prosthetic hand control interface.

The proposed interdisciplinary research effort could inspire and enable new approaches to healthcare monitoring, and can significantly impact several fields including human-centered cyber-physical systems, cyber-security, mobile communications, bioinformatics and applications that require high performance and energy efficient embedded computing from different sensors. The proposed benchmark, characterization, and software-hardware computing framework will be freely shared and broadly disseminated among colleagues in related disciplines. Research results will be integrated in graduate and undergraduate courses offered by the investigators in both campuses. The PIs are active in several campus-wide and national organizations that work to attract and retain members of under-represented groups to engage in research and complete graduate degrees in science and engineering.

This award provides support to George Mason University for the acquisition of a high performance 3 Tesla (3T), whole body Magnetic Resonance Imaging (MRI) scanner to support innovative, transformative research into brain and body. The new 3T MRI system, along with sophisticated coils and software, will be the centerpiece of an Interdisciplinary Multimodal Imaging Center (IMIC). MRI allows detailed, noninvasive imaging of brain and body anatomy and connectivity; function via blood oxygen level dependent (BOLD) responses; and metabolism via magnetic resonance spectroscopy. GMU scientists, representing more than eight disciplines across five colleges, will benefit from this award by collaborating to conduct cross-cutting interdisciplinary research on groundbreaking associations between brain and body. The 3T MRI scanner will also enable researchers at GMU to conduct innovative training of undergraduates, graduate students, and junior scientists in brain-body science approaches. This group of researchers all take advantage of our location in the Northern VA/Washington DC region to study sample diverse in age, disability status, ethnicity, and socioeconomic status.

One central theme to the research of GMU investigators is to understand the interrelations of brain and body functions from a biological, psychological, and social perspective, and the alterations of those relationships in the presence of acute and chronic stress, trauma, and pain. Examples of the interdisciplinary research programs that will significantly benefit from this instrumentation include: those examining interactions between brain and peripheral nervous systems following stress and trauma; central and peripheral pain perception; neural, gene, and protein networks and social ties; neurodevelopment and head impacts; and sensorimotor integration and control. These and the other MRI research programs at GMU will be greatly advanced by the acquisition of a Siemens MAGNETOM Prisma 3T scanner, which offers industry-leading gradient performance, parallel imaging capabilities, and a supportive and innovative sequence development community. To address the big data challenges posed by the research outlined in this proposal, a data analytics core will be established, comprised of computer scientists, engineers, and biostaticians, to facilitate data handling and storage, and to develop new techniques for the analysis and integration of MRI and related biological, physiological, and behavioral data.

This MRI award is supported by the Directorate for Social, Behavioral and Economic Sciences (SBE) Division of Behavioral and Cognitive Sciences (BCS), The Directorate for Engineering (ENG) and the ENG Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET).

The goal of this project is to develop an automated assistive device capable of restoring walking and standing functions in persons with motor impairments. Although research on assistive devices, such as active and passive orthoses and exoskeletons, has been ongoing for several decades, the improvements in mobility have been modest due to a number of limitations. One major challenge has been the limited ability to sense and interpret the state of the human, including volitional motor intent and fatigue. The proposed device will consist of powered electric motors, as well as the power generated by the person's own muscles. This work proposes to develop novel sensors to monitor muscle function, and, muscle fatigue is identified, the system will switch to the electric motors until the muscles recover. Through research on methods of seamless automated control of a hybrid assistive device while minimizing muscle fatigue, this study addresses significant limitations of prior work. The proposed project has the long-term potential to significantly improve walking and quality of life of individuals with spinal cord injuries and stroke. The proposed work will also contribute to new science of cyber-physical systems by integrating wearable image-based biosensing with physical exoskeleton systems through computational algorithms. This project will provide immersive interdisciplinary training for graduate and undergraduate students to integrate computational methods with imaging, robotics, human functional activity and artificial devices for solving challenging public health problems. A strong emphasis will be placed on involving undergraduate students in research as part of structured programs at our institutions. Additionally, students with disabilities will be involved in this research activities by leveraging an ongoing NSF-funded project.

This project includes the development of wearable ultrasound imaging sensors and real-time image analysis algorithms that can provide direct measurement of the function and status of the underlying muscles. This will allow development of dynamic control allocation algorithms that utilize this information to distribute control between actuation and stimulation. This approach for closed-loop control based on muscle-specific feedback represents a paradigm shift from conventional lower extremity exoskeletons that rely only on joint kinematics for feedback. As a testbed for this new approach, the team will utilize a hybrid exoskeleton that combines active joint actuators with functional electrical stimulation of a person's own muscles. Repetitive electrical stimulation leads to the rapid onset of muscle fatigue that limits the utility of these hybrid systems and potentially increases risk of injury. The goals of the project are: develop novel ultrasound sensing technology and image analysis algorithms for real-time sensing of muscle function and fatigue; investigate closed-loop control allocation algorithms utilizing measured muscle contraction rates to minimize fatigue; integrate sensing and control methods into a closed loop hybrid exoskeleton system and evaluate on patients with spinal cord injury. The proposed approach will lead to innovative CPS science by (1) integrating a human-in-the-loop physical exoskeleton system with novel image-based real-time robust sensing of complex time-varying physical phenomena, such as dynamic neuromuscular activity and fatigue, and (2) developing novel computational models to interpret such phenomena and effectively adapt control strategies. This research will enable practical wearable image-based biosensing, with broader applications in healthcare. This framework can be widely applicable in a number of medical CPS problems that involve a human in the loop, including upper and lower extremity prostheses and exoskeletons, rehabilitation and surgical robots. The new control allocation algorithms relying on sensor measurements could have broader applicability in fault-tolerant and redundant actuator systems, and reliable fault-tolerant control of unmanned aerial vehicles.

The Planning Grants for Engineering Research Centers competition was run as a pilot solicitation within the ERC program. Planning grants are not required as part of the full ERC competition, but intended to build capacity among teams to plan for convergent, center-scale engineering research.

Substance Use Disorders (SUDs) have significant negative consequences for the affected individuals, their families and communities, as well as society due to the significant public health and economic burden. While the opioid epidemic currently ravaging communities throughout the US has garnered national attention, SUDs are a long-standing challenge, involve a variety of substances, and are expected to be of societal concern for the foreseeable future. In recent years, there has been a marked shift towards a solution-oriented recovery paradigm for addressing SUDs that emphasizes non-clinical alternatives to treatment, early identification of at-risk behavior, post-treatment monitoring and relapse prevention, and sustaining long-term recovery through strengthening community networks. In such a paradigm, engineering approaches can address important gaps in knowledge and build capacity for preventing relapse and maintaining recovery. In this planning grant, George Mason University and our partners seek to conduct the planning activities necessary to create a powerful and impactful Engineering Research Center (ERC) focused on empowering communities of recovery around individuals with SUDs using engineered systems to achieve measurable improvements in long-term health and wellness. The proposed planning activities will engage students from diverse backgrounds, such as neuroscience, psychology, nursing, computer science, electrical engineering and bioengineering to work together using a convergence approach to tackle a significant societal challenge.

The objectives of this ERC planning grant are to: (1) strengthen relationships between academic partners and SUDs stakeholder communities, (2) form a diverse, multidisciplinary ERC team, a high-performing executive leadership team, and a robust, community-engaged governance structure to guide implementation of the ERC programmatic activities across all four foundational elements (Research, Workforce Development, Culture of Inclusion, Innovation Ecosystem) and (3) develop a comprehensive strategic framework, building around a collective vision for supporting technology-empowered communities of recovery. The planning grant team will formulate a number of hypotheses about current barriers and gaps in research knowledge, and engineering approaches that can be used to address these gaps. We will then iteratively update these hypotheses and solutions based on analysis of collected evidence from stakeholders and develop a framing paper. We will organize a strategic planning retreat with key stakeholders to develop a research agenda. Based on the identified research agenda, we will identify an appropriate ERC leadership team and organize a national symposium to refine the motivating vision and critical needs. The anticipated benefits of the planning grant will include a coordination of efforts across different communities, identification of stakeholder needs and priorities with long-, mid- and near-term impact, documentation of best practices and development of a convergent multi-system framework for addressing the underlying challenges using an engineered system approach at multiple scales, from individuals, family and caregivers, and community support networks.

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.

This project promotes the progress of data science to address the opioid overdose crisis currently ravaging communities across the nation, as well as to address substance use disorders more broadly. Numerous local, state, and federal efforts are underway to collect data relevant to substance use epidemiology, the availability of services, and response strategies. The challenge is that though there is a large volume of diverse and increasingly public data sources (e.g., national epidemiology surveys, mortality records, prescription drug monitoring, housing, health claims), these data sources are often fragmented, siloed in isolated portals, restricted by data-sharing agreements, and are difficult to use as there are no uniform standards for data collection or dissemination. The strategies necessary for linking these data to generate meaningful, actionable knowledge that is easily accessible for different stakeholder communities have not been developed. This project will develop and disseminate an open platform that will extract and integrate relevant data and provide toolkits that will enable stakeholder groups to take timely, appropriate action to manage substance use and other health and social challenges in their communities.

To achieve this objective, the project brings together an interdisciplinary team from academia, industry, and government, with expertise in engineering, physical and social science disciplines to accomplish three tasks: 1) The team will create an open data sharing environment based at George Mason University. 2) The team will develop and test a data extraction and integration layer that will aggregate data appropriately to comply with confidentiality constraints from data stewards of proprietary data sets. The data integration will enable multiple disparate datasets to be linked at multiple geographical scales using de-identified profiles. 3) The team will work with domain experts and community stakeholders to develop, deploy, and refine data-driven analytics methods and toolkits for use cases to answer pressing community needs. The project will result in an open data framework with the potential to transform the current status quo of a crisis-driven acute system of care for substance abuse to data-driven pre-emptive, precision-targeted system of care. The open data framework has the potential to enable communities and government agencies to improve policies and practices through improved identification of relevant factors, prediction, targeting and coordination of resources, interventions and evaluation for managing substance use disorders.

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 vast majority of all trauma-related amputations in the United States involve the upper limbs. Approximately half of those individuals who receive an upper extremity myoelectric prosthesis eventually abandon use of the system, primarily because of their limited functionality. Thus, there continues to be a need for a significant improvement in prosthetic control strategies. The objective of this bioengineering research program is to develop and clinically evaluate a prototype prosthetic control system that uses imaging to sense residual muscle activity, rather than electromyography. This novel approach can better distinguish between different functional compartments in the forearm muscles, and provide robust control signals that are proportional to muscle activity. This improved sensing strategy has the potential to significantly improve functionality of upper extremity prostheses, and provide dexterous intuitive control that is a significant improvement over current state of the art noninvasive control methods. This interdisciplinary project brings together investigators at George Mason University, commercial partners at Infinite Biomedical Technologies and clinicians at MedStar National Rehabilitation Hospital and Hanger Clinic. Specific Aim 1: To develop and test a compact research-grade sonomyographic prosthetic system We will develop and evaluate a compact low-power embedded system for sonomyography. We will optimize and implement algorithms for real-time classification and control with multiple degrees of freedom (DOF). We will then integrate ultrasound imaging transducers within test prosthetic sockets for testing on individuals with transradial limb loss in a laboratory setting. We will complete system integration and testing and evaluate the sonomyographic signal quality with changes in arm position and socket loading. Specific Aim 2: To evaluate performance of sonomyographic control compared to myoelectric control We will compare the performance of SMG vs myoelectric direct control with mode switching in myoelectric- naïve subjects with transradial amputation. Assessment will be performed using a virtual reality Fitts? law task as well as clinical outcome measures using a terminal device. The primary outcome measure will be the SHAP and secondary outcome measure will be the Clothespin Relocation Task. We will assess intuitiveness of control using gaze tracking, and also study quality of movement. We will also compare the performance of SMG vs myoelectric pattern recognition with proportional control in subjects who have been trained on a commercial PR system using the same outcome measures. The successful completion of this project will lead to the first in human evaluation of an integrated prototype that uses low-power portable imaging sensors and real-time image analysis to sense residual muscle activity for prosthetic control. In the long term, we anticipate that the improvements in functionality and intuitiveness of control will increase acceptance by amputees.