Sridevi V Sarma, Ph.D. - US grants

Tracking fast unpredictable movements is a valuable skill, applicable in many situations. In the animal kingdom, the context includes the action of a predator chasing its prey that is running and dodging at high speeds, like a cheetah chasing a gazelle. The sensorimotor control system (SCS) is responsible for such actions and its performance clearly depends on the computing power of neurons, delays between brain and muscles, and the dynamics of muscles involved. Despite these obvious factors that set the limits on how fast an animal can track a moving object, tracking performance of the SCS and its dependence on neural computing, delays, and muscle dynamics have not been explicitly quantified. In this program, we will build upon new theory developed using feedback control principles and an appropriately simplified model of the SCS to identify how neural computing, delays, and muscles interact during the generation of fast movements. Therefore if one component is compromised, we can take advantage of the other components to restore motor performance with assistive neuroprosthetic devices. The program objectives are to first parameterize the major factors (brain and body) limiting fast movements and to derive how these parameters must interact to achieve tracking of fast movements in the SCS. Then, the parameterization and quantified interactions will be tested experimentally in subjects through manipulation of (i) neural computing power, (ii) transmission delays, and (iii) muscle dynamics. If discrepancies emerge between experiments and theory, the SCS model and theory will be modified to explain observation data. Finally, the theoretical model of interactions required to achieve tracking of fast movements will be exploited to apply compensation to account for degradation of some parameters by boosting others. More specifically, we will design assistive neuroprosthetic devices for subjects having compromised neural real estate to restore performance of fast movements. For example, if primary motor cortex is compromised due to disease or damage, we can manipulate muscle dynamics by adding the necessary compensatory forces to restore motor performance, and more importantly restore fast and agile movements. Just how one should compensate will be informed by our SCS model and theory.

Chronic pain affects -100 million adults in the US, and is inadequately treated with drugs, that are often toxic and have side effects (e.g., addiction). Electrical stimulation in targeted nerve fibers is a promising new therapy, but has had suboptimal efficacy and limited long-term success as its mechanisms of action are unclear. Complementary therapies, such as acupuncture and massage that also involve neuromodulation as a mode of action, have not been quantitatively assessed. Critical to advancing pain therapy is a deeper mechanistic understanding of how a nociceptive signal is processed and modulated in spinal dorsal horn (DH), the first central relay station of nociceptive signaling. There are 3 major functionally distinct subsets of neurons in the DH that play different roles in pain transmission. Excitatory neurons and inhibitory neurons form important local pain circuitry that modulates activity of projection neurons that send ascending pain signals to the brain. It is critical to understand the specific roles for each neuron subset and the therapeutic actions of neurostimulation, tactile inputs, and drugs. For example, do they respond differently to different therapies? Can certain patterns of stimulation selectively inhibit or excite any subset neurons to maximize pain inhibition? These fundamental questions could not be easily addressed in a quantitative manner before this study. First, experimental barriers limit probing the DH to uncover the circuit topology, because it has been difficult to differentiate different subsets of DH neurons while simultaneously studying their physiological properties. Computational models of the DH, on the other hand, can predict how changes in sensory inputs influence pain transmission, but current models are hand-tuned, assume a fixed circuitry, nonlinear, high dimensional and thus intractable for sensitivity analysis - rendering a computational barrier. We will break these barriers and will construct a tractable data-driven computational model of the DH that enables powerful predictions on how different treatments alter neuronal activity in the DH. State-of-the-art electrophysiological techniques and powerful mouse genetic approaches will delineate the effects of sensory stimuli and stimulation on various subsets of DH neurons, and these data will be used to estimate the parameters and circuit topology of a mechanistic model of the DH. Model reduction will then be applied to generate a tractable characterization of the DH enabling sensitivity analysis. Developing and validating this innovative model will allow predictions that may differentiate various pain treatments and integrative approaches that can be readily tested in animals. RELEVANCE (See instructions): Chronic pain affects about 100 million adults in the US, but remains inadequately treated. Critical to advancing pain therapy is a deeper mechanistic understanding of how a nociceptive signal is processed and modulated in spinal dorsal horn (DH), the first central relay station of nociceptive signaling. We will combine state-of-the-art electrophysiological techniques and mouse genetic approaches with system identification tools to construct a tractable computational model of the DH that will enable powerful predictions on how different treatments alter neuronal activity in the DH.

Decision-making is one of the most central cognitive functions of importance at practically all levels of society. In many real-world decisions, which of the available alternatives is chosen is influenced by many different attributes. Such multi-attribute decisions are complex because they require the integration and comparison of many pieces of information. For instance, selecting the bundle of goods that maximizes value given a budget constraint in a supermarket that only stocks 100 different goods requires checking approximately 10^30 possible combinations. For this reason, humans do not use rational choice theory in all their decisions. In addition to having to combine the influence of all the different attributes, another complexity is that one alternative is often preferable on one set of attributes, but another is preferred on others. Making a choice then requires a trade-off, which further complicates the decision process. However, the cognitive and neural processes that are at the heart of preference formation are still poorly understood. This complexity is thought to tax limited cognitive resources in humans who therefore can pay attention only to a limited set of information, on which the decision is then based. In addition, task history often systematically changes decision biases. This research program takes advantage of the opportunity to obtain direct recordings from individual's brains while they perform such complex decision. It will study these activity patterns to determine whether they can be explained via mathematical models of decision making. Understanding which attributes are considered during decision making, and how they are weighted could explain decision making in typical and a-typical populations. Furthermore this integrative research program forms an opportunity to expose engineering students to dynamical systems and control theories in an interdisciplinary context.

This project combines behavioral data, neural recordings in humans (patients undergoing epilepsy evaluation) implanted with multiple depth electrodes covering many cortical and subcortical brain areas, and computational approaches to develop a new theory of the neural mechanisms underlying multi-attribute decision-making in complex environments. This is a unique opportunity to study brain circuits simultaneously across multiple brain areas while humans make these decisions. The overall goal of the present proposal is to understand the neural circuit involved in (1) representing the relevant decision variables, (2) integrating these variables to form subjective values, and (3) selecting one of the options in multi-attribute decisions. Participants, with implanted electrodes, will work in a novel behavioral task that makes it possible to observe their focus of attention while they evaluate the offers and select one of them. Data will constrain cutting edge computational models of multi-attribute decision making that will combine: (i) a procedural model of the decision in each trial, and (ii) a latent variable model of biasing influence on decision-making resulting from past trial history. The computational models will make it possible to identify neuronal activity that represents task-relevant variables and the dynamic flow of information across the different elements of the identified neural circuit.

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.

PROJECT SUMMARY Epilepsy affects approximately 70 million people worldwide. About 30% of epilepsy patients are drug resistant and must consider invasive alternatives such as resective surgery, and electrical stimulation therapy. Surgical candidates must have a well-localized focus in an area outside of eloquent brain structures. Although surgery can dramatically improve the lives of patients, it is irreversible and outcomes are highly variable (30-70% success rates). Electrical stimulation, on the other hand, is reversible and has great potential. Chronic open-loop stimulation has shown some efficacy, but does not account for dynamic brain activity and the continuously changing state of the patient, making it suboptimal and crude. To maximize therapeutic effects, new methods must be developed for fine dynamic tuning of stimulation parameters in a patient-specific manner. Closed-loop therapy provides an attractive option that minimizes intervention by limiting the delivery of therapy to times when the patient is in need. Efforts have been made to develop ?closed-loop? stimulation strategies using different protocols, yet none provide a highly effective and reliable solution. All closed-loop strategies proposed and studied are actually ?responsive switches? and haven?t produced reliable results that translate to the clinic. These strategies wait until a seizure is detected (via a detection algorithm) and then stimulate with a fixed pattern to suppress the seizure. In contrast, we will implement real closed-loop control that continuously steers the neural network away from seizure genesis entirely using adaptive stimulation patterns that change with EEG measurements - avoiding seizure detection and seizures altogether. To meet this objective, we plan to use in vivo experimental data to develop an innovative mathematical model that characterizes fundamental neural dynamics during seizure genesis, and the effects of different electrical stimuli on neural activity leading to seizure genesis. Based on this model, we will then design and implement a feedback controller that monitors neural activity in real-time to prevent seizures from evolving in the network. In particular, the controller will steer temporal patterns of stimulation to disrupt pre-seizure activity with minimal energy consumption. To accomplish our goals, we have assembled a highly interdisciplinary team with expertise in system identification, control, and experimental neurophysiology.

The novel Coronavirus (COVID-19) is one of four infectious diseases caused by the SARS-CoV-2 virus. Although the clinical signs and patient symptoms of this complicated disease vary in presentation and severity, clinicians and investigators have reported constitutional symptoms (cough and fever), upper and lower respiratory tract symptoms, as well as gastrointestinal symptoms. Among the most concerning is the life threatening acute respiratory distress syndrome (ARDS) in patients. The pathophysiology of severe ARDS results from a rapid decline in pulmonary function and requires intubation of patients in critical condition for invasive mechanical ventilation to combat lung recruitability, reduced peripheral capillary oxygen saturation (SpO2) and risks of organ failure and death. Ventilator settings to increase SpO2 and oxygen delivery is achieved with positive end-expiratory pressure (PEEP). However, controlling ventilation at a high PEEP for extended periods of time significantly increases risk for ventilator-associated lung injury (VALI). This RAPID project will develop novel engineering strategies for optimal ventilator control to maximize SpO2 in minimal time, while minimizing PEEP and the duration of ventilator use are needed to minimize VALI and subsequent complications, and to improve favorable patient outcomes. In the management of patients with COVID-19, these strategies are significant to optimize oxygen delivery, minimal invasive ventilator use and mechanical lung injury. Further, the understanding of ventilator requirements and operative settings highlights the need for available ventilators. The management of severe ARDS is complicated and strategies and protocols are desperately needed.

To achieve this goal, we will develop data-driven linear parameter-varying (LPV) dynamical systems models that relate patient clinical state and ventilator inputs to the output variable patient SpO2. Patient state will be characterized using data from the electronic health record (EHR) and minute-by-minute physiological time-series (PTS) data (e.g., heart rate, respiratory rate, SpO2) acquired from patient monitoring. We will first develop the LPV model using retrospective data from non-COVID-19 patients who are on ventilators to help treat conditions such as pneumonia and ARDS. Then, we will test the predictive capabilities of the LPV model in COVID-19 patients who are placed on ventilators. Finally, we will develop an optimal ventilator control strategy for COVID-19 patients to regulate SpO2 levels in ICU patients based on the LPV model. Attempting to control a complex biological system using control strategies based on mechanistic models is generally intractable. However, the LPV framework allows for sophisticated optimal strategies to be implemented that not only allow for better performance than other classical methods, but also provides stability and performance guarantees.

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.

Absence of pain sensation; Acute Pain; Address; Anesthesia procedures; Animals; Back; base; Brain; cell type; chronic neuropathic pain; chronic pain; computer framework; Computer Models; Data; Data Set; Deep Brain Stimulation; design; effective therapy; Electric Stimulation; Electrodes; Electrophysiology (science); Engineering; Exhibits; Feedback; Frequencies; Human; Hyperalgesia; Hypersensitivity; in silico; in vivo; in vivo evaluation; Injury; Local Anesthetics; Location; mathematical model; Measures; Mechanics; model design; Modeling; Nerve Fibers; nerve injury; Neurons; neuroregulation; novel; opioid epidemic; Pain; Pain management; pain receptor; pain signal; painful neuropathy; Painless; Pathologic; Pathway interactions; Patients; Perception; Peripheral nerve injury; Peripheral Nerve Stimulation; Pharmacologic Substance; Physiologic pulse; Population; predictive modeling; Prevalence; programs; Rattus; Research; response; restoration; Role; sciatic nerve; side effect; Signal Transduction; Societies; Spinal Cord; Spinal cord posterior horn; Stimulus; Stroke; Syndrome; System; Techniques; Technology; Testing; Thalamic structure; Therapeutic; therapy design; Time; Translations; Update; Width;