Bayesian Inference with Combined Dynamic and Sparsity Models: Application in 3D Electrophysiological Imaging

Data-driven inference is widely encountered in various scientific domains to convert the observed measurements into information that cannot be directly observed about a system. Despite the quickly-developing sensor and imaging technologies, in many domains, data collection remains an expensive endeavor due to financial and physical constraints. To overcome the limits in data and to reduce the demand on expensive data collection, it is important to incorporate prior information in order to place the data-driven inference in a domain-relevant context and to improve its accuracy. Two sources of assumptions have been used successfully in many inverse problem applications. One is the temporal dynamics of the system (dynamic structure). The other is the low-dimensional structure of a system (sparsity structure). In existing work, these two structures have often been explored separately, while in most high-dimensional dynamic system they are commonly co-existing and contain complementary information. In this work, our main focus is to build a robustness inference framework to combine dynamic and sparsity constraints. The driving application in this work is a biomedical inverse problem of electrophysiological (EP) imaging, which noninvasively and quantitatively reconstruct transmural action potentials from body-surface voltage data with the goal to improve cardiac disease prevention, diagnosis, and treatment. The general framework can be extended to a variety of applications that deal with the inference of high-dimensional dynamic systems

Functional Effects of Calcium Regulation of Thin Filaments at Single Particle Resolution

Heart disease is the leading cause of death in the United States. Understanding heart function at the molecular level is critical for developing of more effective treatments. In the cardiac muscle, the thin filament is composed by troponin (Tn), tropomyosin (Tm), and F-actin. It provides Ca2+-dependent regulation of contraction by modulating myosin attachment and force generation in a cooperative scheme. However, this mechanism remains unclear. To understand thin filament activation, we studied the binding and functional properties of Tn and Tm to F-actin at single particle resolution by employing fluorescence image colocalization, in vitro motility assays, and Förster resonance energy transfer based on fluorescence lifetime imaging (FLIM-FRET). Our results suggest that under physiologically relevant conditions, Tn and Tm binding to Factin is not cooperative and it is not affected by Ca2+. This suggests that one single type of interaction is involved in fully regulated thin filaments. Thin filament activation has been confirmed by in vitro motility assays, where phosphorylation of serine 23 and 24 in TnI, truncation of TnT C-terminal region, and incorporation of a Ca2+ desensitizer altered the Ca2+ response to filament sliding. FLIM-FRET measurements revealed an allosteric dependence on thin filament activation as a function of myosin and Ca2+. Our results provide evidence of multiple allosteric elements within thin filaments responsible for the molecular modulation of cardiac muscle activation

Cortical motor prosthetics: the development and use for paralysis

The emerging research field of Brain Computer Interfaces (BCIs) has created an invasive type of BCI, the Cortical Motor Prosthetic (CMP) or invasive BCI (iBCI). The goal is to restore lost motor function via prosthetic control signals to individuals who have long-term paralysis. The development of the CMP consists of two major entities: the implantable, chronic microelectrode array (MEA) and the data acquisition hardware (DAQ) specifically the decoder. The iBCI's function is to record primary motor cortex (M1) neural signals via chronic MEA and translate into a motor command via decoder extraction algorithms that can control a prosthetic to perform the intended movement. The ultimate goal is to use the iBCI as a clinical tool for individuals with long-term paralysis to regain lost motor functioning. Thus, the iBCI is a beacon of hope that could enable individuals to independently perform daily activities and interact once again with their environment. This review seeks to accomplish two major goals. First, elaborate upon the development of the iBCI and focus on the advancements and efforts to create a viable system. Second, illustrate the exciting improvements in the iBCI's use for reaching and grasping actions and in human clinical trials. The ultimate goal is to use the iBCI as a clinical tool for individuals with long-term paralysis to regain movement control. Despite the promise in the iBCI, many challenges, which are described in this review, persist and must be overcome before the iBCI can be a viable tool for individuals with long-term. iBCI future endeavors aim to overcome the challenges and develop an efficient system enhancing the lives of many living with paralysis. Standard terms: Intracortical Brain Computer Interface (iBCI), Intracortical Brain Machine Interface (iBMI), Cortical Motor Prosthetic (CMP), Neuromotor Prostheses (NMP), Intracortical Neural Prosthetics, Invasive Neural Prosthetic all terms used interchangeabl

Scaling Multidimensional Inference for Big Structured Data

In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications [151]. In a world of increasing sensor modalities, cheaper storage, and more data oriented questions, we are quickly passing the limits of tractable computations using traditional statistical analysis methods. Methods which often show great results on simple data have difficulties processing complicated multidimensional data. Accuracy alone can no longer justify unwarranted memory use and computational complexity. Improving the scaling properties of these methods for multidimensional data is the only way to make these methods relevant. In this work we explore methods for improving the scaling properties of parametric and nonparametric models. Namely, we focus on the structure of the data to lower the complexity of a specific family of problems. The two types of structures considered in this work are distributive optimization with separable constraints (Chapters 2-3), and scaling Gaussian processes for multidimensional lattice input (Chapters 4-5). By improving the scaling of these methods, we can expand their use to a wide range of applications which were previously intractable open the door to new research questions

Machine Analysis of Facial Expressions

No abstract

Personalized noninvasive imaging of volumetric cardiac electrophysiology

Three-dimensionally distributed electrical functioning is the trigger of mechanical contraction of the heart. Disturbance of this electrical flow is known to predispose to mechanical catastrophe but, due to its amenability to certain intervention techniques, a detailed understanding of subject-specific cardiac electrophysiological conditions is of great medical interest. In current clinical practice, body surface potential recording is the standard tool for diagnosing cardiac electrical dysfunctions. However, successful treatments normally require invasive catheter mapping for a more detailed observation of these dysfunctions. In this dissertation, we take a system approach to pursue personalized noninvasive imaging of volumetric cardiac electrophysiology. Under the guidance of existing scientific knowledge of the cardiac electrophysiological system, we extract the subject specific cardiac electrical information from noninvasive body surface potential mapping and tomographic imaging data of individual subjects. In this way, a priori knowledge of system physiology leads the physiologically meaningful interpretation of personal data; at the same time, subject-specific information contained in the data identifies parameters in individual systems that differ from prior knowledge. Based on this perspective, we develop a physiological model-constrained statistical framework for the quantitative reconstruction of the electrical dynamics and inherent electrophysiological property of each individual cardiac system. To accomplish this, we first develop a coupled meshfree-BE (boundary element) modeling approach to represent existing physiological knowledge of the cardiac electrophysiological system on personalized heart-torso structures. Through a state space system approach and sequential data assimilation techniques, we then develop statistical model-data coupling algorithms for quantitative reconstruction of volumetric transmembrane potential dynamics and tissue property of 3D myocardium from body surface potential recoding of individual subjects. We also introduce a data integration component to build personalized cardiac electrophysiology by fusing tomographic image and BSP sequence of the same subject. In addition, we develop a computational reduction strategy that improves the efficiency and stability of the framework. Phantom experiments and real-data human studies are performed for validating each of the framework’s major components. These experiments demonstrate the potential of our framework in providing quantitative understanding of volumetric cardiac electrophysiology for individual subjects and in identifying latent threats in individual’s heart. This may aid in personalized diagnose, treatment planning, and fundamentally, prevention of fatal cardiac arrhythmia

Moving On:Measuring Movement Remotely after Stroke

Most persons with stroke suffer from motor impairment, which restricts mobility on one side, and affects their independence in daily life activities. Measuring recovery is needed to develop individualized therapies. However, commonly used clinical outcomes suffer from low resolution and subjectivity. Therefore, objective biomechanical metrics should be identified to measure movement quality. However, non-portable laboratory setups are required in order to measure these metrics accurately. Alternatively, minimal wearable systems can be developed to simplify measurements performed at clinic or home to monitor recovery. Thus, the goal of the thesis was ‘To identify metrics that reflect movement quality of upper and lower extremities after stroke and develop wearable minimal systems for tracking the proposed metrics’. Section Upper Extremity First, we systematically reviewed literature ( Chapter II ) to identify metrics used to measure reaching recovery longitudinally post-stroke. Although several metrics were found, it was not clear how they differentiated recovery from compensation strategies. Future studies must address this gap in order to optimize stroke therapy. Next, we assessed a ‘valid’ measure for smoothness of upper paretic limb reaching ( Chapter III ), as this was commonly used to measure movement quality. After a systematic review and simulation analyses, we found that reaching smoothness is best measured using spectral arc length. The studies in this section offer us a better understanding of movement recovery in the upper extremity post-stroke. Section Lower Extremity Although metrics that reflect gait recovery are yet to be identified, in this section we focused on developing minimal solutions to measure gait quality. First, we showed the feasibility of 1D pressure insoles as a lightweight alternative for measuring 3D Ground Reaction Forces (GRF) ( Chapter IV ). In the following chapters, we developed a minimal system; the Portable Gait Lab (PGL) using only three Inertial Measurement Units (IMUs) (one per foot and one on the pelvis). We explored the Centroidal Moment Pivot (CMP) point ( Chapter V ) as a biomechanical constraint that can help with the reduction in sensors. Then, we showed the feasibility of the PGL to track 3D GRF ( Chapters VI-VII ) and relative foot and CoM kinematics ( Chapter VIII-IX ) during variable overground walking by healthy participants. Finally, we performed a limited validation study in persons with chronic stroke ( Chapter X ). This thesis offers knowledge and tools which can help clinicians and researchers understand movement quality and thereby develop individualized therapies post-stroke

Transcranial Magnetic Stimulation and Neuroimaging Coregistration

The development of neuroimaging techniques is one of the most impressive advancements in neuroscience. The main reason for the widespread use of these instruments lies in their capacity to provide an accurate description of neural activity during a cognitive process or during rest. This important advancement is related to the possibility to selectively detect changes of neuronal activity in space and time by means of different biological markers. Specifically, functional magnetic resonance imaging (fMRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and nearinfrared spectroscopy (NIRS) use metabolic markers of ongoing neuronal activity to provide an accurate description of the activation of specific brain areas with high spatial resolution. Similarly, electroencephalography (EEG) is able to detect electric markers of neuronal activity, providing an accurate description of brain activation with high temporal resolution. The application of these techniques during a cognitive task allows important inferences regarding the relation between the detected neural activity, the cognitive process involved in an ongoing task, and behaviour: this is known as a \u201ccorrelational approach\u201d

Particle filtering for EEG source localization and constrained state spaces

Particle Filters (PFs) have a unique ability to perform asymptotically optimal estimation for non-linear and non-Gaussian state-space models. However, the numerical nature of PFs cause them to have major weakness in two important areas: (1) handling constraints on the state, and (2) dealing with high-dimensional states. In the first area, handling constraints within the PF framework is crucial in dynamical systems, which are often required to satisfy constraints that arise from basic physical laws or other considerations. The current trend in constrained particle filtering is to enforce the constraints on all particles of the PF. We show that this approach leads to more stringent conditions on the posterior density that can cause incorrect state estimates. We subsequently describe a novel algorithm that restricts the mean estimate without restricting the posterior pdf, thus providing a more accurate state estimate. In the second area, we tackle the curse of dimensionality, which causes the PF to require an exponential increase in computational complexity as the dimension of the state increases. The application of interest is localization of the brain neural generators that create the Electroencephalogram (EEG) signal. Specifically, we describe a state-space model that tracks the position and moments of multiple dynamic dipoles and apply the marginalized PF, which alleviates the curse of dimensionality for tracking multiple dynamic dipoles. This modified framework allows us to consider dynamic dipoles, which were historically considered time-invariant