Functional brain imaging on mobile devices by solving the EEG inverse problem: a structured sparsity approach

Mención Internacional en el título de doctorIn this thesis we address the development of a mobile brain scanner, which is based on a wireless EEG neuroheadset, in charge of acquiring and transmitting the electrical potential measured on the scalp, and one mobile device (smartphone or tablet), in charge of receiving and processing these data to produce the cortical activation maps, which show, using a 3D brain model, the brain areas that are currently active. To generate the cortical activation maps, the mobile brain scanner needs to solve an electromagnetic inverse problem called the EEG inverse problem. The low spatial resolution of the EEG caused by the low conductivity of the skull plus the small number of EEG sensors available to capture the electrical activity produced by thousands of brain current sources, imply that the EEG inverse problem is underdetermined, ill-posed, and has infinite solutions. To make this problem tractable, in this thesis we assume that the number of active sources is small, that is, we assume that the set of active sources is a sparse set. Additionally, we also assume a linear relationship between the elements of this set. If we represent the set of brain current sources as a matrix (called the sources matrix), where the rows denote how the electrical activity of the sources vary over time, then the former assumptions lead to estimate a sources matrix which is structured sparse and low rank. To solve this problem, in this thesis we propose a method based on the factorization of the sources matrix as a product of two matrices: the first one encodes the spatial dynamics of the sources (how they change their spatial activation patterns), whereas the second one encodes their corresponding temporal dynamics (how they change their electrical activity over time). This method combines the ideas of the Group Lasso (structured sparsity) and Trace Norm (low rank) into one unified framework. We also develop and analyze the convergence of an alternating minimization algorithm to solve the resulting nonsmooth-nonconvex regularization problem. Finally, in order to implement a working prototype of the mobile brain scanner, we bring our method to a real life scenario: online solving of the EEG inverse problem on a mobile device, which is continuously supplied with EEG data coming from the wireless EEG neuroheadset.En esta tesis se aborda el desarrollo de un escáner móvil cerebral, el cual está basado en un casco inalámbrico que captura y transmite señales electroencefalográficas (EEG) a un dispositivo móvil (teléfono inteligente o tableta). Este las recibe y procesa con el fin de generar mapas de activación cerebral, los cuales muestran qué áreas de la corteza cerebral están actualmente activas. Estos mapas son visualizados en la pantalla del dispositivo móvil usando un modelo en 3D del cerebro. Para generar estos mapas de activación, el escáner móvil cerebral debe resolver el problema inverso del EEG. La baja resolución espacial del EEG, causada por la baja conductividad eléctrica del cráneo, añadida al bajo número de sensores EEG disponibles para capturar la actividad eléctrica generada por miles de fuentes cerebrales, hacen que el problema inverso del EEG sea mal condicionado e indeterminado, admitiendo un número infinito de soluciones. Para disminuir la dificultad de este problema, en esta tesis se asume que el número de fuentes eléctricas cerebrales, activadas por un determinado estímulo, es bajo; es decir, se asume que el conjunto de fuentes activas es un conjunto disperso. Adicionalmente, también se asume la existencia de una relación lineal entre los elementos de dicho conjunto. Si se representa el conjunto de las fuentes eléctricas cerebrales usando una matriz, llamada de aquí en adelante matriz de fuentes, las anteriores hipótesis conducen a estimar una matriz de fuentes que sea dispersa, estructurada y de bajo rango. Para resolver este problema, en esta tesis se propone un método basado en la factorización de la matriz de fuentes como el producto de dos matrices: la primera codifica la dinámica espacial de las fuentes (cómo cambian sus patrones de activación), mientras que la segunda incorpora la dinámica temporal de las fuentes (cómo cambian su actividad eléctrica en el tiempo). Este método combina las ideas de dos regularizadores: el regularizador de Grupo Lasso (dispersidad estructurada) y el regularizador de norma nuclear (bajo rango). Para resolver el problema de estimación resultante, el cual es no convexo y no diferenciable, en esta tesis se desarrolla, y también se analiza la convergencia, de un algoritmo de minimización por etapas. Finalmente, para llevar a cabo la implementación de un prototipo funcional del escáner móvil cerebral, se ha trasladado el método propuesto a un escenario de la vida real: solución, en línea, del problema inverso del EEG en un dispositivo móvil, al cual le llegan continuamente datos EEG provenientes del casco inalámbrico de captura de datos.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Joaquín Míguez Arenas- Vocal: Mónica Fernández Bugallo.

Multi-level characterization and information extraction in directed and node-labeled functional brain networks

Current research in computational neuroscience puts great emphasis on the computation and analysis of the functional connectivity of the brain. The methodological developments presented in this work are concerned with a group-specific comprehensive analysis of networks that represent functional interaction patterns. Four application studies are presented, in which functional brain network samples of different clinical background were analyzed in different ways, using combinations of established approaches and own methodological developments. Study I is concerned with a sample-specific decomposition of the functional brain networks of depressed subjects and healthy controls into small functionally important and recurring subnetworks (motifs) using own developments. Study II investigates whether lithium treatment effects are reflected in the functional brain networks of HIV-positive subjects with diagnosed cognitive impairment. For it, microscopic and macroscopic structural properties were analyzed. Study III explores spatially highly resolved functional brain networks with regard to a functional segmentation given by identified module (community) structure. Also, ground truth networks with known module structure were generated using own methodological developments. They formed the basis of a comprehensive simulation study that quantified module structure quality and preservation in order to evaluate the effects of a novel approach for the identification of connectivity (lsGCI). Study IV tracks the time-evolution of module structure and introduces a newly developed own approach for the determination of edge weight thresholds based on multicriteria optimization. The methodological challenges that underly these different topological analyses, but also the various opportunities to gain an improved understanding of neural information processing among brain areas were highlighted by this work and the presented results

Imaging fascicular organisation in mammalian vagus nerve for selective VNS

Nerves contain a large number of nerve fibres, or axons, organised into bundles known as fascicles. Despite the somatic nervous system being well understood, the organisation of the fascicles within the nerves of the autonomic nervous system remains almost completely unknown. The new field of bioelectronics medicine, Electroceuticals, involves the electrical stimulation of nerves to treat diseases instead of administering drugs or performing complex surgical procedures. Of particular interest is the vagus nerve, a prime target for intervention due to its afferent and efferent innervation to the heart, lungs and majority of the visceral organs. Vagus nerve stimulation (VNS) is a promising therapy for treatment of various conditions resistant to standard therapeutics. However, due to the unknown anatomy, the whole nerve is stimulated which leads to unwanted off-target effects. Electrical Impedance Tomography (EIT) is a non-invasive medical imaging technique in which the impedance of a part of the body is inferred from electrode measurements and used to form a tomographic image of that part. Micro-computed tomography (microCT) is an ex vivo method that has the potential to allow for imaging and tracing of fascicles within experimental models and facilitate the development of a fascicular map. Additionally, it could validate the in vivo technique of EIT. The aim of this thesis was to develop and optimise the microCT imaging method for imaging the fascicles within the nerve and to determine the fascicular organisation of the vagus nerve, ultimately allowing for selective VNS. Understanding and imaging the fascicular anatomy of nerves will not only allow for selective VNS and the improvement of its therapeutic efficacy but could also be integrated into the study on all peripheral nerves for peripheral nerve repair, microsurgery and improving the implementation of nerve guidance conduits. Chapter 1 provides an introduction to vagus nerve anatomy and the principles of microCT, neuronal tracing and EIT. Chapter 2 describes the optimisation of microCT for imaging the fascicular anatomy of peripheral nerves in the experimental rat sciatic and pig vagus nerve models, including the development of pre-processing methods and scanning parameters. Cross-validation of this optimised microCT method, neuronal tracing and EIT in the rat sciatic nerve was detailed in Chapter 3. Chapter 4 describes the study with microCT with tracing, EIT and selective stimulation in pigs, a model for human nerves. The microCT tracing approach was then extended into the subdiaphragmatic branches of the vagus nerves, detailed in Chapter 5. The ultimate goal of human vagus nerve tracing was preliminarily performed and described in Chapter 6. Chapter 7 concludes the work and describes future work. Lastly, Appendix 1 (Chapter 8) is a mini review on the application of selective vagus nerve stimulation to treat acute respiratory distress syndrome and Appendix 2 is morphological data corresponding to Chapter 4

Array Designer: automated optimized array design for functional near-infrared spectroscopy

The position of each source and detector "optode" on the scalp, and their relative separations, determines the sensitivity of each functional near-infrared spectroscopy (fNIRS) channel to the underlying cortex. As a result, selecting appropriate scalp locations for the available sources and detectors is critical to every fNIRS experiment. At present, it is standard practice for the user to undertake this task manually; to select what they believe are the best locations on the scalp to place their optodes so as to sample a given cortical region-of-interest (ROI). This process is difficult, time-consuming, and highly subjective. Here, we propose a tool, Array Designer, that is able to automatically design optimized fNIRS arrays given a user-defined ROI and certain features of the available fNIRS device. Critically, the Array Designer methodology is generalizable and will be applicable to almost any subject population or fNIRS device. We describe and validate the algorithmic methodology that underpins Array Designer by running multiple simulations of array design problems in a realistic anatomical model. We believe that Array Designer has the potential to end the need for manual array design, and in doing so save researchers time, improve fNIRS data quality, and promote standardization across the field

Use of functional neuroimaging and optogenetics to explore deep brain stimulation targets for the treatment of Parkinson's disease and epilepsy

Deep brain stimulation (DBS) is a neurosurgical therapy for Parkinson’s disease and epilepsy. In DBS, an electrode is stereotactically implanted in a specific region of the brain and electrical pulses are delivered using a subcutaneous pacemaker-like stimulator. DBS-therapy has proven to effectively suppress tremor or seizures in pharmaco-resistant Parkinson’s disease and epilepsy patients respectively. It is most commonly applied in the subthalamic nucleus for Parkinson’s disease, or in the anterior thalamic nucleus for epilepsy. Despite the rapidly growing use of DBS at these classic brain structures, there are still non-responders to the treatment. This creates a need to explore other brain structures as potential DBS-targets. However, research in patients is restricted mainly because of ethical reasons. Therefore, in order to search for potential new DBS targets, animal research is indispensable. Previous animal studies of DBS-relevant circuitry largely relied on electrophysiological recordings at predefined brain areas with assumed relevance to DBS therapy. Due to their inherent regional biases, such experimental techniques prevent the identification of less recognized brain structures that might be suitable DBS targets. Therefore, functional neuroimaging techniques, such as functional Magnetic Resonance Imaging and Positron Emission Tomography, were used in this thesis because they allow to visualize and to analyze the whole brain during DBS. Additionally, optogenetics, a new technique that uses light instead of electricity, was employed to manipulate brain cells with unprecedented selectivity

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Bayesian inversion in biomedical imaging

Biomedizinische Bildgebung ist zu einer Schlüsseltechnik geworden, Struktur oder Funktion lebender Organismen nicht-invasiv zu untersuchen. Relevante Informationen aus den gemessenen Daten zu rekonstruieren erfordert neben mathematischer Modellierung und numerischer Simulation das verlässliche Lösen schlecht gestellter inverser Probleme. Um dies zu erreichen müssen zusätzliche a-priori Informationen über die zu rekonstruierende Größe formuliert und in die algorithmischen Lösungsverfahren einbezogen werden. Bayesianische Invertierung ist eine spezielle mathematische Methodik dies zu tun. Die vorliegende Arbeit entwickelt eine aktuelle Übersicht Bayesianischer Invertierung und demonstriert die vorgestellten Konzepte und Algorithmen in verschiedenen numerischen Studien, darunter anspruchsvolle Anwendungen aus der biomedizinischen Bildgebung mit experimentellen Daten. Ein Schwerpunkt liegt dabei auf der Verwendung von Dünnbesetztheit/Sparsity als a-priori Information.Biomedical imaging techniques became a key technology to assess the structure or function of living organisms in a non-invasive way. Besides innovations in the instrumentation, the development of new and improved methods for processing and analysis of the measured data has become a vital field of research. Building on traditional signal processing, this area nowadays also comprises mathematical modeling, numerical simulation and inverse problems. The latter describes the reconstruction of quantities of interest from measured data and a given generative model. Unfortunately, most inverse problems are ill-posed, which means that a robust and reliable reconstruction is not possible unless additional a-priori information on the quantity of interest is incorporated into the solution method. Bayesian inversion is a mathematical methodology to formulate and employ a-priori information in computational schemes to solve the inverse problem. This thesis develops a recent overview on Bayesian inversion and exemplifies the presented concepts and algorithms in various numerical studies including challenging biomedical imaging applications with experimental data. A particular focus is on using sparsity as a-priori information within the Bayesian framework. <br