Development of a Unique Whole-Brain Model for Upper Extremity Neuroprosthetic Control

Neuroprostheses are at the forefront of upper extremity function restoration. However, contemporary controllers of these neuroprostheses do not adequately address the natural brain strategies related to planning, execution and mediation of upper extremity movements. These lead to restrictions in providing complete and lasting restoration of function. This dissertation develops a novel whole-brain model of neuronal activation with the goal of providing a robust platform for an improved upper extremity neuroprosthetic controller. Experiments (N=36 total) used goal-oriented upper extremity movements with real-world objects in an MRI scanner while measuring brain activation during functional magnetic resonance imaging (fMRI). The resulting data was used to understand neuromotor strategies using brain anatomical and temporal activation patterns. The study\u27s fMRI paradigm is unique and the use of goal-oriented movements and real-world objects are crucial to providing accurate information about motor task strategy and cortical representation of reaching and grasping. Results are used to develop a novel whole-brain model using a machine learning algorithm. When tested on human subject data, it was determined that the model was able to accurately distinguish functional motor tasks with no prior knowledge. The proof of concept model created in this work should lead to improved prostheses for the treatment of chronic upper extremity physical dysfunction

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

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

Frontal Functional Network Disruption Associated with Amyotrophic Lateral Sclerosis: An fNIRS-Based Minimum Spanning Tree Analysis

Recent evidence increasingly associates network disruption in brain organization with multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), a rare terminal disease. However, the comparability of brain network characteristics across different studies remains a challenge for conventional graph theoretical methods. One suggested method to address this issue is minimum spanning tree (MST) analysis, which provides a less biased comparison. Here, we assessed the novel application of MST network analysis to hemodynamic responses recorded by functional near-infrared spectroscopy (fNIRS) neuroimaging modality, during an activity-based paradigm to investigate hypothetical disruptions in frontal functional brain network topology as a marker of the executive dysfunction, one of the most prevalent cognitive deficit reported across ALS studies. We analyzed data recorded from nine participants with ALS and ten age-matched healthy controls by first estimating functional connectivity, using phase-locking value (PLV) analysis, and then constructing the corresponding individual and group MSTs. Our results showed significant between-group differences in several MST topological properties, including leaf fraction, maximum degree, diameter, eccentricity, and degree divergence. We further observed a global shift toward more centralized frontal network organizations in the ALS group, interpreted as a more random or dysregulated network in this cohort. Moreover, the similarity analysis demonstrated marginally significantly increased overlap in the individual MSTs from the control group, implying a reference network with lower topological variation in the healthy cohort. Our nodal analysis characterized the main local hubs in healthy controls as distributed more evenly over the frontal cortex, with slightly higher occurrence in the left prefrontal cortex (PFC), while in the ALS group, the most frequent hubs were asymmetrical, observed primarily in the right prefrontal cortex. Furthermore, it was demonstrated that the global PLV (gPLV) synchronization metric is associated with disease progression, and a few topological properties, including leaf fraction and tree hierarchy, are linked to disease duration. These results suggest that dysregulation, centralization, and asymmetry of the hemodynamic-based frontal functional network during activity are potential neuro-topological markers of ALS pathogenesis. Our findings can possibly support new bedside assessments of the functional status of ALS’ brain network and could hypothetically extend to applications in other neurodegenerative diseases

Development of Deep Learning Methods for Magnetic Resonance Phase Imaging of Neurological Disease

Magnetic resonance imaging (MRI) is a high-resolution, non-invasive medical imaging modality that is widely used in human brain. In recent years, susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) have been proposed to utilize MR phase signal to generate contrast from tissue magnetic susceptibility and even quantify the property. On the other hand, deep learning, especially deep convolutional neural networks (DCNNs), have achieved state-of-the-art performances in numerous computer vision tasks and gained significant attention in the field of medical imaging in the recent years. This dissertation combined the idea of deep learning with the two MR phase imaging methods. To combined deep learning with SWI, we designed and trained a 3D deep residual network that can distinguish false positive detected candidates from cerebral microbleeds (CMBs) and built an automatic CMB detection pipeline with high performance. We further confirmed the generalizability of this deep learning-based pipeline using multiple dataset with different scan parameters and pathologies and provided lessons for application and generalization of generic deep learning based medical imaging methods.To combine deep learning with QSM, we developed a 3D U-Net based network that learns to perform dipole inversion from gold standard QSM acquired from data with multiple orientation. The model was further improved with adversarial training strategy and achieved significantly lower reconstruction error than traditional QSM algorithms. In addition, we also performed various background removal and dipole inversion algorithms on both brain tumor patients and healthy volunteers to study and compare their performances. The results could provide guidance on future application of QSM in different scenarios

A subject-specific neurofeedback approach for cognitive enhancement

La técnica de neurofeedback (NF) permite el aprendizaje de la auto-regulación de la actividad cerebral, por la cual los usuarios pueden aprender a modificar (hasta un cierto grado) patrones de la actividad cerebral. Un punto clave del NF en la práctica es la técnica de registro de la actividad cerebral, donde el electroencefalograma (EEG) es la más usada debido a que es no invasiva, portátil y tiene una buena resolución temporal. La investigación en neurociencia ha reportado en repetidas ocasiones la relación de determinadas funciones cognitivas y desórdenes psiquiátricos con las oscilaciones del EEG. Por lo tanto, no es sorprendente que la regulación de estas oscilaciones conlleve efectos en comportamiento. Por ejemplo, muchos estudios han aplicado esta técnica de NF para aumentar funciones cognitivas como memoria de trabajo, atención y habilidades visuoespaciales, usualmente evaluadas en usuarios sanos. Además se ha aplicado NF para el tratamiento de desórdenes psiquiátricos tales como el trastorno por déficit de atención con hiperactividad (TDAH), depresión, epilepsia y tinitus, entre otros. El EEG es una señal no estacionaria que presenta una variabilidad inherente entre usuarios en los correlatos de procesos mentales. Sin embargo, la gran mayoría de técnicas de NF desarrolladas hasta el momento son genéricas en el sentido de que no se adaptan a los patrones individuales de EEG de cada usuario. En este sentido ha habido una tendencia en los últimos años hacia el uso de técnicas de NF específicas por medio de la adaptación de algunos métodos involucrados en el procedimiento. Esta tesis aborda el diseño de una técnica de NF dentro de un framework unificado, y muestra su viabilidad por medio de la implementación de tres estudios de NF consistentes en el incremento de la actividad en alpha superior para mejora cognitiva, evaluada en usuarios sanos, pacientes con depresión mayor y niños con TDAH. Una disciplina que viene a la mente cuando pensamos en cómo individualizar la técnica de NF son las interfaces cerebro-computador (BCIs). Las BCIs es una tecnología reciente cuyo objetivo es abrir un canal de comunicación entre un humano y un dispositivo usando únicamente la actividad cerebral para mejor la calidad de vida de personas con graves deficiencias motoras. Un punto clave de las BCIs es que los métodos de procesamiento de la señal se adaptan a cada usuario y momento de utilización de la tecnología. Esto se logra usualmente por medio de una fase de calibración ejecutada antes de la operación en línea. En esta fase de calibración los usuarios realizan tareas mentales que permiten medir algunos correlatos de EEG que son usados para calcular filtros individualizados, por ejemplo en términos de filtrado de artefactos y detección de tareas mentales. Después estos filtros se aplican en línea al EEG para decodificar las tareas mentales, accionando en consecuencia el dispositivo. Tomando el framework de BCI como referencia, proponemos los siguientes métodos individualizados: (1) un método de filtrado de artefactos en tiempo real (usando separación ciega de fuentes) para eliminar los artefactos de los patrones cerebrales de interés; (2) un método novedoso para la individualización de los patrones cerebrales de acuerdo a la combinación de registros de EEG en dos condiciones (estado de reposo y tarea activa); (3) un método para calcular el nivel de trabajo de los patrones cerebrales (baseline) por sujeto y sesión; y (4) una variedad de métodos y métricas para evaluar los efectos del NF en los patrones cerebrales (post-análisis). Para evaluar la viabilidad y validez experimental de esta técnica de NF, llevamos a cabo una implementación de un protocolo de NF basado en el incremento de la actividad en alpha superior para mejora cognitiva, evaluada en tres estudios distintos de NF involucrando usuarios sanos, pacientes con depresión y niños con TDAH. Estos estudios investigaron si individuos sanos, con depresión y ADHD son capaces de incrementar la potencia en alpha superior por medio de NF y, si es así, si estos efectos están relacionados con efectos en comportamiento (rendimiento cognitivo o escalas clínicas). 1. El primer estudio investigó los efectos de una única sesión de NF en usuarios sanos (N = 19) en un diseño experimental con falso feedback. Este estudio mostró un incremento en la potencia en alpha superior en la tarea activa (inmediatamente después del NF) así como un incremento en potencia en alpha superior durante la tarea de rotación mental (intervalo preestímulo), únicamente para el grupo experimental. Ambos grupos mejoraron en rendimiento cognitivo, con una mejora superior para el grupo experimental. Sin embargo una única sesión parece insuficiente para producir diferencias significativas entre grupos. 2. El segundo estudio investigó los efectos de ocho sesiones de NF en pacientes con depresión (N = 60) en un estudio controlado. Este estudio mostró un incremento en la potencia en alpha superior en la tarea activa (pre-post estudio) para el grupo experimental. Estos efectos no estuvieron restringidos espacialmente o espectralmente al parámetro de NF. Se encontró un incremento de actividad a nivel de las fuentes cerebrales en alpha para el grupo experimental, localizado en el giro cingulado anterior (sgACC, BA 25). El grupo experimental mostró un incremento en rendimiento así como un incremento en velocidad de procesamiento medido por un test de memoria de trabajo después del NF, sugiriendo por tanto que los síntomas cognitivos de pacientes con depresión pueden aliviarse por medio de este procedimiento. 3. El tercer estudio investigó los efectos de 18 sesiones de NF en niños con TDAH (N = 20) en un estudio preliminar no controlado. Este estudio mostró un incremento en la potencia (relativa y absoluta) en alpha superior en la tarea activa (pre-post estudio). Mientras que los cambios pre-post estudio estuvieron restringidos mayormente a la banda alpha superior, los efectos dentro de la sesión mostraron un decremento en potencia absoluta en las bajas frecuencias (se debe notar que los niños con TDAH usualmente tienen un exceso de actividad en bajas frecuencias). Los padres indicaron una mejora clínica en los niños con respecto a inatención e hiperactividad/impulsividad, y los tests neuropsicológicos mostraron una mejora en memoria de trabajo. En resumen, estos resultados muestran que la técnica de NF se adaptó a la gran variabilidad de los patrones cerebrales entre sujetos y sesiones. Además, estas tres poblaciones fueron capaces de auto-regular los patrones cerebrales con una consecuente mejora tanto en rendimiento cognitivo y como en escalas clínicas (en el caso de niños con TDAH). Aunque la principal contribución de esta tesis está en los métodos y en la implementación de una técnica individualizada de NF, los estudios de NF aquí presentados son novedosos en sí mismos y los resultados que se extraen de ellos constituyen una contribución añadida de esta tesis.Neurofeedback (NF) promotes the learning of the self-regulation of brain activity, where subjects can learn to shape (to a certain degree) some patterns of brain activity. A key practical point of NF is the recording technique of brain activity, where the electroencephalogram (EEG) is the most widely used one as it is non-invasive, portable and presents a good temporal resolution. Neuroscience research has repeatedly reported the relation of cognitive functions and some psychiatric disorders to EEG oscillations. Thus, it is not surprising that the regulation of EEG oscillations yields behavioral effects. For instance, a large body of research has applied NF for the enhancement of cognitive functions such as working memory, attention and visuospatial abilities, usually applied to healthy subjects. NF has been also applied for the treatment of psychiatric disorders such as attention-deficit/hyperactive disorder (ADHD), depression, epilepsy and tinnitus, among others. EEG is a non-stationary signal that presents an inherent variability among subjects in the EEG correlates of brain processes. However, the large majority of NF procedures developed to date are subject-generic in the sense that they are not adapted to the individual EEG patterns of each subject. In this direction, there has been a trend in recent years towards the use of subject-specific NF procedures by adapting some methods involved in that procedure. This thesis addresses the design of a subject-specific NF approach in a unified framework, and shows its feasibility by implementing three different NF studies of upper alpha up-regulation for cognitive enhancement in healthy subjects, patients with major depressive disorder and children diagnosed with ADHD. One discipline that comes to mind when thinking about how to individualize EEG-based NF procedures is the brain-computer interfaces (BCIs). BCIs is a recent technology whose objective is to open a communication channel between a human and a device using only brain activity to improve the quality of life of people with severe motor disability. A key point of BCIs is that the signal processing methods are adapted for each subject and time of use of the technology. This is commonly achieved by a calibration phase before the online operation. In this calibration phase, the subjects perform metal tasks that allow to measure some EEG correlates that are used to compute subject-specific filters, for example in terms of filtering the EEG artifacts and detecting the mental tasks. These filters are then applied during the online operation phase to the ongoing EEG to decode the mental tasks, actuating the devices accordingly. Taking the BCI framework as a reference, we propose the following individualized methods: (1) a real-time artifact filtering method (using blind source separation) to remove the artifacts from the brain patterns of interest; (2) a novel method for the individualization of the brain patterns according to the combination of EEG recordings in two conditions (resting state and task-related activity); (3) a method for computing the baseline working level of the brain patterns per subject and session; and (4) a variety of methods and metrics to assess the effects of NF on the brain patterns (post-analysis). In order to demonstrate the feasibility and experimental validity of this subject-specific NF approach, we carried out an implementation of a NF protocol of upper alpha up-regulation for cognitive enhancement, evaluated in three different NF studies involving healthy subjects, depressed patients and ADHD children. These studies investigated whether healthy, depressed and ADHD individuals could learn to increase the individual upper alpha power by means of NF, and whether these effects were related to behavioral effects on either cognition or clinical outcome. 1. The first study investigated the effects of a single NF session on healthy participants (N = 19) following a sham-controlled experimental design. This study showed increased upper alpha power in task-related activity (immediately after training), as well as increased pre-stimulus upper alpha power during the execution of a mental rotation task, apparen only for the experimental group. Both groups improved cognitive performance, with a more prominent improvement for the experimental group. However a single session seems to be insufficient to yield significant differences between groups. 2. The second study investigated the effects of eight NF sessions on depressed patients (N = 60) in a controlled study. This study showed increased upper alpha power in task-related activity (pre-post study) for the experimental group, not spatially or spectrally restricted to the trained parameter. A current density increase appeared at brain source level in alpha for the experimental group, localized in the subgenual anterior cingulate cortex (sgACC, BA 25). The experimental group showed increased performance as well as improved processing speed in a working memory test after the training, thus suggesting that the cognitive symptoms of depressed patients could be alleviated by this typeof procedure. 3. The third study investigated the effects of 18 NF sessions on ADHD children (N = 20) in a preliminary uncontrolled study. This study showed increased relative and absolute upper alpha power in task-related activity (pre-post study). While the pre-post study effects were mainly restricted to upper alpha, within-session analysis showed an absolute power decrease in slow-frequency oscillations (note that ADHD children commonly show an excess of slow-frequency activity). Parents rated a clinical improvement in children regarding inattention and hyperactivity/impulsivity, and neurophysiological tests showed an improvement in working memory. In summary, these results show that the NF technique was able to accommodate the large variability of the brain patterns among subjects and over sessions. In addition, these three populations were able to self-regulate the targeted brain patterns with a consequent improvement in cognitive performance and clinical outcome (in the case of ADHD children). Although the main contribution of the thesis is on the methods and on the implementation of the subject-specific NF procedure, the NF studies herein presented are novel and the results extracted from them constitute an added contribution of this thesis

Motor learning induced neuroplasticity in minimally invasive surgery

Technical skills in surgery have become more complex and challenging to acquire since the introduction of technological aids, particularly in the arena of Minimally Invasive Surgery. Additional challenges posed by reforms to surgical careers and increased public scrutiny, have propelled identification of methods to assess and acquire MIS technical skills. Although validated objective assessments have been developed to assess motor skills requisite for MIS, they poorly understand the development of expertise. Motor skills learning, is indirectly observable, an internal process leading to relative permanent changes in the central nervous system. Advances in functional neuroimaging permit direct interrogation of evolving patterns of brain function associated with motor learning due to the property of neuroplasticity and has been used on surgeons to identify the neural correlates for technical skills acquisition and the impact of new technology. However significant gaps exist in understanding neuroplasticity underlying learning complex bimanual MIS skills. In this thesis the available evidence on applying functional neuroimaging towards assessment and enhancing operative performance in the field of surgery has been synthesized. The purpose of this thesis was to evaluate frontal lobe neuroplasticity associated with learning a complex bimanual MIS skill using functional near-infrared spectroscopy an indirect neuroimaging technique. Laparoscopic suturing and knot-tying a technically challenging bimanual skill is selected to demonstrate learning related reorganisation of cortical behaviour within the frontal lobe by shifts in activation from the prefrontal cortex (PFC) subserving attention to primary and secondary motor centres (premotor cortex, supplementary motor area and primary motor cortex) in which motor sequences are encoded and executed. In the cross-sectional study, participants of varying expertise demonstrate frontal lobe neuroplasticity commensurate with motor learning. The longitudinal study involves tracking evolution in cortical behaviour of novices in response to receipt of eight hours distributed training over a fortnight. Despite novices achieving expert like performance and stabilisation on the technical task, this study demonstrates that novices displayed persistent PFC activity. This study establishes for complex bimanual tasks, that improvements in technical performance do not accompany a reduced reliance in attention to support performance. Finally, least-squares support vector machine is used to classify expertise based on frontal lobe functional connectivity. Findings of this thesis demonstrate the value of interrogating cortical behaviour towards assessing MIS skills development and credentialing.Open Acces

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline

Neurotechnology for Brain Repair:Imaging, Enhancing and Restoring Human Motor Function

Neurotechnology is the application of scientific knowledge to the practical purpose of understanding, interacting and/or repairing the brain or, in a broader sense, the nervous system. The development of novel approaches to decode functional information from the brain, to enhance specific properties of neural tissue and to restore motor output in real end-users is a fundamental challenge to translate these novel solutions into clinical practice. In this Thesis, I introduce i) a novel imaging method to characterize movement-related electroencephalographic (EEG) potentials; ii) a brain stimulation strategy to improve brain-computer interface (BCI) control; iii) and a therapy for motor recovery involving a neuroprosthesis. Overall, results show i) that stable EEG topographies present a subject-independent organization that can be used to robustly decode actual or attempted movements in sub-acute stroke patients and healthy controls, with minimal a-priori information; ii) that transcranial direct-current stimulation (tDCS) enhances the modulability of sensorimotor rhythms used for brain-computer interaction in chronic Spinal Cord Injured (SCI) individuals and healthy controls; iii) that neuromuscular electrical stimulation (NMES) controlled via closed-loop neural activity induces significantly stronger upper limb functional recovery in chronic stroke patients than sham NMES therapy, and that these changes are clinically relevant. These results have or might have important implications in i) disease diagnostics and monitoring through EEG; ii) assistive technology and reduction of permanent disability following SCI; iii) rehabilitation and recovery of upper limb function following a stroke, also after several years of complete paralysis. Briefly, this Thesis provides the conceptual framework, scientific rationale, technical details and clinical evidence supporting translational Neurotechnology that improves, optimizes and disrupts current medical practice in monitoring, substituting and recovering lost upper limb function