Combined brain language connectivity and intraoperative neurophysiologic techniques in awake craniotomy for eloquent-area brain tumor resection

Speech processing can be disturbed by primary brain tumors (PBT). Improvement of presurgical planning techniques decrease neurological morbidity associated to tumor resection during awake craniotomy. The aims of this work were: 1. To perform Diffusion Kurtosis Imaging based tractography (DKI-tract) in the detection of brain tracts involved in language; 2. To investigate which factors contribute to functional magnetic resonance imaging (fMRI) maps in predicting eloquent language regional reorganization; 3. To determine the technical aspects of accelerometric (ACC) recording of speech during surgery. DKI-tracts were streamlined using a 1.5T magnetic resonance scanner. Number of tracts and fiber pathways were compared between DKI and standard Diffusion Tensor Imaging (DTI) in healthy subjects (HS) and PBT patients. fMRI data were acquired using task-specific and resting-state paradigms during language and motor tasks. After testing intraoperative fMRI’s influence on direct cortical stimulation (DCS) number of stimuli, graph-theory measures were extracted and analyzed. Regarding speech recording, ACC signals were recorded after evaluating neck positions and filter bandwidths. To test this method, language disturbances were recorded in patients with dysphonia and after applying DCS in the inferior frontal gyrus. In contrast, HS reaction time was recorded during speech execution. DKI-tract showed increased number of arcuate fascicle tracts in PBT patients. Lower spurious tracts were identified with DKI-tract. Intraoperative fMRI and DCS showed similar stimuli in comparison with DCS alone. Increased local centrality accompanied language ipsilateral and contralateral reorganization. ACC recordings showed minor artifact contamination when placed at the suprasternal notch using a 20-200 Hz filter bandwidth. Patients with dysphonia showed decreased amplitude and frequency in comparison with HS. ACC detected an additional 11% disturbances after DCS, and a shortening of latency within the presence of a loud stimuli during speech execution. This work improved current knowledge on presurgical planning techniques based on brain structural and functional neuroimaging connectivity, and speech recordingA função linguística do ser humano pode ser afetada pela presença de tumores cerebrais (TC) A melhoria de técnicas de planeamento pré-cirurgico diminui a morbilidade neurológica iatrogénica associada ao seu tratamento cirúrgico. O objetivo deste trabalho é: 1. Testar a fiabilidade da tractografia estimada por difusor de kurtose (tract-DKI), dos feixes cerebrais envolvidos na linguagem 2. Identificar os fatores que contribuem para o mapeamento linguagem por ressonância magnética funcional (RMf) na predição da neuroplasticidade. 3. Identificar aspetos técnicos do registo da linguagem por accelerometria (ACC). A DKI-tract foi estimada após realização de RM cerebral com 1.5T. O número e percurso das fibras foi avaliado. A RMf foi adquirida durante realização de tarefas linguísticas, motoras, e em repouso. Foi testada influência dos mapas de ativação calculados por RMf, no número de estímulos realizados durante a estimulação direta cortical (EDC) intraoperatória. Medidas de conectividade foram extraídas de regiões cerebrais. A posição e filtragem de sinal ACC foram estudadas após vocalização de palavras. O sinal ACC obtido em voluntários foi comparado com doentes disfónicos, após estimulação do giro inferior frontal, e após a adição de um estímulo sonoro perturbador durante vocalização. A tract-DKI estimou um elevado número de fascículos do feixe arcuato com menos falsos negativos. Os mapas linguísticos de RMf intraoperatória, não influenciou a EDC. Medidas de centralidade aumentaram após neuroplasticidade ipsilateral e contralateral. A posição supraesternal e a filtragem de sinal ACC entre 20-200Hz demonstrou menor ruido de contaminação. Este método identificou diminuição de frequência e amplitude em doentes com disfonia, 11% de erros linguísticos adicionais após estimulação e diminuição do tempo de latência quando presente o sinal sonoro perturbador. Este trabalho promoveu a utilização de novas técnicas no planeamento pré-cirúrgico do doente com tumor cerebral e alterações da linguagem através do estudo de conectividade estrutural, funcional e registo da linguagem

Automatic signal and image-based assessments of spinal cord injury and treatments.

Spinal cord injury (SCI) is one of the most common sources of motor disabilities in humans that often deeply impact the quality of life in individuals with severe and chronic SCI. In this dissertation, we have developed advanced engineering tools to address three distinct problems that researchers, clinicians and patients are facing in SCI research. Particularly, we have proposed a fully automated stochastic framework to quantify the effects of SCI on muscle size and adipose tissue distribution in skeletal muscles by volumetric segmentation of 3-D MRI scans in individuals with chronic SCI as well as non-disabled individuals. We also developed a novel framework for robust and automatic activation detection, feature extraction and visualization of the spinal cord epidural stimulation (scES) effects across a high number of scES parameters to build individualized-maps of muscle recruitment patterns of scES. Finally, in the last part of this dissertation, we introduced an EMG time-frequency analysis framework that implements EMG spectral analysis and machine learning tools to characterize EMG patterns resulting in independent or assisted standing enabled by scES, and identify the stimulation parameters that promote muscle activation patterns more effective for standing. The neurotechnological advancements proposed in this dissertation have greatly benefited SCI research by accelerating the efforts to quantify the effects of SCI on muscle size and functionality, expanding the knowledge regarding the neurophysiological mechanisms involved in re-enabling motor function with epidural stimulation and the selection of stimulation parameters and helping the patients with complete paralysis to achieve faster motor recovery

Statistical and Graph-Based Signal Processing: Fundamental Results and Application to Cardiac Electrophysiology

The goal of cardiac electrophysiology is to obtain information about the mechanism, function, and performance of the electrical activities of the heart, the identification of deviation from normal pattern and the design of treatments. Offering a better insight into cardiac arrhythmias comprehension and management, signal processing can help the physician to enhance the treatment strategies, in particular in case of atrial fibrillation (AF), a very common atrial arrhythmia which is associated to significant morbidities, such as increased risk of mortality, heart failure, and thromboembolic events. Catheter ablation of AF is a therapeutic technique which uses radiofrequency energy to destroy atrial tissue involved in the arrhythmia sustenance, typically aiming at the electrical disconnection of the of the pulmonary veins triggers. However, recurrence rate is still very high, showing that the very complex and heterogeneous nature of AF still represents a challenging problem. Leveraging the tools of non-stationary and statistical signal processing, the first part of our work has a twofold focus: firstly, we compare the performance of two different ablation technologies, based on contact force sensing or remote magnetic controlled, using signal-based criteria as surrogates for lesion assessment. Furthermore, we investigate the role of ablation parameters in lesion formation using the late-gadolinium enhanced magnetic resonance imaging. Secondly, we hypothesized that in human atria the frequency content of the bipolar signal is directly related to the local conduction velocity (CV), a key parameter characterizing the substrate abnormality and influencing atrial arrhythmias. Comparing the degree of spectral compression among signals recorded at different points of the endocardial surface in response to decreasing pacing rate, our experimental data demonstrate a significant correlation between CV and the corresponding spectral centroids. However, complex spatio-temporal propagation pattern characterizing AF spurred the need for new signals acquisition and processing methods. Multi-electrode catheters allow whole-chamber panoramic mapping of electrical activity but produce an amount of data which need to be preprocessed and analyzed to provide clinically relevant support to the physician. Graph signal processing has shown its potential on a variety of applications involving high-dimensional data on irregular domains and complex network. Nevertheless, though state-of-the-art graph-based methods have been successful for many tasks, so far they predominantly ignore the time-dimension of data. To address this shortcoming, in the second part of this dissertation, we put forth a Time-Vertex Signal Processing Framework, as a particular case of the multi-dimensional graph signal processing. Linking together the time-domain signal processing techniques with the tools of GSP, the Time-Vertex Signal Processing facilitates the analysis of graph structured data which also evolve in time. We motivate our framework leveraging the notion of partial differential equations on graphs. We introduce joint operators, such as time-vertex localization and we present a novel approach to significantly improve the accuracy of fast joint filtering. We also illustrate how to build time-vertex dictionaries, providing conditions for efficient invertibility and examples of constructions. The experimental results on a variety of datasets suggest that the proposed tools can bring significant benefits in various signal processing and learning tasks involving time-series on graphs. We close the gap between the two parts illustrating the application of graph and time-vertex signal processing to the challenging case of multi-channels intracardiac signals

Development of ultrasound to measure deformation of functional spinal units in cervical spine

Neck pain is a pervasive problem in the general population, especially in those working in vibrating environments, e.g. military troops and truck drivers. Previous studies showed neck pain was strongly associated with the degeneration of intervertebral disc, which is commonly caused by repetitive loading in the work place. Currently, there is no existing method to measure the in-vivo displacement and loading condition of cervical spine on the site. Therefore, there is little knowledge about the alternation of cervical spine functionality and biomechanics in dynamic environments. In this thesis, a portable ultrasound system was explored as a tool to measure the vertebral motion and functional spinal unit deformation. It is hypothesized that the time sequences of ultrasound imaging signals can be used to characterize the deformation of cervical spine functional spinal units in response to applied displacements and loading. Specifically, a multi-frame tracking algorithm is developed to measure the dynamic movement of vertebrae, which is validated in ex-vivo models. The planar kinematics of the functional spinal units is derived from a dual ultrasound system, which applies two ultrasound systems to image C-spine anteriorly and posteriorly. The kinematics is reconstructed from the results of the multi-frame movement tracking algorithm and a method to co-register ultrasound vertebrae images to MRI scan. Using the dual ultrasound, it is shown that the dynamic deformation of functional spinal unit is affected by the biomechanics properties of intervertebral disc ex-vivo and different applied loading in activities in-vivo. It is concluded that ultrasound is capable of measuring functional spinal units motion, which allows rapid in-vivo evaluation of C-spine in dynamic environments where X-Ray, CT or MRI cannot be used.2020-02-20T00:00:00

Water and Brain Function: Effects of Hydration Status on Neurostimulation and Neurorecording

Introduction: TMS and EEG are used to study normal neurophysiology, diagnose, and treat clinical neuropsychiatric conditions, but can produce variable results or fail. Both techniques depend on electrical volume conduction, and thus brain volumes. Hydration status can affect brain volumes and functions (including cognition), but effects on these techniques are unknown. We aimed to characterize the effects of hydration on TMS, EEG, and cognitive tasks. Methods: EEG and EMG were recorded during single-pulse TMS, paired-pulse TMS, and cognitive tasks from 32 human participants on dehydrated (12-hour fast/thirst) and rehydrated (1 Liter oral water ingestion in 1 hour) testing days. Hydration status was confirmed with urinalysis. MEP, ERP, and network analyses were performed to examine responses at the muscle, brain, and higher-order functioning. Results: Rehydration decreased motor threshold (increased excitability) and shifted the motor hotspot. Significant effects on TMS measures occurred despite being re-localized and re-dosed to these new parameters. Rehydration increased SICF of the MEP, magnitudes of specific TEP peaks in inhibitory protocols, specific ERP peak magnitudes and reaction time during the cognitive task. Rehydration amplified nodal inhibition around the stimulation site in inhibitory paired-pulse networks and strengthened nodes outside the stimulation site in excitatory and CSP networks. Cognitive performance was not improved by rehydration, although similar performance was achieved with generally weaker network activity. Discussion: Results highlight differences between mild dehydration and rehydration. The rehydrated brain was easier to stimulate with TMS and produced larger responses to external and internal stimuli. This is explainable by the known physiology of body water dynamics, which encompass macroscopic and microscopic volume changes. Rehydration can shift 3D cortical positioning, decrease scalp cortex distance (bringing cortex closer to stimulator/recording electrodes), and cause astrocyte swelling-induced glutamate release. Conclusions: Previously unaccounted variables like osmolarity, astrocyte and brain volumes likely affect neurostimulation/neurorecording. Controlling for and carefully manipulating hydration may reduce variability and improve therapeutic outcomes of neurostimulation. Dehydration is common and produces less excitable circuits. Rehydration should offer a mechanism to macroscopically bring target cortical areas closer to an externally applied neurostimulation device to recruit greater volumes of tissue and microscopically favor excitability in the stimulated circuits

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

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