Within-Subject Joint Independent Component Analysis of Simultaneous fMRI/ERP in an Auditory Oddball Paradigm

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations of applying joint independent component analysis (jICA) within-subjects, for ERP and fMRI data collected simultaneously in a parametric auditory frequency oddball paradigm. In a group of 20 subjects, an increase in ERP peak amplitude ranging 1–8 μV in the time window of the P300 (350–700 ms), and a correlated increase in fMRI signal in a network of regions including the right superior temporal and supramarginal gyri, was observed with the increase in deviant frequency difference. JICA of the same ERP and fMRI group data revealed activity in a similar network, albeit with stronger amplitude and larger extent. In addition, activity in the left pre- and post-central gyri, likely associated with right hand somato-motor response, was observed only with the jICA approach. Within-subject, the jICA approach revealed significantly stronger and more extensive activity in the brain regions associated with the auditory P300 than the P300 linear regression analysis. The results suggest that with the incorporation of spatial and temporal information from both imaging modalities, jICA may be a more sensitive method for extracting common sources of activity between ERP and fMRI

Joint EEG-fMRI signal model for EEG separation and localization

Electroencephalography (EEG) offers a rich representation of human brain activity in the time domain. EEG would in many circumstances be the preferred technique for analysing brain activity, as it is less expensive and more practical to use than other modalities such as functional Magnetic Resonance Imaging (fMRI), notably due to its size. However, its spatial resolution is limited, which hampers its ability to characterise activity across spatially distributed brain networks. In comparison, functional Magnetic Resonance Imaging (fMRI) offers very good spatial resolution but the hemodynamic nature of the signal limits its temporal resolution to the order of seconds. A possible solution to this problem is to use both EEG and fMRI signals, but this approach would lead to the loss of convenience of EEG alone. Hence it is desirable to bring the advantages of an fMRI signal into EEG assessment of the brain’s state and responses without the necessity for the presence of fMRI equipment on site. In this work, a joint statistical model of fMRI/EEG signals is proposed and used for processing of EEG signals. The performance of a standard Blind Source Separation (BSS) method is compared with the new method, which uses the above joint EEG-fMRI model, which in turn shows improvement in the precision of both source separation and localisation

Understanding Actions of Others: The Electrodynamics of the Left and Right Hemispheres. A High-Density EEG Neuroimaging Study

Background: When we observe an individual performing a motor act (e.g. grasping a cup) we get two types of information on the basis of how the motor act is done and the context: what the agent is doing (i.e. grasping) and the intention underlying it (i.e. grasping for drinking). Here we examined the temporal dynamics of the brain activations that follow the observation of a motor act and underlie the observer’s capacity to understand what the agent is doing and why. Methodology/Principal Findings: Volunteers were presented with two-frame video-clips. The first frame (T0) showed an object with or without context; the second frame (T1) showed a hand interacting with the object. The volunteers were instructed to understand the intention of the observed actions while their brain activity was recorded with a high-density 128-channel EEG system. Visual event-related potentials (VEPs) were recorded time-locked with the frame showing the hand-object interaction (T1). The data were analyzed by using electrical neuroimaging, which combines a cluster analysis performed on the group-averaged VEPs with the localization of the cortical sources that give rise to different spatiotemporal states of the global electrical field. Electrical neuroimaging results revealed four major steps: 1) bilateral posterior cortical activations; 2) a strong activation of the left posterior temporal and inferior parietal cortices with almost a complete disappearance of activations in the right hemisphere; 3) a significant increase of the activations of the right temporo-parieta

Integration of EEG-FMRI in an Auditory Oddball Paradigm Using Joint Independent Component Analysis

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. The overall objective of this dissertation is to determine the sensitivity and limitations of joint independent component analysis (jICA) within-subject for integration of ERP and fMRI data collected simultaneously in a parametric auditory oddball paradigm. The main experimental finding in this work is that jICA revealed significantly stronger and more extensive activity in brain regions associated with the auditory P300 ERP than a P300 linear regression analysis, both at the group level and within-subject. The results suggest that, with the incorporation of spatial and temporal information from both imaging modalities, jICA is more sensitive to neural sources commonly observed with ERP and fMRI compared to a linear regression analysis. Furthermore, computational simulations suggest that jICA can extract linear and nonlinear relationships between ERP and fMRI signals, as well as uncoupled sources (i.e., sources with a signal in only one imaging modality). These features of jICA can be important for assessing disease states in which the relationship between the ERP and fMRI signals is unknown, as well as pathological conditions causing neurovascular uncoupling, such as stroke

Neural Basis of Action Observation and Understanding From First- and Third-Person Perspectives: An fMRI Study

Understanding the intentions of others while observing their actions is a fundamental aspect of social behavior. However, the differences in neural and functional mechanisms between observing actions from the first-person perspective (1PP) and third-person perspective (3PP) are poorly understood. The present study had two aims: (1) to delineate the neural basis of action observation and understanding from the 1PP and 3PP; and (2) to identify whether there are different activation patterns during action observation and understanding from 1PP and 3PP. We used a blocked functional magnetic resonance imaging (fMRI) experimental design. Twenty-six right-handed participants observed interactions between the right hand and a cup from 1PP and 3PP. The results indicated that both 1PP and 3PP were associated with similar patterns of activation in key areas of the mirror neuron system underlying action observation and understanding. Importantly, besides of the core network of mirror neuron system, we also found that parts of the basal ganglia and limbic system were involved in action observation in both the 1PP and 3PP tasks, including the putamen, insula and hippocampus, providing a more complete understanding of the neural basis for action observation and understanding. Moreover, compared with the 3PP, the 1PP task caused more extensive and stronger activation. In contrast, the opposite comparison revealed that no regions exhibited significantly more activation in the 3PP compared with the 1PP condition. The current results have important implications for understanding the role of the core network underlying the mirror neuron system, as well as parts of the basal ganglia and limbic system, during action observation and understanding

Loop radiofrequency coils for clinical magnetic resonance imaging at 7 TESLA

To date, the 7 T magnetic resonance imaging (MRI) scanner remains a pure research system and there is still a long way ahead till full clinical integration. Key challenges are the absence of a body transmit radiofrequency (RF) coil as well as of dedicated RF coils in general, short RF wavelengths of the excitation field in the order of the dimensions of a human body leading to signal inhomogeneities, and severe limitations with respect to the specific absorption rate. They all result in a strong need for RF engineering and sequence optimization to explore the potential of MRI at 7 T, and to pave the way for its future clinical application. In this thesis, high-resolution MRI with a rather small field-of-view (FOV) in the head and neck region (parotid gland/duct and carotid arteries), and of the musculoskeletal system as well as with a very large FOV in the abdomen (spine) were presented. Therefore, a variety of RF coils were used: from a commercially available single-loop coil to novel, specially developed phased array coils each consisting of eight loop elements. Methods to thoroughly characterize and test the developed RF coils were presented, including numerical simulations, bench and MRI measurements. Characterization with respect to performance for parallel acquisition techniques and an extensive compliance testing for patient safety were described in detail. All aspects of the engineering part, from design to optimization, and finally, to the in vivo application in volunteers and patients were covered. Since clinical applicability has always been the purpose, optimized imaging protocols along with a discussion on the clinical relevance was included in each study. The presented RF loop coils widely expand the options for clinical research at 7 T and advance the integration of this technology in a clinical setting

Towards Individualized Transcranial Electric Stimulation Therapy through Computer Simulation

Transkranielle Elektrostimulation (tES) beschreibt eine Gruppe von Hirnstimulationstechniken, die einen schwachen elektrischen Strom über zwei nicht-invasiv am Kopf angebrachten Elektroden applizieren. Handelt es sich dabei um einen Gleichstrom, spricht man von transkranieller Gleichstromstimulation, auch tDCS abgekürzt. Die allgemeine Zielstellung aller Hirnstimulationstechniken ist Hirnfunktion durch ein Verstärken oder Dämpfen von Hirnaktivität zu beeinflussen. Unter den Stimulationstechniken wird die transkranielle Gleichstromstimulation als ein adjuvantes Werkzeug zur Unterstützung der mikroskopischen Reorganisation des Gehirnes in Folge von Lernprozessen und besonders der Rehabilitationstherapie nach einem Schlaganfall untersucht. Aktuelle Herausforderungen dieser Forschung sind eine hohe Variabilität im erreichten Stimulationseffekt zwischen den Probanden sowie ein unvollständiges Verständnis des Zusammenspiels der der Stimulation zugrundeliegenden Mechanismen. Als Schlüsselkomponente für das Verständnis der Stimulationsmechanismen wird das zwischen den Elektroden im Kopf des Probanden aufgebaute elektrische Feld erachtet. Einem grundlegenden Konzept folgend wird angenommen, dass Hirnareale, die einer größeren elektrischen Feldstärke ausgesetzt sind, ebenso einen höheren Stimulationseffekt erfahren. Damit kommt der Positionierung der Elektroden eine entscheidende Rolle für die Stimulation zu. Allerdings verteilt sich das elektrische Feld wegen des heterogenen elektrischen Leitfähigkeitsprofil des menschlichen Kopfes nicht uniform im Gehirn der Probanden. Außerdem ist das Verteilungsmuster auf Grund anatomischer Unterschiede zwischen den Probanden verschieden. Die triviale Abschätzung der Ausbreitung des elektrischen Feldes anhand der bloßen Position der Stimulationselektroden ist daher nicht ausreichend genau für eine zielgerichtete Stimulation. Computerbasierte, biophysikalische Simulationen der transkraniellen Elektrostimulation ermöglichen die individuelle Approximation des Verteilungsmusters des elektrischen Feldes in Probanden basierend auf deren medizinischen Bildgebungsdaten. Sie werden daher zunehmend verwendet, um tDCS-Anwendungen zu planen und verifizieren, und stellen ein wesentliches Hilfswerkzeug auf dem Weg zu individualisierter Schlaganfall-Rehabilitationstherapie dar. Softwaresysteme, die den dahinterstehenden individualisierten Verarbeitungsprozess erleichtern und für ein breites Feld an Forschern zugänglich machen, wurden in den vergangenen Jahren für den Anwendungsfall in gesunden Erwachsenen entwickelt. Jedoch bleibt die Simulation von Patienten mit krankhaftem Hirngewebe und strukturzerstörenden Läsionen eine nicht-triviale Aufgabe. Daher befasst sich das hier vorgestellte Projekt mit dem Aufbau und der praktischen Anwendung eines Arbeitsablaufes zur Simulation transkranieller Elektrostimulation. Dabei stand die Anforderung im Vordergrund medizinische Bildgebungsdaten insbesondere neurologischer Patienten mit krankhaft verändertem Hirngewebe verarbeiten zu können. Der grundlegende Arbeitsablauf zur Simulation wurde zunächst für gesunde Erwachsene entworfen und validiert. Dies umfasste die Zusammenstellung medizinischer Bildverarbeitungsalgorithmen zu einer umfangreichen Verarbeitungskette, um elektrisch relevante Strukturen in den Magnetresonanztomographiebildern des Kopfes und des Oberkörpers der Probanden zu identifizieren und zu extrahieren. Die identifizierten Strukturen mussten in Computermodelle überführt werden und das zugrundeliegende, physikalische Problem der elektrischen Volumenleitung in biologischen Geweben mit Hilfe numerischer Simulation gelöst werden. Im Verlauf des normalen Alterns ist das Gehirn strukturellen Veränderungen unterworfen, unter denen ein Verlust des Hirnvolumens sowie die Ausbildung mikroskopischer Veränderungen seiner Nervenfaserstruktur die Bedeutendsten sind. In einem zweiten Schritt wurde der Arbeitsablauf daher erweitert, um diese Phänomene des normalen Alterns zu berücksichtigen. Die vordergründige Herausforderung in diesem Teilprojekt war die biophysikalische Modellierung der veränderten Hirnmikrostruktur, da die resultierenden Veränderungen im Leitfähigkeitsprofil des Gehirns bisher noch nicht in der Literatur quantifiziert wurden. Die Erweiterung des Simulationsablauf zeichnete sich vorrangig dadurch aus, dass mit unsicheren elektrischen Leitfähigkeitswerten gearbeitet werden konnte. Damit war es möglich den Einfluss der ungenau bestimmbaren elektrischen Leitfähigkeit der verschiedenen biologischen Strukturen des menschlichen Kopfes auf das elektrische Feld zu ermitteln. In einer Simulationsstudie, in der Bilddaten von 88 Probanden einflossen, wurde die Auswirkung der veränderten Hirnfaserstruktur auf das elektrische Feld dann systematisch untersucht. Es wurde festgestellt, dass sich diese Gewebsveränderungen hochgradig lokal und im Allgemeinen gering auswirken. Schließlich wurden in einem dritten Schritt Simulationen für Schlaganfallpatienten durchgeführt. Ihre großen, strukturzerstörenden Läsionen wurden dabei mit einem höheren Detailgrad als in bisherigen Arbeiten modelliert und physikalisch abermals mit unsicheren Leitfähigkeiten gearbeitet, was zu unsicheren elektrischen Feldabschätzungen führte. Es wurden individuell berechnete elektrische Felddaten mit der Hirnaktivierung von 18 Patienten in Verbindung gesetzt, unter Berücksichtigung der inhärenten Unsicherheit in der Bestimmung der elektrischen Felder. Das Ziel war zu ergründen, ob die Hirnstimulation einen positiven Einfluss auf die Hirnaktivität der Patienten im Kontext von Rehabilitationstherapie ausüben und so die Neuorganisierung des Gehirns nach einem Schlaganfall unterstützen kann. Während ein schwacher Zusammenhang hergestellt werden konnte, sind weitere Untersuchungen nötig, um diese Frage abschließend zu klären.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms BibliographyTranscranial electric current stimulation (tES) denotes a group of brain stimulation techniques that apply a weak electric current over two or more non-invasively, head-mounted electrodes. When employing a direct-current, this method is denoted transcranial direct current stimulation (tDCS). The general aim of all tES techniques is the modulation of brain function by an up- or downregulation of brain activity. Among these, transcranial direct current stimulation is investigated as an adjuvant tool to promote processes of the microscopic reorganization of the brain as a consequence of learning and, more specifically, rehabilitation therapy after a stroke. Current challenges of this research are a high variability in the achieved stimulation effects across subjects and an incomplete understanding of the interplay between its underlying mechanisms. A key component to understanding the stimulation mechanism is considered the electric field, which is exerted by the electrodes and distributes in the subjects' heads. A principle concept assumes that brain areas exposed to a higher electric field strength likewise experience a higher stimulation. This attributes the positioning of the electrodes a decisive role for the stimulation. However, the electric field distributes non-uniformly across subjects' brains due to the heterogeneous electrical conductivity profile of the human head. Moreover, the distribution pattern is variable between subjects due to their individual anatomy. A trivial estimation of the distribution of the electric field solely based on the position of the stimulating electrodes is, therefore, not precise enough for a well-targeted stimulation. Computer-based biophysical simulations of transcranial electric stimulation enable the individual approximation of the distribution pattern of the electric field in subjects based on their medical imaging data. They are, thus, increasingly employed for the planning and verification of tDCS applications and constitute an essential tool on the way to individualized stroke rehabilitation therapy. Software pipelines facilitating the underlying individualized processing for a wide range of researchers have been developed for use in healthy adults over the past years, but, to date, the simulation of patients with abnormal brain tissue and structure disrupting lesions remains a non-trivial task. Therefore, the presented project was dedicated to establishing and practically applying a tES simulation workflow. The processing of medical imaging data of neurological patients with abnormal brain tissue was a central requirement in this process. The basic simulation workflow was first designed and validated for the simulation of healthy adults. This comprised compiling medical image processing algorithms into a comprehensive workflow to identify and extract electrically relevant physiological structures of the human head and upper torso from magnetic resonance images. The identified structures had to be converted to computational models. The underlying physical problem of electric volume conduction in biological tissue was solved by means of numeric simulation. Over the course of normal aging, the brain is subjected to structural alterations, among which a loss of brain volume and the development of microscopic alterations of its fiber structure are the most relevant. In a second step, the workflow was, thus, extended to incorporate these phenomena of normal aging. The main challenge in this subproject was the biophysical modeling of the altered brain microstructure as the resulting alterations to the conductivity profile of the brain were so far not quantified in the literature. Therefore, the augmentation of the workflow most notably included the modeling of uncertain electrical properties. With this, the influence of the uncertain electrical conductivity of the biological structures of the human head on the electric field could be assessed. In a simulation study, including imaging data of 88 subjects, the influence of the altered brain fiber structure on the electric field was then systematically investigated. These tissue alterations were found to exhibit a highly localized and generally low impact. Finally, in a third step, tDCS simulations of stroke patients were conducted. Their large, structure-disrupting lesions were modeled in a more detailed manner than in previous stroke simulation studies, and they were physically, again, modeled by uncertain electrical conductivity resulting in uncertain electric field estimates. Individually simulated electric fields were related to the brain activation of 18 patients, considering the inherently uncertain electric field estimations. The goal was to clarify whether the stimulation exerts a positive influence on brain function in the context of rehabilitation therapy supporting brain reorganization following a stroke. While a weak correlation could be established, further investigation will be necessary to answer that research question.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms Bibliograph

Safety of Simultaneous Scalp and Intracranial Electroencephalography Functional Magnetic Resonance Imaging

Understanding the brain and its activity is one of the great challenges of modern science. Normal brain activity (cognitive processes, etc.) has been extensively studied using electroencephalography (EEG) since the 1930’s, in the form of spontaneous fluctuations in rhythms, and patterns, and in a more experimentally-driven approach in the form of event-related potentials allowing us to relate scalp voltage waveforms to brain states and behaviour. The use of EEG recorded during functional magnetic resonance imaging (EEG-fMRI) is a more recent development that has become an important tool in clinical neuroscience, for example, for the study of epileptic activity. The primary aim of this thesis is to devise a protocol in order to minimise the health risks that are associated with simultaneous scalp and intracranial EEG during fMRI (S- icEEG-fMRI). The advances in this technique will be helpful in presenting a new imaging method that will allow the measurement of brain activity with unprecedented sensitivity and coverage. However, this cannot be achieved without assessing the safety implications of such a technique. Therefore, five experiments were performed to fulfil the primary aim. First, the safety of icEEG- fMRI using body transmit RF coil was investigated to improve the results of previous attempts using a head transmit coil at 1.5T. The results of heating increases during a high-SAR sequence were in the range of 0.2-2.4 °C at the contacts with leads positioned along the central axis inside the MRI bore. These findings suggest the need for careful lead placement. Second, also for the body transmit coil we compared the heating in the vicinity of icEEG electrodes placed inside a realistically-shaped head phantom following the addition of scalp EEG electrodes. The peak temperature change was +2.7 °C at the most superior icEEG electrode contact without scalp electrodes, and +2.1 °C at the same contact and the peak increase in the vicinity of a scalp electrode contact was +0.6 °C (location FP2). These findings show that the S-icEEG-fMRI technique is feasible if our protocol is followed carefully. Third, the heating of a realistic 3D model of icEEG electrode during MRI using EM computational simulation was investigated. The resulting peak 10 g averaged SAR was 20% higher than without icEEG. Moreover, the superior icEEG placed perpendicular to B0 showed significant local SAR increase. These results were in line with previous studies. Fourth, the possibility of simplifying a complete 8-contact with 8 wires depth icEEG electrode model into an electrode with 1-contact and 1 wire using EM simulations was addressed. The results showed similar patterns of averaged SAR values around the electrode tip during phantom and electrode position along Z for the Complete and Simplified models, except an average maximum at Z = ~2.5 W/kg for the former. The SAR values during insertion depth for the Simplified model were double those for the Complete model. The effect of extension cable length is in agreement with previous experiments. Fifth, further simulations were implemented using two more simplified models: 8-contact with 1 wire shared with all contact and 8-contact 1 wire connected to each contact at a time as well as the previously modelled simplified 1-contact 1 wire. Two sets of simulations were performed: with a single electrode and with multiple electrodes. For the single electrode, three scenarios were tested: the first simplified model used only, the second simplified models used only and the third model positioned in different 13 locations. The results of these simulations showed about 11.4-20.5-fold lower SAR for the first model than the second and 0.29-5.82-fold lower SAR for the first model than the complete model. The results also showed increased SAR for the electrode close to the head coil than the ones away from it. For the multiple electrodes, three scenarios were tested: two 1-contact and wire electrodes in different separations, multiple electrodes with their wires separated and multiple electrodes with their wires shorted. The results showed interaction between the two tested electrodes. The results of the multiple electrodes presented 2 to ~10 times higher SAR for the separated setup than the shorted. The comparison between the 1-contact with 1 wire model and the complete model is still unknown and more tests are required to show it. From the findings of this PhD research, we conclude that a body RF coil can be utilized for icEEG-fMRI at 1.5 T; however, the safety protocol has to be implemented. In addition, scalp EEG can be used in conjunction with icEEG electrodes inside the body RF coil at 1.5 T and the safety protocol has to be followed. Finally, it is feasible to perform EM computational simulations using realistic icEEG electrodes on a human model. However, simplifying the realistic icEEG electrode model might result in overestimations of the heating, although it is possible that the simplification of the model can help to simulate more complex implantations such as the implantation of multiple electrodes with their leads open circuited or short circuited, which can provide more information about the safety of implanted patients inside the MRI

Understanding and overcoming head motion in ultra-high field Magnetic Resonance Imaging with parallel radio-frequency transmission

Ultra-high field (UHF) magnetic resonance imaging (MRI) offers more signal-to-noise compared to most clinical systems, but clinical uptake of UHF MRI remains low, partly due to artificial signal contrast variations and a higher risk of undesirable tissue heating at UHF. Parallel RF transmission (pTx) is capable of overcoming both issues, but the implications of patient motion on signal and safety (i.e., specific absorption rate; SAR) when pTx is used are currently not well understood. The work in this thesis aims to better characterise these effects, and presents novel approaches to help overcome them. The study chapters present investigations into firstly, the effects of motion on signal quality when different RF pulse types are used (Chapter 5); secondly, the effects of pTx coil dimensions on motion-related SAR changes (Chapter 6); and thirdly, the inter-subject variability of motion-related SAR changes in pTx (Chapter 7). Following this, two methods are outlined which aim to reduce the sensitivity of signal to head motion in pTx. These comprise firstly, a method which uses composite B1+ maps for pTx pulse design (Chapter 8), and secondly, a deep learning framework which can estimate B1+ maps following head motion (Chapter 9). Finally, the generalisability of the latter approach across coil models is explored (Chapter 10). Simulated data were used for all of the work presented. Electromagnetic field distributions were generated using Sim4Life (Zurich MedTech, Zurich, Switzerland). RF pulse design and evaluation was conducted in MATLAB (The MathWorks Inc., Natick, MA). Findings indicate that systematic differences in the signal and SAR behaviour arise when different RF pulse types and different pTx coils are used, respectively. On the other hand, differences were observed in the SAR sensitivity to motion across different virtual body models, but these were not clearly systematic. These findings indicate that new approaches are needed in order to guarantee good image quality and safety for pTx in cases of subject motion. The two proposed methods reduced the impact of motion on simulated signal profiles