31 research outputs found

    Probing the sub-thalamic nucleus: development of bio-markers from very Local Field Potentials

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    Magnetoencephalography

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    This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

    THE MANY WAYS OF WAKING UP FROM SLEEP - MOVING FORWARD THE ANALYSIS OF SLEEP MICROARCHITECTURE

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    One of the defining characteristics of sleep is that it is readily reversible towards wakefulness. This is exemplified in the common daily experience of waking up in the morning. My thesis studies sleep-wake transitions that are equally common and frequent, yet often not consciously perceived and neglected as random sleep perturbations of minor significance. Using mice as an experimental species, I find that healthy non-rapid-eye-movement sleep (NREMS), also named deep restorative sleep, is a dynamic brain state showing defined, periodically recurring moments of fragility. During these, diverse types of brief arousal-like events with various combinations of physiological correlates appear, including global or local cortical activation, muscle activity, and heart rate changes. Using a mice model of chronic neuropathic pain, I find that the rules I have identified in healthy sleep serve to identify previously unrecognized sleep disruptions that could contribute to sleep complaints of chronic pain patients. The experimental and analytical methods I have developed in these studies also helped in the identification of the neuronal basis of the fragility periods of NREM sleep. Together, my studies offer novel insights and analytical tools for the study of sleep-wake transitions and their perturbance in pathological conditions linked to sensory discomfort. More specifically, my work departed from recent findings that NREMS in mice is divided in recurring periods of sleep fragility at frequencies ~0.02 Hz, characterized by heightened arousability. Through analyzing the temporal distribution of brief arousal events termed microarousals, I hypothesized that these fragility periods could serve a time raster for the probing of spontaneous sleep perturbations. Motivated by the question of how sensory discomfort caused by pain affects sleep, I have used the spared nerve injury (SNI) model, which consists in the injury of two of the 3 branches of the sciatic nerve. I found that the role of fragility periods in timing spontaneous arousals is highly useful to identify sleep disruptions not commonly detected with standard polysomnographic measures. Thus, by scrutinizing the fragility periods of NREMS in the SNI mice, I discovered an overrepresentation of a novel form of local perturbation within the hindlimb primary somatosensory cortex (S1HL), accompanied by heart rate increases. In addition, I showed that SNI animals woke up more frequently facing external stimuli, using closed-loop methods targeting specifically the fragility or continuity periods. These findings led me to propose that chronic pain-related sleep complaints may arise primarily from a perturbed arousability. The closed-loop techniques to probe arousability could be transferred to interrogate neuronal mechanisms underlying NREMS fragility, leading to the recognition that intrusion of wake-related activity into NREMS is a previously underappreciated mechanism controlling sleep fragility and architecture. Overall, I present my thesis to advance the view on NREMS as a dynamic heterogeneous state of which insights into its neuronal mechanisms, and its physio- and pathophysiological manifestations in animal models should be key to formulate testable hypotheses aimed to cure the suffering of sleep disorder in human. -- Une des caractĂ©ristiques qui dĂ©finit le sommeil, est que l’on peut rapidement retourner Ă  un Ă©tat d’éveil. De fait, nous l’expĂ©rimentons chaque matin au rĂ©veil. Ma thĂšse Ă©tudie les transitions sommeil-Ă©veil qui, bien que frĂ©quentes, sont souvent non consciemment perçues et traitĂ©es comme des perturbations sans importance et alĂ©atoires du sommeil. En utilisant la souris comme modĂšle expĂ©rimental, je montre que le sommeil sans mouvements rapides des yeux (NREMS), Ă©galement appelĂ© le sommeil profond et rĂ©parateur, est un Ă©tat cĂ©rĂ©bral dynamique composĂ© de pĂ©riodes discrĂštes et rĂ©currentes de fragilitĂ© face Ă  des stimuli externe. Pendant celles-ci, plusieurs types d’évĂšnements associĂ©s Ă  des Ă©veils brefs apparaissent, combinant activation corticale, activitĂ© musculaire et/ou une hausse des battements cardiaques. Je dĂ©montre que la comprĂ©hension des transitions sommeil-Ă©veil physiologiques s’avĂšre utile pour Ă©tudier le sommeil de souris souffrant de douleurs neuropathiques chroniques. Ces souris prĂ©sentent un nouveau type de perturbations locales lors du sommeil, qui pourraient possiblement expliquer une partie des plaintes de mauvais sommeil exprimĂ©es par les patients souffrant de douleurs chroniques. Les mĂ©thodes analytiques et expĂ©rimentales que j’ai dĂ©veloppĂ©es dans ces Ă©tudes ont aussi aidĂ© Ă  l’identification des bases neuronales de la genĂšse des pĂ©riodes de fragilitĂ©s du sommeil NREM. En somme, mes Ă©tudes offrent des connaissances inĂ©dites et des mĂ©thodes d’analyses pour l’étude des transitions sommeil-Ă©veil et de leurs perturbations en conditions pathologiques. Une Ă©tude rĂ©cente du laboratoire a montrĂ© que le sommeil NREM est divisĂ© en pĂ©riodes de fragilitĂ© alternant avec des pĂ©riodes de non-fragilitĂ© (continuitĂ©), environ toutes les 50 secondes ce qui donne une frĂ©quence de 0.02 Hz. Les pĂ©riodes de fragilitĂ© sont caractĂ©risĂ©es par une hausse de « l’éveillabilitĂ© » ou propension Ă  s’éveiller. Ma premiĂšre observation est que les Ă©veils brefs, couramment appelĂ©s micro-rĂ©veils, prĂ©sentent une distribution temporelle hautement restreinte aux pĂ©riodes de fragilitĂ©. Ainsi, j’ai Ă©mis l’hypothĂšse que ces pĂ©riodes pourraient servir de moments spĂ©cialement choisis par le cerveau pour la mesure de potentielles perturbations spontanĂ©es. MotivĂ© par la question de comment les douleurs chroniques perturbent le sommeil, je l’ai analysĂ© chez un modĂšle de souris de douleurs neuropathique, le modĂšle de d’épargne du nerf sural (SNI). Le rĂŽle des pĂ©riodes de fragilitĂ© Ă  restreindre les micro- rĂ©veils s’est avĂ©rĂ© trĂšs utile pour dĂ©tecter de nouvelles formes de rĂ©action Ă  des perturbations qui ne sont pas Ă©videntes par des analyses classiques du sommeil. En effet, spĂ©cifiquement pendant ces pĂ©riodes de fragilitĂ©, j’ai dĂ©couvert une sur-reprĂ©sentation d’un nouveau type d’éveil local confinĂ© au cortex somatosensoriel primaire et accompagnĂ© d’une hausse du rythme cardiaque. De plus, en utilisant de nouvelles mĂ©thodes basĂ©es sur des boucles-fermĂ©es, j’ai dĂ©montrĂ© que les souris SNI se rĂ©veillaient plus frĂ©quemment que leurs contrĂŽles en faisant face Ă  des stimuli externes. Sur la base de ces dĂ©couvertes, je propose que les plaintes de mauvais sommeil chez les patients souffrant de douleurs chroniques puissent prendre leur source dans une Ă©veillabilitĂ© perturbĂ©e. Les mĂ©thodes de boucles-fermĂ©es pour analyser l’éveillabilitĂ© a aussi pu ĂȘtre transfĂ©rĂ©e pour l’étude optogĂ©nĂ©tique des mĂ©canismes neuronaux Ă  la base de la fragilitĂ© du sommeil NREM. Cela a menĂ© Ă  la reconnaissance que l’intrusion d’activitĂ© normalement associĂ©e Ă  l’éveil dans le sommeil est un mĂ©canisme de contrĂŽle de sa fragilitĂ© et de son architecture souvent ignorĂ© dans le domaine. En somme, ma thĂšse permet une avancĂ©e de notre vision du sommeil NREM comme Ă©tant un Ă©tat dynamique et hĂ©tĂ©rogĂšne dont les mĂ©canismes neuronaux sous-jacent, en conditions normales et pathogĂ©niques, sont clefs pour la formulation d’hypothĂšses testables visant Ă  la guĂ©rison des patients souffrant de troubles du sommeil

    Task-based fMRI investigation of the newborn brain: sensorimotor development and learning

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    Human brain development relies upon the interaction between genetic and environmental factors, and the latter plays a critical role during the perinatal period. In this period, neuronal plasticity through experience-dependent activity is enhanced in the sensory systems, and drive the maturation of the brain. While plasticity is essential for maturation, it is also a source of vulnerability as altered early experiences may interact with the normal course of development. This is particularly evident in infants born preterm, who are prematurely exposed to a sensory-rich environment, and at risk or neurodevelopmental disorders. In keeping with the somatosensory system being at a critical period for development during late gestation, sensorimotor disorders, such as cerebral palsy, are more common in preterm compared with full-term born infants. It is therefore important to understand the normal trajectory of sensorimotor development and how this may be moulded by early sensory experiences. It is well acknowledged that the sensorimotor cortex is topographically organised so that different body parts map to a specific location within the cortex and this map is generally referred to as the ``homunculus". Although the somatotopy has been well characterised in the mature brain, it remains unknown when this organisation emerges during development. Animal studies hints that functional cortical maps might emerge across the equivalent period to the third trimester of human gestation, nevertheless there is currently no evidence. Therefore, I first investigated the topography of the preterm somatosensory cortex in a group of newborn infants. In this purpose I used fMRI and automated robotic tools and measured the functional responses to different sensory simulations (delivered to the mouth, wrists and ankles). The results provide evidence that it is possible to identify distinct areas in the somatosensory cortex devoted to different body parts even in the preterm brain supporting the presence of an immature \textit{homunculus}. Next, I wanted to investigate how activity and development in the sensorimotor system are influenced by experience. Experience-dependent plasticity is the basis of learning (e.g. adaptive behaviour), which is observed in newborn infants. Associative learning in particular has been widely investigated in infants, however, the underlining neuronal processes have previously been poorly understood. To study the neural correlates of associative learning in newborn infants, I developed and used a classical conditioning paradigm in combination with robot-assisted fMRI. The results confirm that associative learning can occur even at this early stage of life and with non-aversive stimuli. More importantly, I could observe learning-induced changes in brain activity within the primary sensory cortices, suggesting that such experience can shape cortical circuitry and is likely to influence early brain development.Open Acces

    Multimodal Investigation of Neuronal Responses

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    This thesis describes an investigation of neuronal responses with both magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). MEG and fMRI are widely used in neuroscience. However, aspects of the MEG and fMRI signal are still not well understood, particularly post-stimulus responses – responses which occur after a stimulus has ended. Post-stimulus responses have been shown to correlate with various illnesses and as a result, MEG and fMRI have yet to reach their full potential clinically. By developing carefully controlled experiments, MEG is used in this thesis to characterise post-stimulus responses to a grip-force task. The results showed that the beta-band post-stimulus response (post-movement beta rebound, PMBR) is modulated by task duration. Functional network analysis, using amplitude envelope correlation and a hidden Markov model, showed that the PMBR re-establishes networks after breaking down during a task, suggesting the PMBR is related to functional connectivity. The results of this thesis provide new information about the nature of the PMBR, demonstrating that it can be systematically controlled by task parameters and provides insight into its generation. It is hoped this research will contribute to a deeper understanding of the PMBR and provide a step forward for its use clinically. In fMRI, the origin of the post-stimulus response is also poorly understood. To investigate fMRI post-stimulus responses, an MR pulse sequence was developed and optimised to measure blood flow, volume and oxygenation changes simultaneously at 7 T. This was implemented with the grip-force task, allowing direct comparison between MEG and fMRI. This study provides new insights into the fMRI post-stimulus undershoot which warrant further investigation. Understanding the link between fMRI and MEG signals will help further understanding of both modalities and how they relate to neuronal activity. Finally, the applications of fMRI were explored by comparing fMRI responses in patients with focal hand dystonia (FHD) with healthy controls. 7 T fMRI was used to map cortical fingertip representations and measures were developed to compare overlap of digit representations between patients and healthy controls. This project provided an important opportunity to advance the understanding of FHD and was the first study to use fMRI to explore the effects of treatment on patients with FHD

    Multimodal Investigation of Neuronal Responses

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    This thesis describes an investigation of neuronal responses with both magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). MEG and fMRI are widely used in neuroscience. However, aspects of the MEG and fMRI signal are still not well understood, particularly post-stimulus responses – responses which occur after a stimulus has ended. Post-stimulus responses have been shown to correlate with various illnesses and as a result, MEG and fMRI have yet to reach their full potential clinically. By developing carefully controlled experiments, MEG is used in this thesis to characterise post-stimulus responses to a grip-force task. The results showed that the beta-band post-stimulus response (post-movement beta rebound, PMBR) is modulated by task duration. Functional network analysis, using amplitude envelope correlation and a hidden Markov model, showed that the PMBR re-establishes networks after breaking down during a task, suggesting the PMBR is related to functional connectivity. The results of this thesis provide new information about the nature of the PMBR, demonstrating that it can be systematically controlled by task parameters and provides insight into its generation. It is hoped this research will contribute to a deeper understanding of the PMBR and provide a step forward for its use clinically. In fMRI, the origin of the post-stimulus response is also poorly understood. To investigate fMRI post-stimulus responses, an MR pulse sequence was developed and optimised to measure blood flow, volume and oxygenation changes simultaneously at 7 T. This was implemented with the grip-force task, allowing direct comparison between MEG and fMRI. This study provides new insights into the fMRI post-stimulus undershoot which warrant further investigation. Understanding the link between fMRI and MEG signals will help further understanding of both modalities and how they relate to neuronal activity. Finally, the applications of fMRI were explored by comparing fMRI responses in patients with focal hand dystonia (FHD) with healthy controls. 7 T fMRI was used to map cortical fingertip representations and measures were developed to compare overlap of digit representations between patients and healthy controls. This project provided an important opportunity to advance the understanding of FHD and was the first study to use fMRI to explore the effects of treatment on patients with FHD

    Curve Estimation and Signal Discrimination in Spatial Problems

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    In many instances arising prominently, but not exclusively, in imaging problems, it is important to condense the salient information so as to obtain a low-dimensional approximant of the data. This thesis is concerned with two basic situations which call for such a dimension reduction. The first of these is the statistical recovery of smooth edges in regression and density surfaces. The edges are understood to be contiguous curves, although they are allowed to meander almost arbitrarily through the plane, and may even split at a finite number of points to yield an edge graph. A novel locally-parametric nonparametric method is proposed which enjoys the benefit of being relatively easy to implement via a `tracking' approach. These topics are discussed in Chapters 2 and 3, with pertaining background material being given in the Appendix. In Chapter 4 we construct concomitant confidence bands for this estimator, which have asymptotically correct coverage probability. The construction can be likened to only a few existing approaches, and may thus be considered as our main contribution. ¶ Chapter 5 discusses numerical issues pertaining to the edge and confidence band estimators of Chapters 2-4. Connections are drawn to popular topics which originated in the fields of computer vision and signal processing, and which surround edge detection. These connections are exploited so as to obtain greater robustness of the likelihood estimator, such as with the presence of sharp corners. ¶ Chapter 6 addresses a dimension reduction problem for spatial data where the ultimate objective of the analysis is the discrimination of these data into one of a few pre-specified groups. In the dimension reduction step, an instrumental role is played by the recently developed methodology of functional data analysis. Relatively standar non-linear image processing techniques, as well as wavelet shrinkage, are used prior to this step. A case study for remotely-sensed navigation radar data exemplifies the methodology of Chapter 6

    Simultaneous EEG-fMRI at ultra-high field for the study of human brain function

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    Scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have highly complementary domains, and their combination has been actively sought within neuroscience research. The important gains in fMRI sensitivity achieved with higher field strengths open exciting perspectives for combined EEG-fMRI; however, simultaneous acquisitions are subject to highly undesirable interactions between the two modalities, which can strongly compromise data quality and subject safety, and most of these interactions are increased at higher fields. The work described in this thesis was centered on the development of simultaneous EEG-fMRI in humans at 7T, covering aspects of subject safety, signal quality assessment, and quality improvement. Additionally, given the potential value of high-field EEG-fMRI to study the neuronal correlates of so-called negative BOLD responses, an initial fMRI study was dedicated to these phenomena. The initial fMRI study aimed to characterize positive (PBR) and negative BOLD responses (NBR) to visual checkerboard stimulation of varying contrast and duration, focusing on NBRs occurring in visual and in auditory cortical regions. Results showed that visual PBRs and both visual and auditory NBRs significantly depend on stimulus contrast and duration, suggesting a dynamic system of visual-auditory interactions, sensitive to stimulus contrast and duration. The neuronal correlates of these interactions could not be addressed in higher detail with fMRI alone, yet could potentially be clarified in future work with combined EEG-fMRI. Moving on to simultaneous EEG-fMRI implementation, the first stage comprised an assessment of potential safety concerns at 7T. The safety tests comprised numerical simulations of RF power distribution and real temperature measurements on a phantom during acquisition. Overall, no significant safety concerns were found for the setup tested. A characterization of artifacts induced on MRI data due to the presence of EEG components was then performed. With the introduction of the EEG system, functional and anatomical images exhibited general losses in spatial SNR, with a smaller loss in temporal SNR in fMRI data. B0 and B1 field mapping pointed towards RF pulse disruption as the major degradation mechanism affecting MRI data. The main part of this work focused on EEG artifacts induced by MRI. The first step focused on optimizing signal transmission between the EEG cap and amplifiers, to minimize artifact contamination at this important stage. Along this line, adequate cable shortening and bundling effectively reduced environment noise in EEG recordings. Simultaneous acquisitions were then performed on humans using the optimized setup. On average, EEG data exhibited clear alpha modulation and average visual evoked potentials (VEP), with concomitant BOLD signal changes. In the second step, a novel approach for head motion artifact detection was developed, based on a simple modification of the EEG cap, and simultaneous acquisitions were performed in volunteers undergoing visual checkerboard stimulation. After gradient artifact correction, EEG signal variance was found to be largely dominated by pulse artifacts, but contributions from spontaneous motion were still comparable to those of neuronal activity. Using a combination of pulse artifact correction, motion artifact correction and ICA denoising, strong improvements in data quality could be obtained, especially at a single-trial level

    On the Recognition of Emotion from Physiological Data

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    This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

    Characterizing neural mechanisms of attention-driven speech processing

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