8 research outputs found
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Spatio-temporal evolution of interictal epileptic activity : a study with unaveraged multichannel MEG data in association with MRIs.
This thesis addresses issues relating to MEG modelling, analysis and interpretation of results. A source model employing current density distributions, namely Magnetic Field Tomography (MET), is used to obtain the MEG results. The first issue of concern refers to the registration of MEG data with structural MR images in an attempt to improve the localisation capability of MEG/MET. Simulations testing some spatial and tem poral aspects of the reconstruction capability of MET are also provided. A novel way of conducting MET studies in depth is suggested and implemented: the iterative use of a source space designed to cover deep situated structures on either side of the brain. The main bulk of this thesis is concerned with research into interictal epileptic activity as recorded by means of multichannel MEG system s and analysed using MET. The major aim is to investigate whether or not MET analysis of unaveraged MEG data (single epochs) is feasible in cases of pathophysiological signals and more specifically interictal
signals from patients with epilepsy of a complex partial type. The investigation is undertaken against the "traditional" view of the impropriety and absurdity of using single epoch records in the MEG analysis due to noise dominance; we provide evidence that analysis of single, unaveraged epileptic spikes is actually feasible: we demonstrate spatio-temporal coherence in the MET results of the various single interictal events and show that activity extracted from the "averaged event" is made up of activity contributions which occur intermittently and at variable latencies. Our statements are drawn from the study of both superficial and deep activity
Cognitive and Neural Map Representations in Schizophrenia
An ability to build structured cognitive maps of the world may lie at the heart of understanding cognitive features of schizophrenia. In rodents, cognitive map representations are supported by sequential hippocampal place cell reactivations during rest (offline), known as replay. These events occur in the context of local high frequency ripple oscillations, and whole-brain default mode network (DMN) activation. Genetic mouse models of schizophrenia also report replay and ripple abnormalities. Here, I investigate the behavioural and neural signatures of structured internal representations in people with a diagnosis of schizophrenia (PScz, n = 29) and matched control participants (n = 28) using magnetoencephalography (MEG). Participants were asked to infer correct sequential relationships between task pictures by applying a pre-learned task template to visual experiences containing these pictures. In Chapter 3 I show that, during a post-task rest session, controls exhibited fast spontaneous neural reactivation of task state representations that replayed inferred relationships. Replay was coincident with increased ripple power in hippocampus, which may be related to NMDAR availability (Chapter 4). PScz showed both reduced replay and augmented ripple power, convergent with genetic mouse models. These abnormalities were linked to impairments in behavioural acquisition of task structure, and to its subsequent representation in visually evoked neural responses. In Chapter 5 I explore the temporal coupling between replay onsets and DMN activation. I show an impairment in this association in PScz, which related to subsequent mnemonic maintenance of learned task structure, complementing previous reports of DMN abnormalities in the condition. Finally, in Chapter 6, using a separate verbal fluency task, I show that PScz exhibit evidence of reduced use of (semantic) associative information when sampling concepts from memory. Together, my results provide support for a hypothesis that schizophrenia is associated with abnormalities in neural and behavioural correlates of cognitive map representation
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Neurobiology of incremental speech comprehension
Understanding spoken language requires the rapid transition from perceptual processing of the auditory input through a variety of cognitive processes involved in constructing the mental representation of the message that the speaker is intending to convey. Listeners carry out these complex processes very rapidly and accurately as they hear each word incrementally unfolding in a sentence. However, little is known about the specific spatiotemporal patterning of this wide range of incremental processing operations that underpin the dynamic transitions from the speech input to the development of a meaning interpretation of an utterance. This thesis aims to address this set of issues by investigating the spatiotemporal dynamics of brain activity as spoken sentences unfold over time in order to illuminate the neurocomputational properties of the human language processing system and determine how the representation of a spoken sentence develops incrementally as each upcoming word is heard.
Using a novel application of multidimensional probabilistic modelling combined with models from computational linguistics, I developed models of a variety of computational processes associated with accessing and processing the syntactic and semantic properties of sentences and tested these models at various points as sentences unfolded over time. Since a wide range of incremental processes occur very rapidly during speech comprehension, it is crucial to keep track of the temporal dynamics of the neural computations involved. To do this, I used combined electroencephalography and magnetoencephalography (EMEG) to record neural activity with millisecond resolution and analyzed the recordings in source space using univariate and/or multivariate approaches. The results confirm the value of this combination of methods in examining the properties of incremental speech processing. My findings corroborate the predictive nature of human speech comprehension and demonstrate that the effects of early semantic constraint are not dependent on explicit syntactic knowledge
Recent Application in Biometrics
In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers
Preliminary studies in imaging neuronal depolarization in the brain with electrical or magnetic detection impedance tomography.
Electrical impedance Tomography (EIT) is a novel medical imaging method which has the potential to provide the revolutionary advance of a method to image fast neural activity non-invasively. by imaging electrical impedance changes over milliseconds which occur when neuronal ion channels open during activity. These changes have been estimated to be c.1% locally in cerebral cortex, if measured with applied current below 100Hz. The purpose of this work was to determine if such changes could be reproducibly recorded in humans non invasive First, a novel recessed electrode was designed and tested to determine to enable a maximal current of 1mA to be applied to the scalp without causing painful skin sensation. Modelling indicated that this produced a peak current density of 0.3A/m2 in underlying cortex, which was below the threshold for stimulation. Next, the signal-to-noise ratio of impedance changes during evoked visual activity was investigated in healthy volunteers with current injected with scalp electrodes and recording of potential by scalp electrodes (Low Frequency EIT) or magnetic field by magnetoencephalography (Magnetic Detection EIT). Numerical FEM simulations predicted that resistivity changes of 1% in the primary7 visual cortex translate into scalp voltage changes of IjiV (0.004%) and external magnetic field changes of 30fT (0.2%) and were independently validated in saline filled tanks. In vivo, similar changes with a signal-to-noise ratio of 3 after averaging for 10 minutes were recorded for both methods the main noise sources were background brain activity and the current source. These studies with non-invasive scalp recording have, for the first time, demonstrated the existence of such changes when measured non-invasively. These are unfortunately too low to enable reliable imaging within a realistic recording time but support the view that such imaging could be possible in animal or human epileptic studies with electrodes placed on the brain or non-invasively following technological improvements this further work is currently in progress
Bayesian inversion in biomedical imaging
Biomedizinische Bildgebung ist zu einer Schlüsseltechnik geworden, Struktur oder Funktion lebender Organismen nicht-invasiv zu untersuchen. Relevante Informationen aus den gemessenen Daten zu rekonstruieren erfordert neben mathematischer Modellierung und numerischer Simulation das verlässliche Lösen schlecht gestellter inverser Probleme. Um dies zu erreichen müssen zusätzliche a-priori Informationen über die zu rekonstruierende Größe formuliert und in die algorithmischen Lösungsverfahren einbezogen werden. Bayesianische Invertierung ist eine spezielle mathematische Methodik dies zu tun. Die vorliegende Arbeit entwickelt eine aktuelle Übersicht Bayesianischer Invertierung und demonstriert die vorgestellten Konzepte und Algorithmen in verschiedenen numerischen Studien, darunter anspruchsvolle Anwendungen aus der biomedizinischen Bildgebung mit experimentellen Daten. Ein Schwerpunkt liegt dabei auf der Verwendung von Dünnbesetztheit/Sparsity als a-priori Information.Biomedical imaging techniques became a key technology to assess the structure or function of living organisms in a non-invasive way. Besides innovations in the instrumentation, the development of new and improved methods for processing and analysis of the measured data has become a vital field of research. Building on traditional signal processing, this area nowadays also comprises mathematical modeling, numerical simulation and inverse problems. The latter describes the reconstruction of quantities of interest from measured data and a given generative model. Unfortunately, most inverse problems are ill-posed, which means that a robust and reliable reconstruction is not possible unless additional a-priori information on the quantity of interest is incorporated into the solution method. Bayesian inversion is a mathematical methodology to formulate and employ a-priori information in computational schemes to solve the inverse problem. This thesis develops a recent overview on Bayesian inversion and exemplifies the presented concepts and algorithms in various numerical studies including challenging biomedical imaging applications with experimental data. A particular focus is on using sparsity as a-priori information within the Bayesian framework. <br
Fusion of magnetometer and gradiometer sensors of MEG in the presence of multiplicative error.
Novel neuroimaging techniques have provided unprecedented information on the structure and function of the living human brain. Multimodal fusion of data from different sensors promises to radically improve this understanding, yet optimal methods have not been developed. Here, we demonstrate a novel method for combining multichannel signals. We show how this method can be used to fuse signals from the magnetometer and gradiometer sensors used in magnetoencephalography (MEG), and through extensive experiments using simulation, head phantom and real MEG data, show that it is both robust and accurate. This new approach works by assuming that the lead fields have multiplicative error. The criterion to estimate the error is given within a spatial filter framework such that the estimated power is minimized in the worst case scenario. The method is compared to, and found better than, existing approaches. The closed-form solution and the conditions under which the multiplicative error can be optimally estimated are provided. This novel approach can also be employed for multimodal fusion of other multichannel signals such as MEG and EEG. Although the multiplicative error is estimated based on beamforming, other methods for source analysis can equally be used after the lead-field modification
Fusion of magnetometer and gradiometer sensors of MEG in the presence of multiplicative error.
Novel neuroimaging techniques have provided unprecedented information on the structure and function of the living human brain. Multimodal fusion of data from different sensors promises to radically improve this understanding, yet optimal methods have not been developed. Here, we demonstrate a novel method for combining multichannel signals. We show how this method can be used to fuse signals from the magnetometer and gradiometer sensors used in magnetoencephalography (MEG), and through extensive experiments using simulation, head phantom and real MEG data, show that it is both robust and accurate. This new approach works by assuming that the lead fields have multiplicative error. The criterion to estimate the error is given within a spatial filter framework such that the estimated power is minimized in the worst case scenario. The method is compared to, and found better than, existing approaches. The closed-form solution and the conditions under which the multiplicative error can be optimally estimated are provided. This novel approach can also be employed for multimodal fusion of other multichannel signals such as MEG and EEG. Although the multiplicative error is estimated based on beamforming, other methods for source analysis can equally be used after the lead-field modification