Spatial-temporal Source Reconstruction for Magnetoencephalography PDF Download

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 203

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Author: Thomas R. Knösche
Publisher: Springer Nature
ISBN: 3030749185
Category : Medical
Languages : en
Pages : 429

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Book Description
This textbook provides a comprehensive and didactic introduction from the basics to the current state of the art in the field of EEG/MEG source reconstruction. Reconstructing the generators or sources of electroencephalographic and magnetoencephalographic (EEG/MEG) signals is an important problem in basic neuroscience as well as clinical research and practice. Over the past few decades, an entire theory, together with a whole collection of algorithms and techniques, has developed. In this textbook, the authors provide a unified perspective on a broad range of EEG/MEG source reconstruction methods, with particular emphasis on their respective assumptions about sources, data, head tissues, and sensor properties. An introductory chapter highlights the concept of brain imaging and the particular importance of the neuroelectromagnetic inverse problem. This is followed by an in-depth discussion of neural information processing and brain signal generation and an introduction to the practice of data acquisition. Next, the relevant mathematical models for the sources of EEG and MEG are discussed in detail, followed by the neuroelectromagnetic forward problem, that is, the prediction of EEG or MEG signals from those source models, using biophysical descriptions of the head tissues and the sensors. The main part of this textbook is dedicated to the source reconstruction methods. The authors present a theoretical framework of the neuroelectromagnetic inverse problem, centered on Bayes’ theorem, which then serves as the basis for a detailed description of a large variety of techniques, including dipole fit methods, distributed source reconstruction, spatial filters, and dynamic source reconstruction methods. The final two chapters address the important topic of assessment, including verification and validation of source reconstruction methods, and their actual application to real-world scientific and clinical questions. This book is intended as basic reading for anybody who is engaged with EEG/MEG source reconstruction, be it as a method developer or as a user, including advanced undergraduate students, PhD students, and postdocs in neuroscience, biomedical engineering, and related fields.

Author: Moritz Dannhauer
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Mithun Diwakar
Publisher:
ISBN: 9781124867786
Category :
Languages : en
Pages : 170

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Beamformer adaptive spatial filters have been used extensively in the field of magnetoencephalography (MEG) as tools to reconstruct functional activation of the brain. Conventional single beamformer techniques suffer from distortion in the presence of coherent activation of the cortex or are difficult to use due to the need of a priori information. These qualities present a major disadvantage to analyzing human brain responses, as coordinated functional responses require a degree of synchronous activation in different parts of the active cortex. In this dissertation, a novel beamformer technique, the multi-core beamformer, is developed that is robust to source correlation and does not require the use of a priori information. This novel approach is tested in both simulated and real experiments, including auditory and median-nerve stimulation, which provide well-studied systems to gauge the effectiveness of our new technique. Simulations show that the multi-core beamformer can successfully determine source time-courses, source powers, and source locations while minimizing or eliminating the distortion present in other methods. Results from real-life experiments show that the multi-core beamformer produces physiologically meaningful solutions that agree with previous functional imaging and neurophysiology studies. The use of the multi-core beamformer is expected to greatly contribute to the analysis of MEG recordings and, in general, improve our understanding of functional brain activity.

Author: Kensuke Sekihara
Publisher: Springer Science & Business Media
ISBN: 3540793704
Category : Technology & Engineering
Languages : en
Pages : 247

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Neural activity in the human brain generates coherent synaptic and intracellular currents in cortical columns that create electromagnetic signals which can be measured outside the head using magnetoencephalography (MEG) and electroencephalography (EEG). Electromagnetic brain imaging refers to techniques that reconstruct neural activity from MEG and EEG signals. Electromagnetic brain imaging is unique among functional imaging techniques for its ability to provide spatio-temporal brain activation profiles that reflect not only where the activity occurs in the brain but also when this activity occurs in relation to external and internal cognitive events, as well as to activity in other brain regions. Adaptive spatial filters are powerful algorithms for electromagnetic brain imaging that enable high-fidelity reconstruction of neuronal activity. This book describes the technical advances of adaptive spatial filters for electromagnetic brain imaging by integrating and synthesizing available information and describes various factors that affect its performance. The intended audience include graduate students and researchers interested in the methodological aspects of electromagnetic brain imaging.

Author: Selma Supek
Publisher: Springer
ISBN: 3642330452
Category : Technology & Engineering
Languages : en
Pages : 999

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Magnetoencephalography (MEG) is an invaluable functional brain imaging technique that provides direct, real-time monitoring of neuronal activity necessary for gaining insight into dynamic cortical networks. Our intentions with this book are to cover the richness and transdisciplinary nature of the MEG field, make it more accessible to newcomers and experienced researchers and to stimulate growth in the MEG area. The book presents a comprehensive overview of MEG basics and the latest developments in methodological, empirical and clinical research, directed toward master and doctoral students, as well as researchers. There are three levels of contributions: 1) tutorials on instrumentation, measurements, modeling, and experimental design; 2) topical reviews providing extensive coverage of relevant research topics; and 3) short contributions on open, challenging issues, future developments and novel applications. The topics range from neuromagnetic measurements, signal processing and source localization techniques to dynamic functional networks underlying perception and cognition in both health and disease. Topical reviews cover, among others: development on SQUID-based and novel sensors, multi-modal integration (low field MRI and MEG; EEG and fMRI), Bayesian approaches to multi-modal integration, direct neuronal imaging, novel noise reduction methods, source-space functional analysis, decoding of brain states, dynamic brain connectivity, sensory-motor integration, MEG studies on perception and cognition, thalamocortical oscillations, fetal and neonatal MEG, pediatric MEG studies, cognitive development, clinical applications of MEG in epilepsy, pre-surgical mapping, stroke, schizophrenia, stuttering, traumatic brain injury, post-traumatic stress disorder, depression, autism, aging and neurodegeneration, MEG applications in cognitive neuropharmacology and an overview of the major open-source analysis tools.

Author: Neil U. Desai
Publisher:
ISBN:
Category :
Languages : en
Pages : 82

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The inverse problem for magnetoencephalography (MEG) involves estimating the magnitude and location of sources inside the brain that give rise to the magnetic field recorded on the scalp as subjects execute cognitive, motor and/or sensory tasks. Given a forward model which describes how the signals emanate from the brain sources, a standard approach for estimating the MEG sources from scalp measurements is to use regularized least squares approaches such as LORETA, MNE, VARETA. These regularization methods impose a spatial constraint on the MEG inverse solution yet, they do not consider the temporal dynamics inherent to the biophysics of the problem. To address these issues, we present a state-space formulation of the MEG inverse problem by specifying a state equation that describes temporal dynamics of the MEG sources. Using a standard forward model system as the observation equation, we derive spatio-temporal Kalman filter and fixed-interval smoothing algorithms for MEG source localization. To compare the methods analytically, we present a Bayesian derivation of the regularized least squares and Kalman filtering methods. This analysis reveals that the estimates computed from the static methods bias the location of the sources toward zero. We compare the static, Kalman filter and fixed-interval smoothing methods in a simulated study of MEG data designed to emulate somatosensory MEG sources with different signal-to-noise ratios (SNR) and mean offsets. The data were mixtures of sinusoids with SNR ranging from 1 to 10 and mean offset ranging from 0 to 20. With both decrease in SNR and increase in mean offset, the Kalman filter and the fixed interval smoothing methods gave uniformly more accurate estimates of source locations in terms of mean square error. Because the fixed interval smoothing estimates were based on all recorded measurements, they had uniformly lower mean-squared errors than the Kalman estimates. These results suggest that state-space models can offer a more accurate approach to localizing brain sources from MEG recordings and that this approach may enhance appreciably the use of MEG as a non-invasive tool for studying brain function.

Author: Nikolaus Kriegeskorte
Publisher: MIT Press
ISBN: 0262016249
Category : Mathematics
Languages : en
Pages : 659

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How visual content is represented in neuronal population codes and how to analyze such codes with multivariate techniques. Vision is a massively parallel computational process, in which the retinal image is transformed over a sequence of stages so as to emphasize behaviorally relevant information (such as object category and identity) and deemphasize other information (such as viewpoint and lighting). The processes behind vision operate by concurrent computation and message passing among neurons within a visual area and between different areas. The theoretical concept of "population code" encapsulates the idea that visual content is represented at each stage by the pattern of activity across the local population of neurons. Understanding visual population codes ultimately requires multichannel measurement and multivariate analysis of activity patterns. Over the past decade, the multivariate approach has gained significant momentum in vision research. Functional imaging and cell recording measure brain activity in fundamentally different ways, but they now use similar theoretical concepts and mathematical tools in their modeling and analyses. With a focus on the ventral processing stream thought to underlie object recognition, this book presents recent advances in our understanding of visual population codes, novel multivariate pattern-information analysis techniques, and the beginnings of a unified perspective for cell recording and functional imaging. It serves as an introduction, overview, and reference for scientists and students across disciplines who are interested in human and primate vision and, more generally, in understanding how the brain represents and processes information.

Author: Todd C. Handy
Publisher: MIT Press
ISBN: 0262013088
Category : Cognitive neuroscience
Languages : en
Pages : 271

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Recent developments in the tools and techniques of data acquisition and analysis in cognitive electrophysiology.

Author: Christopher W. Tyler
Publisher: Frontiers Media SA
ISBN: 2889199231
Category : Neurosciences. Biological psychiatry. Neuropsychiatry
Languages : en
Pages : 144

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The ultimate goal of functional brain imaging is to provide optimal estimates of the neural signals flowing through the long-range and local pathways mediating all behavioral performance and conscious experience. In functional MRI (Magnetic Resonance Imaging), despite its impressive spatial resolution, this goal has been somewhat undermined by the fact that the fMRI response is essentially a blood-oxygenation level dependent (BOLD) signal that only indirectly reflects the nearby neural activity. The vast majority of fMRI studies restrict themselves to describing the details of these BOLD signals and deriving non-quantitative inferences about their implications for the underlying neural activity. This Frontiers Research Topic welcomed empirical and theoretical contributions that focus on the explicit relationship of non-invasive brain imaging signals to the causative neural activity. The articles presented within this resulting eBook aim to both highlight the importance and improve the non-invasive estimation of neural signals in the human brain. To achieve this aim, the following issues are targeted: (1) The spatial limitations of source localization when using MEG/EEG. (2) The coupling of the BOLD signal to neural activity. Articles discuss how animal studies are fundamental in increasing our understanding of BOLD fMRI signals, analyze how non-neuronal cell types may contribute to the modulation of cerebral blood flow, and use modeling to improve our understanding of how local field potentials are linked to the BOLD signal. (3) The contribution of excitatory and inhibitory neuronal activity to the BOLD signal. (4) Assessment of neural connectivity through the use of resting state data, computational modeling and functional Diffusion Tensor Imaging (fDTI) approaches.