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"Neuronal communication in the brain is associated with minute electrical currents that give rise to both electrical potentials on the scalp (measurable by means of electroencephalography [EEG]) and magnetic fields outside the head (measurable by means of magnetoencephalography [MEG]). Both MEG and EEG are noninvasive neurophysiological methods used to study brain dynamics, temporal changes in the activation patterns, and sequences. Their differences between MEG and EEG mainly reflect differences in the spread of electric potentials and magnetic fields generated by the same electric currents in the human brain. In this chapter, we give an overall description of the main principles of MEG and EEG, going deeper into details in the following chapters"--
EEG/MEG Source Reconstruction by Thomas R. Knösche,Jens Haueisen Pdf
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.
Brain Signals by Risto J. Ilmoniemi,Jukka Sarvas Pdf
A unified treatment of the generation and analysis of brain-generated electromagnetic fields. In Brain Signals, Risto Ilmoniemi and Jukka Sarvas present the basic physical and mathematical principles of magnetoencephalography (MEG) and electroencephalography (EEG), describing what kind of information is available in the neuroelectromagnetic field and how the measured MEG and EEG signals can be analyzed. Unlike most previous works on these topics, which have been collections of writings by different authors using different conventions, this book presents the material in a unified manner, providing the reader with a thorough understanding of basic principles and a firm basis for analyzing data generated by MEG and EEG. The book first provides a brief introduction to brain states and the early history of EEG and MEG, describes the generation of electromagnetic fields by neuronal activity, and discusses the electromagnetic forward problem. The authors then turn to EEG and MEG analysis, offering a review of linear and matrix algebra and basic statistics needed for analysis of the data, and presenting several analysis methods: dipole fitting; the minimum norm estimate (MNE); beamforming; the multiple signal classification algorithm (MUSIC), including RAP-MUSIC with the RAP dilemma and TRAP-MUSIC, which removes the RAP dilemma; independent component analysis (ICA); and blind source separation (BSS) with joint diagonalization.
Fisch and Spehlmann's EEG Primer by Bruce J. Fisch,Rainer Spehlmann Pdf
Organized to serve as a resource for those just beginning to learn EEG as well as those who are already experienced, it contains concise presentations of the fundamentals of EEG technology and interpretation as well as an up-to-date review of the latest digital EEG technology and EEG clinical correlations. Unlike other EEG textbooks, the second half of this book is uniquely organized according to EEG findings rather than individual disorders. This is the best practical approach to learning interpretation because it mirrors the actual practice of EEG, the EEGer is confronted by EEG patterns, not diagnoses. Each chapter begins with a summary of major concepts. An overview of EEG can be quickly obtained by those beginning the study of EEG by simply reading the introductory summaries of all chapters before reading the
From raw MEG/EEG to publication: How to perform MEG/EEG group analysis with free academic software by Vladimir Litvak,Arnaud Delorme,Francois Tadel,Alexandre Gramfort,Robert Oostenveld,Srikantan S Nagarajan Pdf
Hardbound. This edition of the EEG Primer has been renamed Spehlmann's EEG Primer in honour of the late Dr. Rainer Spehlmann who is remembered for his contributions of neuroscience and for his dedication to the advancement of clinical neurophysiology.The purpose of Spehlmann's EEG Primer is to introduce the fundamentals of EEG recording and interpretation in a clear and concise fashion. It is a primer in the sense that the text focusses on well established techniques and clinical correlations; those which are either controversial or not clinically useful are not discussed. Information that is essential for physicians seeking special certification in clinical neurophysiology has been included in the revised text and newly created appendix. The addition of the American EEG Society Guidelines in EEG, the International Federation of Societies for EEG and Clinical Neurophysiology, as well as a more extensive index, help make this edition a usef
MEG by Peter Hansen,Morten Kringelbach,Riitta Salmelin Pdf
Magnetoencephalography (MEG) is an exciting brain imaging technology that allows real-time tracking of neural activity, making it an invaluable tool for advancing our understanding of brain function. In this comprehensive introduction to MEG, Peter Hansen, Morten Kringelbach, and Riitta Salmelin have brought together the leading researchers to provide the basic tools for planning and executing MEG experiments, as well as analyzing and interpreting the resulting data. Chapters on the basics describe the fundamentals of MEG and its instrumentation, and provide guidelines for designing experiments and performing successful measurements. Chapters on data analysis present it in detail, from general concepts and assumptions to analysis of evoked responses and oscillatory background activity. Chapters on solutions propose potential solutions to the inverse problem using techniques such as minimum norm estimates, spatial filters and beamformers. Chapters on combinations elucidate how MEG can be used to complement other neuroimaging techniques. Chapters on applications provide practical examples of how to use MEG to study sensory processing and cognitive tasks, and how MEG can be used in a clinical setting. These chapters form a complete basic reference source for those interested in exploring or already using MEG that will hopefully inspire them to try to develop new, exciting approaches to designing and analyzing their own studies. This book will be a valuable resource for researchers from diverse fields, including neuroimaging, cognitive neuroscience, medical imaging, computer modelling, as well as for clinical practitioners.
Neuroimaging in Schizophrenia by Marek Kubicki,Martha E. Shenton Pdf
This comprehensive book explains the importance of imaging techniques in exploring and understanding the role of brain abnormalities in schizophrenia. The findings obtained using individual imaging modalities and their biological interpretation are reviewed in detail, and updates are provided on methodology, testable hypotheses, limitations, and new directions for research. The coverage also includes important recent applications of neuroimaging to schizophrenia, for example in relation to non-pharmacological interventions, brain development, genetics, and prediction of treatment response and outcome. Written by world renowned experts in the field, the book will be invaluable to all who wish to learn about the newest and most important developments in neuroimaging research in schizophrenia, how these developments relate to the last 30 years of research, and how they can be leveraged to bring us closer to a cure for this devastating disorder. Neuroimaging in Schizophrenia will assist clinicians in navigating what is an extremely complex field and will be a source of insight and stimulation for researchers.
Introduction to Computational Health Informatics by Arvind Kumar Bansal,Javed Iqbal Khan,S. Kaisar Alam Pdf
This class-tested textbook is designed for a semester-long graduate or senior undergraduate course on Computational Health Informatics. The focus of the book is on computational techniques that are widely used in health data analysis and health informatics and it integrates computer science and clinical perspectives. This book prepares computer science students for careers in computational health informatics and medical data analysis. Features Integrates computer science and clinical perspectives Describes various statistical and artificial intelligence techniques, including machine learning techniques such as clustering of temporal data, regression analysis, neural networks, HMM, decision trees, SVM, and data mining, all of which are techniques used widely used in health-data analysis Describes computational techniques such as multidimensional and multimedia data representation and retrieval, ontology, patient-data deidentification, temporal data analysis, heterogeneous databases, medical image analysis and transmission, biosignal analysis, pervasive healthcare, automated text-analysis, health-vocabulary knowledgebases and medical information-exchange Includes bioinformatics and pharmacokinetics techniques and their applications to vaccine and drug development
Fifty Years of Magnetoencephalography by Andrew C. Papanicolaou,Timothy P.L. Roberts,James W. Wheless Pdf
Fifty Years of Magnetoencephalography celebrates the first half century of research in and clinical applications of magnetoencephalography (MEG). It catalogs and documents its evolution as a means of imaging the ongoing activity of the brain and the activation of particular neuronal networks within it that mediate sensory motor and higher functions like language. The volume's first section looks at the discovery of MEG and its first tentative applications by three of its founders. The following sections detail the rapid progress in the development of the instrumentation necessary for recording noninvasively the magnetic signals on the head that are associated with the brain activity; improvements in the techniques for analyzing the magnetic signals and reconstructing, on their basis, the functional images of brain activity; and improvements in our understanding of the nature and significance of those signals. Subsequent sections of the book detail the main clinical applications of MEG in localizing brain areas that contain sources of epileptiform activity and areas encompassing parts of functional networks essential for motor and sensory function as well as for language that have become an essential part of planning for brain surgery in many epilepsy and tumor surgery centers around the world. In addition, several chapters describe the most current efforts aiming at expanding the utility of MEG in clinical diagnosis and theoretical research.
This concise but comprehensive guide covers common procedures in pain management necessary for daily practice, and includes topics on international pain medicine curricula, for example, the American Board of Anesthesiology, World Institute of Pain/Fellow of Interventional Pain Practice, and American Board of Pain Medicine. Treatments for pain are discussed, including nerve blocks (head, neck, back, pelvis and lower extremity). Chapters have a consistent format including high yield points for exams, and questions in the form of case studies. Pain: A Review Guide is aimed at trainees in pain medicine all over the world. This book will also be beneficial to all practitioners who practice pain.
Machine Learning for Brain Disorders by Olivier Colliot Pdf
This Open Access volume provides readers with an up-to-date and comprehensive guide to both methodological and applicative aspects of machine learning (ML) for brain disorders. The chapters in this book are organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the main types of data used to characterize brain disorders, including clinical assessments, neuroimaging, electro- and magnetoencephalography, genetics and omics data, electronic health records, mobile devices, connected objects and sensors. Part Three covers the core methodologies of ML in brain disorders and the latest techniques used to study them. Part Four is dedicated to validation and datasets, and Part Five discusses applications of ML to various neurological and psychiatric disorders. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Comprehensive and cutting, Machine Learning for Brain Disorders is a valuable resource for researchers and graduate students who are new to this field, as well as experienced researchers who would like to further expand their knowledge in this area. This book will be useful to students and researchers from various backgrounds such as engineers, computer scientists, neurologists, psychiatrists, radiologists, and neuroscientists.
Introduction to Neuroimaging Analysis by Mark Jenkinson,Michael Chappell Pdf
MRI has emerged as a powerful way of studying in-vivo brain structure and function in both healthy and disease states. Whilst new researchers may be able to call upon advice and support for acquisition from operators, radiologists and technicians, it is more challenging to obtain an understanding of the principles of analysing neuroimaging data. This is crucial for choosing acquisition parameters, designing and performing appropriate experiments, and correctly interpreting the results. This primer gives a general and accessible introduction to the wide array of MRI-based neuroimaging methods that are used in research. Supplemented with online datasets and examples to enable the reader to obtain hands-on experience working with real data, it provides a practical and approachable introduction for those new to the neuroimaging field. The text also covers the fundamentals of what different MRI modalities measure, what artifacts commonly occur, the essentials of the analysis, and common 'pipelines' including brain extraction, registration and segmentation. As it does not require any background knowledge beyond high-school mathematics and physics, this primer is essential reading for anyone wanting to work in neuroimaging or grasp the results coming from this rapidly expanding field. The Oxford Neuroimaging Primers are short texts aimed at new researchers or advanced undergraduates from the biological, medical or physical sciences. They are intended to provide a broad understanding of the ways in which neuroimaging data can be analyzed and how that relates to acquisition and interpretation. Each primer has been written so that it is a stand-alone introduction to a particular area of neuroimaging, and the primers also work together to provide a comprehensive foundation for this increasingly influential field.