This book is intended as an introduction to neuroradiology and aims to provide the reader with a comprehensive overview of this highly specialized radiological subspecialty. One hundred illustrated cases from clinical practice are presented in a standard way. Each case is supported by representative images and is divided into three parts: a brief summary of the patient’s medical history, a discussion of the disease, and a description of the most characteristic imaging features of the disorder. The focus is not only on common neuroradiological entities such as stroke and acute head trauma but also on less frequent disorders that the practitioner should recognize. Learning Neuroimaging: 100 Essential Cases is an ideal resource for neuroradiology and radiology residents, neurology residents, neurosurgery residents, nurses, radiology technicians, and medical students.
This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021, held on September 27, 2021, in conjunction with MICCAI 2021. The workshop was held virtually due to the COVID-19 pandemic. The 17 papers presented in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections named: computational anatomy and brain networks and time series.
This book constitutes the revised selected papers from the 4th International Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2014, held in Montreal, QC, Canada, in December 2014 as a satellite event of the 11th annual conference on Neural Information Processing Systems, NIPS 2014. The 10 MLINI 2014 papers presented in this volume were carefully reviewed and selected from 17 submissions. They were organized in topical sections named: networks and decoding; speech; clinics and cognition; and causality and time-series. In addition, the book contains the 3 best papers presented at MLINI 2013.
This accessible primer gives an introduction to the wide array of MRI-based neuroimaging methods that are used in research. It provides an overview of the fundamentals of what different MRI modalities measure, what artifacts commonly occur, the essentials of the analysis, and common 'pipelines'.
By 2030 there will be about 70 million people in the United States who are older than 64. Approximately 26 percent of these will be racial and ethnic minorities. Overall, the older population will be more diverse and better educated than their earlier cohorts. The range of late-life outcomes is very dramatic with old age being a significantly different experience for financially secure and well-educated people than for poor and uneducated people. The early mission of behavioral science research focused on identifying problems of older adults, such as isolation, caregiving, and dementia. Today, the field of gerontology is more interdisciplinary. When I'm 64 examines how individual and social behavior play a role in understanding diverse outcomes in old age. It also explores the implications of an aging workforce on the economy. The book recommends that the National Institute on Aging focus its research support in social, personality, and life-span psychology in four areas: motivation and behavioral change; socioemotional influences on decision-making; the influence of social engagement on cognition; and the effects of stereotypes on self and others. When I'm 64 is a useful resource for policymakers, researchers and medical professionals.
A new edition of the essential resource on using functional neuroimaging techniques to study the neural basis of cognition, revised with the student in mind; thoroughly updated, with new chapters on fMRI physics, skill learning, emotion and social cognition, and other topics. This essential resource on neuroimaging provides an accessible and user-friendly introduction to the field written by leading researchers. The book describes theoretical and methodological developments in the use of functional neuroimaging techniques to study the neural basis of cognition, from early scientific efforts to link brain and behavior to the latest applications of fMRI and PET methods. The core of the book covers fMRI and PET studies in specific domains: attention, skill learning, semantic memory, language, episodic memory, working memory, and executive functions. By introducing a technique within the description of a domain, the book offers a clear explanation of the process while highlighting its biological context. The emphasis on readability makes Handbook of Functional Neuroimaging of Cognition ideal for classroom use in advanced undergraduate and graduate courses in cognitive neuroscience. This second edition has been completely updated to reflect new developments in the field, with existing chapters rewritten and new chapters added to each section. The section on history and methods now includes a chapter on the crucial topic of the physics of functional neuroimaging; the chapters on skill learning and executive functions are new to the domain section; and chapters on childhood development and emotion and social cognition have been added to the section on developmental, social, and clinical applications. The color insert has been increased in size, enhancing the visual display of representative findings. Contributors: Todd S. Braver, Jeffrey Browndyke, Roberto Cabeza, B.J. Casey, Jody Culham, Clayton E. Curtis, Mark D'Esposito, Sander Daselaar, Lila Davachi, Ian Dobbins, Karl J. Friston, Barry Giesbrecht, Todd C. Handy, Joseph B. Hopfinger, Scott A. Huettel, Irene P. Kan, Alan Kingstone, Eleni Kotsoni, Kevin S. LaBar, George R. Mangun, Gregory McCarthy, Uta Noppeney, Robyn T. Oliver, Elizabeth A. Phelps, Russel A. Poldrack, Cathy J. Price, Marcus E. Raichle, Hannes Ruge, Gaia Scerif, Allen W. Song, Sharon L. Thompson-Schill, Daniel T. Willingham, Richard J.S. Wise
Brain imaging has been successfully used to generate image-based biomarkers for various neurological and psychiatric disorders, such as Alzheimer’s and related dementias, Parkinson’s disease, stroke, traumatic brain injury, brain tumors, depression, schizophrenia, etc. However, accurate brain image-based diagnosis at the individual level remains elusive, and this applies to the diagnosis of neuropathological diseases as well as clinical syndromes. In recent years, deep learning techniques, due to their ability to learn complex patterns from large amounts of data, have had remarkable success in various fields, such as computer vision and natural language processing. Applying deep learning methods to brain imaging-assisted diagnosis, while promising, is facing challenges such as insufficiently labeled data, difficulty in interpreting diagnosis results, variations in data acquisition in multi-site projects, integration of multimodal data, clinical heterogeneity, etc. The goal of this research topic is to gather cutting-edge research that showcases the application of deep learning methods in brain imaging for the diagnosis of neurological and psychiatric disorders. We encourage submissions that demonstrate novel approaches to overcome various abovementioned difficulties and achieve more accurate, reliable, generalizable, and interpretable diagnosis of neurological and psychiatric disorders in this field.
An authoritative reference giving a systematic overview of new electrical imaging methods. Provides a comprehensive and sound introduction to the basics of multichannel recording of EEG and event-related potential (ERP) data, as well as spatio-temporal analysis of the potential fields. Chapters include practical examples of illustrative studies and approaches.
