This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science.
Rezensionen / Stimmen
"Handbook of Neuroimaging Data Analysis is a great source to help you get started . . . If you find a particular modality that interests you, just email one of the authors in the book who also works on data analysis within that modality. They are all friendly and helpful, and they will point you to sources of publically available data."
~Timothy D. Johnson
"These chapters are primarily written by statisticians, but the book is nicely balanced by contributions from biomedical engineers, psychologists, and cognitive scientists. . . I recommend this book to statisticians interested in learning about neuroimaging and contributing to its growth."
~Journal of the American Statistical Association
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Researchers who are developing novel statistical approaches to model brain data in areas such as statistics, biostatistics, computer science, cognitive science, psychology, computer engineering, biomedical engineering, applied mathematics, physics, and radiology; advanced masters and Ph.D. students in a special topics course on statistical methods for brain data.
Illustrationen
172 s/w Abbildungen
172 Illustrations, black and white
Maße
Höhe: 246 mm
Breite: 189 mm
Dicke: 38 mm
Gewicht
ISBN-13
978-0-367-33069-9 (9780367330699)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Hernando Ombao is Professor in the Department of Statistics at the University of California, Irvine and Fellow of the American Statistical Association. Martin Lindquist is Professor in the Department of Biostatistics at Johns Hopkins University and Fellow of the American Statistical Association. Wesley Thompson is Associate Professor in the Department of Psychiatry at the University of California, San Diego and Lead Scientist at the Institute of Biological Psychiatry, Mental Health Services, Copenhagen, Denmark. John Aston is Professor in the Statistical Laboratory at the University of Cambridge and Fellow of the American Statistical Association.
Herausgeber*in
Johns Hopkins University, Baltimore, Maryland, USA
Primary affiliation: Institute of Biological Psychiatry, Denmark; Secondary affiliation: University of California, San Diego, La Jolla, USA
University of Cambridge, UK
Overview. Imaging Modalities. Statistical Methods and Models.
