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One of the most intriguing questions facing modern science is the inner workings of the human brain. Functional magnetic resonance imaging (fMRI) is a powerful tool used to study the human brain in action. The data produced from mapping the active processes within the brain present many challenges to statisticians, computer scientists, engineers and other data analysts, due to their complex structure and the ever-increasing sophistication of the scientific questions being posed by researchers. This book represents the first in-depth discussion of statistical methodology, which it couples with an introduction to the scientific background needed to understand the data. Starting from the basic science - where fMRI data come from, why they are so complicated, and the role statistics can play in designing and interpreting experiments - the book gives a detailed survey of the numerous methods that have been applied in the last fifteen years. The analysis of fMRI data features many of the major issues of concern in modern statistics, such as high dimensionality, multiple testing, and visualization. The array of techniques examined in the book ranges from the simple two-sample t-test and the general linear model to hierarchical spatiotemporal models, multivariate methods such as principal components analysis, and Bayesian approaches as they have been used in fMRI. Software, including descriptions of the most popular freeware packages and their capabilities, is also discussed. This book offers researchers who are interested in the analysis of fMRI data a detailed discussion from a statistical perspective that covers the entire process from data collection to the graphical presentation of results. The book is a valuable resource for statisticians who want to learn more about this growing field, and for neuroscientists who want to learn more about how their data can be analyzed. Nicole A. Lazar is Professor of Statistics at the University of Georgia and affiliated faculty of the Center for Health Statistics, University of Illinois at Chicago. She is a prominent researcher in this area, a contributor to the FIASCO software for fMRI data analysis, and heads an fMRI statistics research group at the University of Georgia.

Brain - Magnetic resonance imaging - Statistics. --- Brain - physiology. --- Magnetic Resonance Imaging - methods. --- Magnetic Resonance Imaging - statistics & numerical data. --- Magnetic resonance imaging - Statistics. --- Models, Statistical. --- Statistics as Topic. --- Brain --- Magnetic resonance imaging --- Statistics as Topic --- Magnetic Resonance Imaging --- Models, Statistical --- Methods --- Physiology --- Epidemiologic Methods --- Tomography --- Models, Theoretical --- Investigative Techniques --- Health Care Evaluation Mechanisms --- Biological Science Disciplines --- Central Nervous System --- Mathematics --- Diagnostic Imaging --- Quality of Health Care --- Nervous System --- Diagnostic Techniques and Procedures --- Natural Science Disciplines --- Analytical, Diagnostic and Therapeutic Techniques and Equipment --- Public Health --- Environment and Public Health --- Anatomy --- Diagnosis --- Disciplines and Occupations --- Health Care Quality, Access, and Evaluation --- Health Care --- Neurology --- Medicine --- Health & Biological Sciences --- Statistics --- 519.2 --- Cerebrum --- Mind --- Central nervous system --- Head --- Clinical magnetic resonance imaging --- Diagnostic magnetic resonance imaging --- Functional magnetic resonance imaging --- Imaging, Magnetic resonance --- Medical magnetic resonance imaging --- MR imaging --- MRI (Magnetic resonance imaging) --- NMR imaging --- Nuclear magnetic resonance --- Nuclear magnetic resonance imaging --- Cross-sectional imaging --- Diagnostic imaging --- Statistics. --- physiology. --- methods. --- statistics & numerical data. --- Probability. Mathematical statistics --- Diagnostic use --- 519.2 Probability. Mathematical statistics --- Medicine. --- Neurosciences. --- Radiology. --- Bioinformatics. --- Psychology --- Psychological measurement. --- Medicine & Public Health. --- Imaging / Radiology. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Signal, Image and Speech Processing. --- Psychological Methods/Evaluation. --- Methodology. --- Magnetic resonance imaging&delete& --- Radiology, Medical. --- Psychological tests and testing. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Neural sciences --- Neurological sciences --- Neuroscience --- Medical sciences --- Nervous system --- Clinical radiology --- Radiology, Medical --- Radiology (Medicine) --- Medical physics --- Data processing --- Statistics . --- Signal processing. --- Image processing. --- Speech processing systems. --- Psychology—Methodology. --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Radiological physics --- Physics --- Radiation --- Measurement --- Scaling --- Methodology

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

One of the most intriguing questions facing modern science is the inner workings of the human brain. Functional magnetic resonance imaging (fMRI) is a powerful tool used to study the human brain in action. The data produced from mapping the active processes within the brain present many challenges to statisticians, computer scientists, engineers and other data analysts, due to their complex structure and the ever-increasing sophistication of the scientific questions being posed by researchers. This book represents the first in-depth discussion of statistical methodology, which it couples with an introduction to the scientific background needed to understand the data. Starting from the basic science - where fMRI data come from, why they are so complicated, and the role statistics can play in designing and interpreting experiments - the book gives a detailed survey of the numerous methods that have been applied in the last fifteen years. The analysis of fMRI data features many of the major issues of concern in modern statistics, such as high dimensionality, multiple testing, and visualization. The array of techniques examined in the book ranges from the simple two-sample t-test and the general linear model to hierarchical spatiotemporal models, multivariate methods such as principal components analysis, and Bayesian approaches as they have been used in fMRI. Software, including descriptions of the most popular freeware packages and their capabilities, is also discussed. This book offers researchers who are interested in the analysis of fMRI data a detailed discussion from a statistical perspective that covers the entire process from data collection to the graphical presentation of results. The book is a valuable resource for statisticians who want to learn more about this growing field, and for neuroscientists who want to learn more about how their data can be analyzed. Nicole A. Lazar is Professor of Statistics at the University of Georgia and affiliated faculty of the Center for Health Statistics, University of Illinois at Chicago. She is a prominent researcher in this area, a contributor to the FIASCO software for fMRI data analysis, and heads an fMRI statistics research group at the University of Georgia.

MRI (magnetic resonance imaging)