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Multivariate Analysis for Neuroimaging Data

Author: Atsushi Kawaguchi

Publisher: CRC Press

Published: 2021-07-01

Total Pages: 214

ISBN-13: 1000369870

This book describes methods for statistical brain imaging data analysis from both the perspective of methodology and from the standpoint of application for software implementation in neuroscience research. These include those both commonly used (traditional established) and state of the art methods. The former is easier to do due to the availability of appropriate software. To understand the methods it is necessary to have some mathematical knowledge which is explained in the book with the help of figures and descriptions of the theory behind the software. In addition, the book includes numerical examples to guide readers on the working of existing popular software. The use of mathematics is reduced and simplified for non-experts using established methods, which also helps in avoiding mistakes in application and interpretation. Finally, the book enables the reader to understand and conceptualize the overall flow of brain imaging data analysis, particularly for statisticians and data-scientists unfamiliar with this area. The state of the art method described in the book has a multivariate approach developed by the authors’ team. Since brain imaging data, generally, has a highly correlated and complex structure with large amounts of data, categorized into big data, the multivariate approach can be used as dimension reduction by following the application of statistical methods. The R package for most of the methods described is provided in the book. Understanding the background theory is helpful in implementing the software for original and creative applications and for an unbiased interpretation of the output. The book also explains new methods in a conceptual manner. These methodologies and packages are commonly applied in life science data analysis. Advanced methods to obtain novel insights are introduced, thereby encouraging the development of new methods and applications for research into medicine as a neuroscience.

Multivariate Analysis for Neuroimaging Data

Author: Atsushi Kawaguchi

Publisher: CRC Press

Published: 2023-07

Total Pages: 0

ISBN-13: 9780367752217

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This book enables us to analyze statistically brain imaging data. It is meant for a wide range of researchers interested in biostatistics, data science, and neuroscience. It is useful to understand the background theory of standard software for neuroimaging data analysis.

Handbook of Neuroimaging Data Analysis

Author: Hernando Ombao

Publisher: CRC Press

Published: 2016-11-18

Total Pages: 702

ISBN-13: 1482220989

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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.

Multivariate Analysis for the Biobehavioral and Social Sciences

Author: Bruce L. Brown

Publisher: John Wiley & Sons

Published: 2011-11-01

Total Pages: 404

ISBN-13: 1118131614

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An insightful guide to understanding and visualizing multivariate statistics using SAS®, STATA®, and SPSS® Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach outlines the essential multivariate methods for understanding data in the social and biobehavioral sciences. Using real-world data and the latest software applications, the book addresses the topic in a comprehensible and hands-on manner, making complex mathematical concepts accessible to readers. The authors promote the importance of clear, well-designed graphics in the scientific process, with visual representations accompanying the presented classical multivariate statistical methods . The book begins with a preparatory review of univariate statistical methods recast in matrix notation, followed by an accessible introduction to matrix algebra. Subsequent chapters explore fundamental multivariate methods and related key concepts, including: Factor analysis and related methods Multivariate graphics Canonical correlation Hotelling's T-squared Multivariate analysis of variance (MANOVA) Multiple regression and the general linear model (GLM) Each topic is introduced with a research-publication case study that demonstrates its real-world value. Next, the question "how do you do that?" is addressed with a complete, yet simplified, demonstration of the mathematics and concepts of the method. Finally, the authors show how the analysis of the data is performed using Stata®, SAS®, and SPSS®. The discussed approaches are also applicable to a wide variety of modern extensions of multivariate methods as well as modern univariate regression methods. Chapters conclude with conceptual questions about the meaning of each method; computational questions that test the reader's ability to carry out the procedures on simple datasets; and data analysis questions for the use of the discussed software packages. Multivariate Analysis for the Biobehavioral and Social Sciences is an excellent book for behavioral, health, and social science courses on multivariate statistics at the graduate level. The book also serves as a valuable reference for professionals and researchers in the social, behavioral, and health sciences who would like to learn more about multivariate analysis and its relevant applications.

Statistical Parametric Mapping: The Analysis of Functional Brain Images

Author: William D. Penny

Publisher: Elsevier

Published: 2011-04-28

Total Pages: 656

ISBN-13: 0080466508

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In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. An essential reference and companion for users of the SPM software Provides a complete description of the concepts and procedures entailed by the analysis of brain images Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data Stands as a compendium of all the advances in neuroimaging data analysis over the past decade Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes Structured treatment of data analysis issues that links different modalities and models Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible

The Statistical Analysis of Functional MRI Data

Author: Nicole Lazar

Publisher: Springer Science & Business Media

Published: 2008-06-10

Total Pages: 299

ISBN-13: 0387781919

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The study of brain function is one of the most fascinating pursuits of m- ern science. Functional neuroimaging is an important component of much of the current research in cognitive, clinical, and social psychology. The exci- ment of studying the brain is recognized in both the popular press and the scienti?c community. In the pages of mainstream publications, including The New York Times and Wired, readers can learn about cutting-edge research into topics such as understanding how customers react to products and - vertisements (“If your brain has a ‘buy button,’ what pushes it?”, The New York Times,October19,2004),howviewersrespondtocampaignads(“Using M. R. I. ’s to see politics on the brain,” The New York Times, April 20, 2004; “This is your brain on Hillary: Political neuroscience hits new low,” Wired, November 12,2007),howmen and womenreactto sexualstimulation (“Brain scans arouse researchers,”Wired, April 19, 2004), distinguishing lies from the truth (“Duped,” The New Yorker, July 2, 2007; “Woman convicted of child abuse hopes fMRI can prove her innocence,” Wired, November 5, 2007), and even what separates “cool” people from “nerds” (“If you secretly like Michael Bolton, we’ll know,” Wired, October 2004). Reports on pathologies such as autism, in which neuroimaging plays a large role, are also common (for - stance, a Time magazine cover story from May 6, 2002, entitled “Inside the world of autism”).

Statistical Analysis of fMRI Data, second edition

Author: F. Gregory Ashby

Publisher: MIT Press

Published: 2019-09-17

Total Pages: 569

ISBN-13: 0262042681

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A guide to all aspects of experimental design and data analysis for fMRI experiments, completely revised and updated for the second edition. Functional magnetic resonance imaging (fMRI), which allows researchers to observe neural activity in the human brain noninvasively, has revolutionized the scientific study of the mind. An fMRI experiment produces massive amounts of highly complex data for researchers to analyze. This book describes all aspects of experimental design and data analysis for fMRI experiments, covering every step—from preprocessing to advanced methods for assessing functional connectivity—as well as the most popular multivariate approaches. The goal is not to describe which buttons to push in the popular software packages but to help researchers understand the basic underlying logic, the assumptions, the strengths and weaknesses, and the appropriateness of each method. The field of fMRI research has advanced dramatically in recent years, in both methodology and technology, and this second edition has been completely revised and updated. Six new chapters cover experimental design, functional connectivity analysis through the methods of psychophysiological interactions and beta-series regression, decoding using multi-voxel pattern analysis, dynamic causal modeling, and representational similarity analysis. Other chapters offer new material on recently discovered problems related to head movements, the multivariate GLM, meta-analysis, and other topics. All complex derivations now appear at the end of the relevant chapter to improve readability. A new appendix describes how to build a design matrix with effect coding for group analysis. As in the first edition, MATLAB code is provided with which readers can implement many of the methods described.

Handbook of Functional MRI Data Analysis

Author: Russell A. Poldrack

Publisher: Cambridge University Press

Published: 2024-02-08

Total Pages: 0

ISBN-13: 9781009481168

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Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook for Functional MRI Data Analysis provides a comprehensive and practical introduction to the methods used for fMRI data analysis. Using minimal jargon, this book explains the concepts behind processing fMRI data, focusing on the techniques that are most commonly used in the field. This book provides background about the methods employed by common data analysis packages including FSL, SPM, and AFNI. Some of the newest cutting-edge techniques, including pattern classification analysis, connectivity modeling, and resting state network analysis, are also discussed. Readers of this book, whether newcomers to the field or experienced researchers, will obtain a deep and effective knowledge of how to employ fMRI analysis to ask scientific questions and become more sophisticated users of fMRI analysis software.

Statistical Analysis of fMRI Data, second edition

Author: F. Gregory Ashby

Publisher: MIT Press

Published: 2019-09-17

Total Pages: 569

ISBN-13: 0262354063

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Statistical Analysis of fMRI Data

Author: F. Gregory Ashby

Publisher: MIT Press

Published: 2011-03-11

Total Pages: 349

ISBN-13: 0262294419

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An overview of statistical methods for analyzing data from fMRI experiments. Functional magnetic resonance imaging (fMRI), which allows researchers to observe neural activity in the human brain noninvasively, has revolutionized the scientific study of the mind. An fMRI experiment produces massive amounts of highly complex data; researchers face significant challenges in analyzing the data they collect. This book offers an overview of the most widely used statistical methods of analyzing fMRI data. Every step is covered, from preprocessing to advanced methods for assessing functional connectivity. The goal is not to describe which buttons to push in the popular software packages but to help readers understand the basic underlying logic, the assumptions, the strengths and weaknesses, and the appropriateness of each method. The book covers all of the important current topics in fMRI data analysis, including the relation of the fMRI BOLD (blood oxygen-level dependent) response to neural activation; basic analyses done in virtually every fMRI article—preprocessing, constructing statistical parametrical maps using the general linear model, solving the multiple comparison problem, and group analyses; the most popular methods for assessing functional connectivity—coherence analysis and Granger causality; two widely used multivariate approaches, principal components analysis and independent component analysis; and a brief survey of other current fMRI methods. The necessary mathematics is explained at a conceptual level, but in enough detail to allow mathematically sophisticated readers to gain more than a purely conceptual understanding. The book also includes short examples of Matlab code that implement many of the methods described; an appendix offers an introduction to basic Matlab matrix algebra commands (as well as a tutorial on matrix algebra). A second appendix introduces multivariate probability distributions.