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Handbook of Functional MRI Data Analysis [Kõva köide]

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(University of Oxford), (Stanford University, California), (Stanford University, California)
  • Formaat: Hardback, 238 pages, kõrgus x laius x paksus: 261x184x19 mm, kaal: 650 g, 5 Tables, unspecified; 51 Plates, unspecified; 51 Plates, color; 18 Halftones, unspecified; 18 Halftones, black and white; 27 Line drawings, black and white
  • Ilmumisaeg: 22-Aug-2011
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521517664
  • ISBN-13: 9780521517669
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Handbook of Functional MRI Data AnalysisSuurem pilt
  • Formaat: Hardback, 238 pages, kõrgus x laius x paksus: 261x184x19 mm, kaal: 650 g, 5 Tables, unspecified; 51 Plates, unspecified; 51 Plates, color; 18 Halftones, unspecified; 18 Halftones, black and white; 27 Line drawings, black and white
  • Ilmumisaeg: 22-Aug-2011
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521517664
  • ISBN-13: 9780521517669
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780521517669.html
Märksõnad:
"Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook of 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"--Provided by publisher.

Arvustused

'Wow! Very often in neuroimaging a title has little relationship to what follows. That is clearly not the case with the Handbook of Functional MRI Data Analysis by Poldrack, Mumford, and Nichols. This relatively slender volume is all that a handbook should be: it is crafted by true experts in the field, it is structured so that a newcomer can understand a method's strengths and weaknesses, but it also contains meaty information useful to experts. The book touches on all of the major analytical approaches current in the field and, while I don't agree with every choice the authors make, their advice is always well-conceived. This will be a standard reference on every neuroimager's shelf.' Steven Petersen, Washington University, St Louis 'This book, by some of the best in the field, will no doubt be the go-to book found in every imaging lab and recommendedfor all trainees. Poldrack, Mumford, and Nichols cover the most basic to sophisticated imaging analyses in a wonderfully accessible way.' B. J. Casey, Sackler Institute, Weill Cornell Medical College 'This is a great and timely book. The authors start with the basic concepts of fMRI and image analysis, develop the standard processings and statistical models, and finally explain in a simple and didactic style more advanced topics such as connectivity and machine learning techniques. This textbook provides a comprehensive, and yet very clear, introduction to all of the important aspects of fMRI data analysis. It is extremely readable, and I would strongly recommend anyone new to the field of neuroimaging to read this from cover to cover. Psychologists and medics will find it accessible, and not mathematically daunting, while engineers and other methods researchers will find the breadth of imaging-related issues a very valuable background.' Steve Smith, FMRIB Analysis Group, Oxford 'The book is a must in any research laboratory or clinical environment using fMRI, and it is the perfect reading for studnets or researchers, whether they want to develop fMRI data analysis methods or understand and apply these methods. I believe this book will be a best-seller in our field and a reference for many years because it ideally fills the gap between introductory and advanced research textbooks.' Jean-Baptiste Poline, Neurospin, Institut d'Imagerie Biomédicale, CEA, France

Muu info

Using minimal jargon, this book provides a comprehensive and practical introduction to the methods used for fMRI data analysis.
Preface page ix
1 Introduction
1(12)
1.1 A brief overview of fMRI
1(2)
1.2 The emergence of cognitive neuroscience
3(1)
1.3 A brief history of fMRI analysis
4(3)
1.4 Major components of fMRI analysis
7(1)
1.5 Software packages for fMRI analysis
7(3)
1.6 Choosing a software package
10(1)
1.7 Overview of processing streams
10(1)
1.8 Prerequisites for fMRI analysis
10(3)
2 Image processing basics
13(21)
2.1 What is an image?
13(2)
2.2 Coordinate systems
15(2)
2.3 Spatial transformations
17(14)
2.4 Filtering and Fourier analysis
31(3)
3 Preprocessing fMRI data
34(19)
3.1 Introduction
34(1)
3.2 An overview of fMRI preprocessing
34(1)
3.3 Quality control techniques
34(4)
3.4 Distortion correction
38(3)
3.5 Slice timing correction
41(2)
3.6 Motion correction
43(7)
3.7 Spatial smoothing
50(3)
4 Spatial normalization
53(17)
4.1 Introduction
53(1)
4.2 Anatomical variability
53(1)
4.3 Coordinate spaces for neuroimaging
54(1)
4.4 Atlases and templates
55(1)
4.5 Preprocessing of anatomical images
56(2)
4.6 Processing streams for fMRI normalization
58(2)
4.7 Spatial normalization methods
60(2)
4.8 Surface-based methods
62(1)
4.9 Choosing a spatial normalization method
63(2)
4.10 Quality control for spatial normalization
65(1)
4.11 Troubleshooting normalization problems
66(1)
4.12 Normalizing data from special populations
66(4)
5 Statistical modeling: Single subject analysis
70(30)
5.1 The BOLD signal
70(16)
5.2 The BOLD noise
86(6)
5.3 Study design and modeling strategies
92(8)
6 Statistical modeling: Group analysis
100(10)
6.1 The mixed effects model
100(5)
6.2 Mean centering continuous covariates
105(5)
7 Statistical inference on images
110(20)
7.1 Basics of statistical inference
110(2)
7.2 Features of interest in images
112(4)
7.3 The multiple testing problem and solutions
116(7)
7.4 Combining inferences: masking and conjunctions
123(3)
7.5 Use of region of interest masks
126(1)
7.6 Computing statistical power
126(4)
8 Modeling brain connectivity
130(30)
8.1 Introduction
130(1)
8.2 Functional connectivity
131(13)
8.3 Effective connectivity
144(11)
8.4 Network analysis and graph theory
155(5)
9 Multivoxel pattern analysis and machine learning
160(13)
9.1 Introduction to pattern classification
160(3)
9.2 Applying classifiers to fMRI data
163(1)
9.3 Data extraction
163(1)
9.4 Feature selection
164(1)
9.5 Training and testing the classifier
165(6)
9.6 Characterizing the classifier
171(2)
10 Visualizing, localizing, and reporting fMRI data
173(18)
10.1 Visualizing activation data
173(3)
10.2 Localizing activation
176(3)
10.3 Localizing and reporting activation
179(4)
10.4 Region of interest analysis
183(8)
Appendix A Review of the General Linear Model
191(10)
A.1 Estimating GLM parameters
191(3)
A.2 Hypothesis testing
194(1)
A.3 Correlation and heterogeneous variances
195(2)
A.4 Why "general" linear model?
197(4)
Appendix B Data organization and management
201(7)
B.1 Computing for fMRI analysis
201(1)
B.2 Data organization
202(2)
B.3 Project management
204(1)
B.4 Scripting for data analysis
205(3)
Appendix C Image formats
208(3)
C.1 Datastorage
208(1)
C.2 File formats
209(2)
Bibliography 211(14)
Index 225
Dr Russell A. Poldrack is the Director of the Imaging Research Center and Professor of Psychology and Neurobiology at the University of Texas, Austin. He has published more than 100 articles in the field of cognitive neuroscience, in journals including Science, Nature, Neuron, Nature Neuroscience and PNAS. He is well known for his writings on how neuroimaging can be used to make inferences about psychological function, as well as for his research using fMRI and other imaging techniques to understand the brain systems that support learning and memory, decision making and executive function. Dr Jeanette A. Mumford is a Research Assistant Professor in the Department of Psychology at the University of Texas, Austin. Trained in biostatistics, her research has focused on the development and characterisation of new methods for statistical modeling and analysis of fMRI data. Her work has examined the impact of different group modeling strategies and developed new tools for modeling network structure in resting-state fMRI data. She is the developer of the fmriPower software package, which provides power analysis tools for fMRI data. Dr Thomas E. Nichols is the Head of Neuroimaging Statistics at the University of Warwick, United Kingdom. He has been working in functional neuroimaging since 1992, when he joined the University of Pittsburgh's PET Facility as programmer and statistician. He is known for his work on inference in brain imaging, using both parametric and nonparametric methods, and he is an active contributor to the FSL and SPM software packages. In 2009 he received the Wiley Young Investigator Award from the Organization for Human Brain Mapping in recognition for his contributions to statistical modeling and inference of neuroimaging data.