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Statistical Analysis of fMRI Data second edition [Kõva köide]

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(University of California, Santa Barbara)
  • Formaat: Hardback, 568 pages, kõrgus x laius x paksus: 229x178x30 mm, 106 b&w illus.; 212 Illustrations
  • Sari: The MIT Press
  • Ilmumisaeg: 17-Sep-2019
  • Kirjastus: MIT Press
  • ISBN-10: 0262042681
  • ISBN-13: 9780262042680
Teised raamatud teemal:
Statistical Analysis of fMRI Data second editionSuurem pilt
  • Formaat: Hardback, 568 pages, kõrgus x laius x paksus: 229x178x30 mm, 106 b&w illus.; 212 Illustrations
  • Sari: The MIT Press
  • Ilmumisaeg: 17-Sep-2019
  • Kirjastus: MIT Press
  • ISBN-10: 0262042681
  • ISBN-13: 9780262042680
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780262042680.html
Märksõnad:

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.



A guide to all aspects of experimental design and data analysis for fMRI experiments, completely revised and updated for the second edition.
Preface to the Second Edition xiii
Preface to the First Edition xv
List of Acronyms xix
1 Introduction 1(10)
1.1 What Is fMRI?
4(1)
1.2 The Scanning Session
5(2)
1.3 Data Analysis
7(1)
1.4 Software Packages
8(3)
2 Data Formats 11(6)
2.1 Some Commonly Used Data Formats
12(3)
2.1.1 DICOM
12(1)
2.1.2 Analyze
13(1)
2.1.3 NIfTI
14(1)
2.1.4 MINC
14(1)
2.1.5 BIDS
14(1)
2.2 Converting from One Format to Another
15(1)
2.3 Reading fMRI Data into MATLAB
15(2)
3 Modeling the BOLD Response 17(28)
3.1 Linear Models of the BOLD Response
17(6)
3.2 Methods of Estimating the hrf
23(15)
3.2.1 Input an Impulse, and Observe the Response
23(1)
3.2.2 Open the Box; Study the Circuit
24(1)
3.2.3 Take a Guess
24(2)
3.2.4 Select a Flexible Mathematical Model of the hrf
26(9)
3.2.5 Deconvolution
35(3)
3.3 Nonlinear Models of the BOLD Response
38(4)
3.4 Conclusions
42(3)
4 Experimental Designs 45(18)
4.1 Organizing and Presenting Stimulus Events
45(8)
4.1.1 Block Designs
45(4)
4.1.2 Slow Event-Related Designs
49(1)
4.1.3 Rapid Event-Related Designs
50(1)
4.1.4 Free-Behavior Designs
51(1)
4.1.5 Resting-State fMRI
52(1)
4.2 Choosing the Right Experimental Conditions
53(8)
4.2.1 The Method of Subtraction
53(2)
4.2.2 Conjunction Analysis Designs
55(2)
4.2.3 Factorial Designs and the Additive Factor Method
57(2)
4.2.4 Parametric Designs
59(1)
4.2.5 Repetition Suppression Designs
60(1)
4.3 Conclusions
61(2)
5 Preprocessing 63(42)
5.1 Slice-Timing Correction
64(8)
5.1.1 Slice-Timing Correction during Preprocessing
65(6)
5.1.2 Slice-Timing Correction during Task-Related Statistical Analysis
71(1)
5.2 Head Motion Correction
72(11)
5.2.1 Correcting for Motion-Induced Location Changes
73(7)
5.2.2 Motion-Induced Changes in the BOLD Response
80(3)
5.3 Coregistering the Functional and Structural Data
83(5)
5.4 Normalization
88(4)
5.4.1 Brain Atlases
88(1)
5.4.2 The Spatial Normalization Process
89(3)
5.5 Spatial Smoothing
92(5)
5.6 Temporal Filtering
97(5)
5.7 Other Preprocessing Steps
102(2)
5.7.1 Quality Assurance
102(1)
5.7.2 Distortion Correction
102(1)
5.7.3 Grand Mean Scaling
103(1)
5.8 Conclusions
104(1)
6 The General Linear Model 105(50)
6.1 The Correlation Approach
106(5)
6.2 Collinearity
111(4)
6.3 Accounting for Nuisance Effects
115(2)
6.4 The FBR Method
117(5)
6.5 Microlinearity versus Macrolinearity
122(1)
6.6 Block Designs
123(2)
6.7 A Graphical Convention for Displaying the Design Matrix
125(1)
6.8 An Introduction to the General Linear Model
126(4)
6.9 Parameter Estimation in the Correlation and FBR Models
130(2)
6.10 Testing a Hypothesis by Constructing Statistical Parametric Maps
132(10)
6.10.1 Tests of One Linear Hypothesis
132(7)
6.10.2 Tests of Multiple Linear Hypotheses
139(1)
6.10.3 Testing a Nonlinear Hypothesis
140(2)
6.11 The Multivariate GLM
142(3)
6.12 Nonparametric Approaches to Hypothesis Testing
145(1)
6.12.1 Algorithm for Hypothesis Testing with a Permutation Test
145(1)
6.13 Percent Signal Change
146(3)
6.14 Comparing the Correlation and FBR Methods
149(2)
6.15 Derivations of Propositions 6.1-6.3
151(4)
6.15.1 Proposition 6.1
151(1)
6.15.2 Proposition 6.2
152(1)
6.15.3 Proposition 6.3
153(2)
7 The Multiple Comparisons Problem 155(36)
7.1 The Sidak and Bonferroni Corrections
156(2)
7.2 Using Gaussian Random Fields (GRFs) to Make Single-Voxel Corrections
158(8)
7.3 Using GRFs to Correct at the Cluster Level
166(8)
7.3.1 Cluster-Based Methods Using a Spatial Extent Criterion
170(1)
7.3.2 Cluster-Based Methods Using a Criterion That Depends on Cluster Height and Spatial Extent
171(3)
7.4 Permutation-Based Solutions to the Multiple Comparisons Problem
174(2)
7.4.1 Permutation-Based Algorithm for Finding the Threshold T That Leads to an Experiment-Wise Error Rate of αE When Decisions Are Made at the Single-Voxel Level
175(1)
7.4.2 Permutation-Based Algorithm for Finding the Threshold S on Cluster Size That Leads to an Experiment-Wise Error Rate of αE When Cluster-Based Decisions Are Made
175(1)
7.5 Comparing the Various Methods
176(2)
7.6 False Discovery Rate
178(4)
7.6.1 Benjamini and Hochberg (1995) Algorithm for Ensuring That FDR lesser or equal q
179(3)
7.7 Voodoo Correlations
182(1)
7.8 Conclusions
183(1)
7.9 Derivations
183(8)
7.9.1 Proposition 7.1
183(1)
7.9.2 Proposition 7.2
184(1)
7.9.3 Proposition 7.3
185(1)
7.9.4 Proposition 7.4
185(1)
7.9.5 Worsley et al. (1996) Algorithm for Computing Resel Counts (i.e., Rd)
186(2)
7.9.6 Why the FDR Algorithm Works
188(3)
8 Group Analyses 191(30)
8.1 Individual Differences
191(3)
8.2 Fixed versus Random Factors in the General Linear Model
194(2)
8.3 A Fixed-Effects Group Analysis
196(5)
8.4 A Random-Effects Group Analysis
201(2)
8.5 Comparing Fixed-Effects and Random-Effects Analyses
203(2)
8.6 Multiple-Factor Experiments
205(3)
8.7 Power Analysis
208(5)
8.8 Meta-Analysis
213(5)
8.9 Derivations
218(3)
8.9.1 Proposition 8.1
218(1)
8.9.2 Proposition 8.2
219(2)
9 Functional Connectivity Analysis via Psychophysiological Interactions and Beta-Series Regression 221(22)
9.1 The Method of Psychophysiological Interactions (PPI)
224(9)
9.1.1 Selecting a Seed
224(1)
9.1.2 PPI in Block Designs
225(6)
9.1.3 PPI in Rapid Event-Related Designs
231(2)
9.2 Beta-Series Regression
233(8)
9.3 Conclusions
241(2)
10 Functional Connectivity Analysis via Granger Causality 243(26)
10.1 Quantitative Measures of Causality
250(3)
10.2 Parameter Estimation
253(4)
10.3 Inference
257(1)
10.4 Conditional Granger Causality
258(7)
10.5 Theoretical Extensions
265(1)
10.6 Validity
266(2)
10.6.1 Is the Temporal Resolution of fMRI Good Enough for Granger Causality?
267(1)
10.6.2 Do Interregional Timing Differences in the hrf Invalidate Granger Causality?
267(1)
10.7 Software Packages
268(1)
10.8 Derivation of Proposition 10.1
268(1)
11 Assessing Functional Connectivity via Coherence Analysis 269(34)
11.1 Autocorrelation and Cross-Correlation
269(5)
11.2 Power Spectrum and Cross-Power Spectrum
274(4)
11.3 Coherence
278(12)
11.3.1 Coherence in Rapid versus Slow Event-Related Designs
284(4)
11.3.2 An Empirical Application
288(2)
11.3.3 Hypothesis Testing
290(1)
11.4 Partial Coherence
290(3)
11.5 Using the Phase Spectrum to Determine Causality
293(6)
11.6 Conclusions
299(1)
11.7 Derivations
300(3)
11.7.1 Proposition 11.1
300(1)
11.7.2 Proposition 11.2
300(3)
12 Principal Component Analysis 303(16)
12.1 Principal Component Analysis
304(3)
12.2 PCA with fMRI Data
307(2)
12.3 Using PCA to Eliminate Noise
309(6)
12.3.1 Algorithm for Eliminating Noise from fMRI Data
311(4)
12.4 Singular-Value Decomposition
315(3)
12.5 Conclusions
318(1)
13 Independent Component Analysis 319(34)
13.1 The Cocktail-Party Problem
320(1)
13.2 Applying ICA to fMRI Data
320(8)
13.2.1 Spatial ICA
322(2)
13.2.2 Assessing Statistical Independence
324(1)
13.2.3 The Importance of Nonnormality in ICA
325(1)
13.2.4 Preparing Data for ICA
326(2)
13.3 ICA Algorithms
328(8)
13.3.1 Minimizing Mutual Information
328(2)
13.3.2 Methods That Maximize Nonnormality
330(2)
13.3.3 Maximum Likelihood Approaches
332(1)
13.3.4 Infomax
333(3)
13.4 Interpreting ICA Results
336(4)
13.4.1 Determining the Relative Importance of Each Component
336(1)
13.4.2 Assigning Meaning to Components
337(3)
13.5 The Noisy ICA Model
340(5)
13.6 Other Issues
345(1)
13.7 Group ICA
346(1)
13.8 Comparing ICA and GLM Approaches
347(2)
13.9 Conclusions
349(1)
13.10 Derivations
350(3)
13.10.1 Why Whitening Reduces the Number of Free Parameters in the ICA Model
350(1)
13.10.2 The Infomax Learning Algorithm
351(2)
14 Decoding via Multivoxel Pattern Analysis 353(36)
14.1 General Overview of MVPA
353(2)
14.2 Determining the Search Region and the Curse of Dimensionality
355(5)
14.3 Creating the Activity Vectors
360(4)
14.4 Preprocessing for MVPA
364(2)
14.5 Building a Classifier
366(7)
14.5.1 Fisher Linear Discriminant Analysis
370(1)
14.5.2 Support Vector Machines
371(2)
14.6 Validation
373(3)
14.7 Statistical Inference
376(3)
14.7.1 Individual-Subject Analysis
376(1)
14.7.2 Group-Level Inference
377(2)
14.8 Feature Selection
379(1)
14.9 MVPA Software
380(1)
14.10 Conclusions
381(1)
14.11 Description of the SVM Algorithm That Maximizes the Margin
382(7)
14.11.1 Linear SVMs
382(4)
14.11.2 Nonlinear SVMs
386(3)
15 Encoding Models 389(18)
15.1 Voxel-Based Encoding Models
390(7)
15.2 Inverting an Encoding Model to Produce a Decoding Scheme
397(3)
15.3 Model-Based fMRI
400(3)
15.4 Computational Cognitive Neuroscience
403(2)
15.5 Conclusions
405(2)
16 Dynamic Causal Modeling 407(46)
16.1 Linear Dynamical Models of Neural Activation
408(4)
16.2 Bilinear Dynamical Models of Neural Activation
412(7)
16.3 Generalizations of the Bilinear Model
419(3)
16.3.1 Quadratic DCM
419(1)
16.3.2 Two-State DCM
420(1)
16.3.3 Stochastic DCM
421(1)
16.4 The Hemodynamic Model
422(1)
16.5 Parameter Estimation
423(6)
16.6 Model Selection
429(13)
16.6.1 Model Selection by Minimizing BIC
438(1)
16.6.2 Model Selection by Maximizing Negative Free Energy
439(3)
16.7 Group Analysis
442(3)
16.7.1 Fixed-Effects DCM Analyses
442(1)
16.7.2 Random-Effects DCM Analyses
443(2)
16.8 Conclusions
445(1)
16.9 Derivation of Negative Free Energy
446(7)
17 Representational Similarity Analysis 453(26)
17.1 Extracting an RDM from the BOLD Data
455(7)
17.1.1 Selecting the ROI
455(1)
17.1.2 Estimating Activity Vectors
456(1)
17.1.3 Computing Dissimilarity between Activity Vectors
457(5)
17.2 Building a Geometric Model of the Similarity Structure
462(5)
17.3 Perceived Similarity in Humans
467(3)
17.4 Group-Level Inference with RSA
470(5)
17.5 Encoding and Decoding Using Representational Similarity
475(2)
17.6 RSA Software
477(1)
17.7 Conclusions
477(2)
Appendix A. Matrix Algebra 479(20)
A.1 Matrices and Their Basic Operations
479(7)
A.2 Rank
486(2)
A.3 Solving Linear Equations
488(4)
A.4 Eigenvalues and Eigenvectors
492(7)
A.4.1 Definitions
492(3)
A.4.2 Properties
495(4)
Appendix B. Multivariate Probability Distributions 499(6)
B.1 Introduction
499(1)
B.2 Multivariate Normal Distributions
500(5)
Appendix C. Building a Design Matrix for Group Analysis 505(8)
C.1 Effect Coding
505(4)
C.2 Statistical Testing
509(4)
Notes 513(8)
References 521(18)
Index 539