Statistical analysis of fMRI data / F. Gregory Ashby.

Format:

Book

Author/Creator:

Ashby, F. Gregory, author.

Language:

English

Subjects (All):

Magnetic resonance imaging.

Brain mapping.

Physical Description:

1 online resource (568 pages)

Edition:

Second edition.

Place of Publication:

Cambridge : MIT Press, 2019.

System Details:

text file

Contents:

1.1 What Is fMRI? p. 4

1.2 The Scanning Session p. 5

1.3 Data Analysis p. 7

1.4 Software Packages p. 8

2 Data Formats p. 11

2.1 Some Commonly Used Data Formats p. 12

2.1.1 DICOM p. 12

2.1.2 Analyze p. 13

2.1.3 NifTI p. 14

2.1.4 MINC p. 14

2.1.5 BIDS p. 14

2.2 Converting from One Format to Another p. 15

2.3 Reading fMRI Data into MATLAB p. 15

3 Modeling the BOLD Response p. 17

3.1 Linear Models of the BOLD Response p. 17

3.2 Methods of Estimating the hrf p. 23

3.2.1 Input an Impulse, and Observe the Response p. 23

3.2.2 Open the Box: Study the Circuit p. 24

3.2.3 Take a Guess p. 24

3.2.4 Select a Flexible Mathematical Model of the hrf p. 26

3.2.5 Deconvolution p. 35

3.3 Nonlinear Models of the BOLD Response p. 38

4 Experimental Designs p. 45

4.1 Organizing and Presenting Stimulus Events p. 45

4.1.1 Block Designs p. 45

4.1.2 Slow Event-Related Designs p. 49

4.1.3 Rapid Event-Related Designs p. 50

4.1.4 Free-Behavior Designs p. 51

4.1.5 Resting-State fMRI p. 52

4.2 Choosing the Right Experimental Conditions p. 53

4.2.1 The Method of Subtraction p. 53

4.2.2 Conjunction Analysis Designs p. 55

4.2.3 Factorial Designs and the Additive Factor Method p. 57

4.2.4 Parametric Designs p. 59

4.2.5 Repetition Suppression Designs p. 60

5 Preprocessing p. 63

5.1 Slice-Timing Correction p. 64

5.1.1 Slice-Timing Correction during Preprocessing p. 65

5.1.2 Slice-Timing Correction during Task-Related Statistical Analysis p. 71

5.2 Head Motion Correction p. 72

5.2.1 Correcting for Motion-Induced Location Changes p. 73

5.2.2 Motion-Induced Changes in the BOLD Response p. 80

5.3 Coregistering the Functional and Structural Data p. 83

5.4 Normalization p. 88

5.4.1 Brain Atlases p. 88

5.4.2 The Spatial Normalization Process p. 89

5.5 Spatial Smoothing p. 92

5.6 Temporal Filtering p. 97

5.7 Other Preprocessing Steps p. 102

5.7.1 Quality Assurance p. 102

5.7.2 Distortion Correction p. 102

5.7.3 Grand Mean Scaling p. 103

6 The General Linear Model p. 105

6.1 The Correlation Approach p. 106

6.2 Collinearity p. 111

6.3 Accounting for Nuisance Effects p. 115

6.4 The FBR Method p. 117

6.5 Microlinearity versus Macrolinearity p. 122

6.6 Block Designs p. 123

6.7 A Graphical Convention for Displaying the Design Matrix p. 125

6.8 An Introduction to the General Linear Model p. 126

6.9 Parameter Estimation in the Correlation and FBR Models p. 130

6.10 Testing a Hypothesis by Constructing Statistical Parametric Maps p. 132

6.10.1 Tests of One Linear Hypothesis p. 132

6.10.2 Tests of Multiple Linear Hypotheses p. 139

6.10.3 Testing a Nonlinear Hypothesis p. 140

6.11 The Multivariate GLM p. 142

6.12 Nonparametric Approaches to Hypothesis Testing p. 145

6.12.1 Algorithm for Hypothesis Testing with a Permutation Test p. 145

6.13 Percent Signal Change p. 146

6.14 Comparing the Correlation and FBR Methods p. 149

6.15 Derivations of Propositions 6.1-6.3 p. 151

6.15.1 Proposition 6.1 p. 151

6.15.2 Proposition 6.2 p. 152

6.15.3 Proposition 6.3 p. 153

7 The Multiple Comparisons Problem p. 155

7.1 The Sidak and Bonferroni Corrections p. 156

7.2 Using Gaussian Random Fields (GRFs) to Make Single-Voxel Corrections p. 158

7.3 Using GRFs to Correct at the Cluster Level p. 166

7.3.1 Cluster-Based Methods Using a Spatial Extent Criterion p. 170

7.3.2 Cluster-Based Methods Using a Criterion That Depends on Cluster Height and Spatial Extent p. 171

7.4 Permutation-Based Solutions to the Multiple Comparisons Problem p. 174

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 p. 175

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 p. 175

7.5 Comparing the Various Methods p. 176

7.6 False Discovery Rate p. 178

7.6.1 Benjaminiand Hochberg (1995) Algorithm for Ensuring That FDR>q p. 179

7.7 Voodoo Correlations p. 182

7.9 Derivations p. 183

7.9.1 Proposition 7.1 p. 183

7.9.2 Proposition 7.2 p. 184

7.9.3 Proposition 7.3 p. 185

7.9.4 Proposition 7.4 p. 185

7.9.5 Worsley et al. (1996) Algorithm for Computing Resel Counts (i.e., Rd) p. 186

7.9.6 Why the FDR Algorithm Works p. 188

8 Group Analyses p. 191

8.1 Individual Differences p. 191

8.2 Fixed versus Random Factors in the General Linear Model p. 194

8.3 A Fixed-Effects Group Analysis p. 196

8.4 A Random-Effects Group Analysis p. 201

8.5 Comparing Fixed-Effects and Random-Effects Analyses p. 203

8.6 Multiple-Factor Experiments p. 205

8.7 Power Analysis p. 208

8.8 Meta-Analysis p. 213

8.9 Derivations p. 218

8.9.1 Proposition 8.1 p. 218

8.9.2 Proposition 8.2 p. 219

9 Functional Connectivity Analysis via Psychophysiological Interactions and Beta-Series Regression p. 221

9.1 The Method of Psychophysiological Interactions (PPI) p. 224

9.1.1 Selecting a Seed p. 224

9.1.2 PPI in Block Designs p. 225

9.1.3 PPI in Rapid Event-Related Designs p. 231

9.2 Beta-Series Regression p. 233

10 Functional Connectivity Analysis via Granger Causality p. 243

10.1 Quantitative Measures of Causality p. 250

10.2 Parameter Estimation p. 253

10.3 Inference p. 257

10.4 Conditional Granger Causality p. 258

10.5 Theoretical Extensions p. 265

10.6.1 Is the Temporal Resolution of fMRI Good Enough for Granger Causality? p. 267

10.6.2 Do Interregional Timing Differences in the hrf Invalidate Granger Causality? p. 267

10.7 Software Packages p. 268

10.8 Derivation of Proposition 10.1 p. 268

11 Assessing Functional Connectivity via Coherence Analysis p. 269

11.1 Autocorrelation and Cross-Correlation p. 269

11.2 Power Spectrum and Cross-Power Spectrum p. 274

11.3 Coherence p. 278

11.3.1 Coherence in Rapid versus Slow Event-Related Designs p. 284

11.3.2 An Empirical Application p. 288

11.3.3 Hypothesis Testing p. 290

11.4 Partial Coherence p. 290

11.5 Using the Phase Spectrum to Determine Causality p. 293

11.7 Derivations p. 300

11.7.1 Proposition 11.1 p. 300

11.7.2 Proposition 11.2 p. 300

12 Principal Component Analysis p. 303

12.1 Principal Component Analysis p. 304

12.2 PCA with fMRI Data p. 307

12.3 Using PCA to Eliminate Noise p. 309

12.3.1 Algorithm for Eliminating Noise from fMRi Data p. 311

12.4 Singular-Value Decomposition p. 315

13 Independent Component Analysis p. 319

13.1 The Cocktail-Party Problem p. 320

13.2 Applying ICA to fMRI Data p. 320

13.2.1 Spatial ICA p. 322

13.2.2 Assessing Statistical Independence p. 324

13.2.3 The Importance of Nonnormality in ICA p. 325

13.2.4 Preparing Data for ICA p. 326

13.3 ICA Algorithms p. 328

13.3.1 Minimizing Mutual Information p. 328

13.3.2 Methods That Maximize Nonnormality p. 330

13.3.3 Maximum Likelihood Approaches p. 332

13.3.4 Infomax p. 333

13.4 Interpreting ICA Results p. 336

13.4.1 Determining the Relative Importance of Each Component p. 336

13.4.2 Assigning Meaning to Components p. 337

13.5 The Noisy ICA Model p. 340

13.7 Group ICA p. 346

13.8 Comparing ICA and GLM Approaches p. 347

13.10 Derivations p. 350

13.10.1 Why Whitening Reduces the Number of Free Parameters in the ICA Model p. 350

13.10.2 The Infomax Learning Algorithm p. 351

14 Decoding via Multivoxel Pattern Analysis p. 353

14.1 General Overview of MVPA p. 353

14.2 Determining the Search Region and the Curse of Dimensionality p. 355

14.3 Creating the Activity Vectors p. 360

14.4 Preprocessing for MVPA p. 364

14.5 Building a Classifier p. 366

14.5.1 Fisher Linear Discriminant Analysis p. 370

14.5.2 Support Vector Machines p. 371

14.6 Validation p. 373

14.7 Statistical Inference p. 376

14.7.1 Individual-Subject Analysis p. 376

14.7.2 Croup-Level inference p. 377

14.8 Feature Selection p. 379

14.9 MVPA Software p. 380

14.11 Description of the SVM Algorithm That Maximizes the Margin p. 382

14.11.1 Linear SVMs p. 382

14.11.2 Nonlinear SVMs p. 386

15 Encoding Models p. 389

15.1 Voxel-Based Encoding Models p. 390

15.2 Inverting an Encoding Model to Produce a Decoding Scheme p. 397

15.3 Model-Based fMRI p. 400

15.4 Computational Cognitive Neuroscience p. 403

16 Dynamic Causal Modeling p. 407

16.1 Linear Dynamical Models of Neural Activation p. 408

16.2 Bilinear Dynamical Models of Neural Activation p. 412

16.3 Generalizations of the Bilinear Model p. 419

16.3.1 Quadratic DCM p. 419

16.3.2 Two-State DCM p. 420

16.3.3 Stochastic DCM p. 421

16.4 The Hemodynamic Model p. 422

16.5 Parameter Estimation p. 423

16.6 Model Selection p. 429

16.6.1 Model Selection by Minimizing BIC p. 438

16.6.2 Model Selection by Maximizing Negative Free Energy p. 439

16.7 Group Analysis p. 442

16.7.1 Fixed-Effects DCM Analyses p. 442

16.7.2 Random-Effects DCM Analyses p. 443

16.9 Derivation of Negative Free Energy p. 446

17 Representational Similarity Analysis p. 453

17.1 Extracting an RDM from the BOLD Data p. 455

17.1.1 Selecting the ROI p. 455

17.1.2 Estimating Activity Vectors p. 456

17.1.3 Computing Dissimilarity between Activity Vectors p. 457

17.2 Building a Geometric Model of the Similarity Structure p. 462

17.3 Perceived Similarity in Humans p. 467

17.4 Group-Level Inference with RSA p. 470

17.5 Encoding and Decoding Using Representational Similarity p. 475

17.6 RSA Software p. 477.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

9780262354059

0262354055

OCLC:

1099693301

Access Restriction:

Restricted for use by site license.