Statistical analysis of fMRI data:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. [u.a.]
MIT Press
2011
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. [321] - 328 |
| Beschreibung: | XIII, 332 S. Ill., graph. Darst. |
| ISBN: | 9780262015042 |
Internformat
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Datensatz im Suchindex
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| adam_text | Titel: Statistical analysis of fMRI data
Autor: Ashby, F. Gregory
Jahr: 2011
Preface x¡
Acronyms xv
Introduction 1
What Is fMRI? 2
The Scanning Session 4
Experimental Design 6
Data Analysis 7
Software Packages 8
Data Formats 11
Data Formats 11
DICOM 11
Analyze 13
NlfTI 13
MINC 14
Converting from One Format to Another 14
Reading fMRI Data into MATLAB 15
Modeling the BOLD Response 17
Linear Models of the BOLD Response 17
Methods of Estimating the hrf 21
Input an Impulse, and Observe the Response 21
Deconvolution 22
Open the Box; Study the Circuit 25
Take a Guess 25
Select a Flexible Mathematical Model of the hrf 28
Nonlinear Models of the BOLD Response 33
Conclusions 40
Preprocessing 41
Slice-Timing Correction 42
Slice-Timing Correction during Preprocessing 43
Linear Interpolation 43
Spline Interpolation 44
Sine Interpolation 45
Slice-Timing Correction during Task-Related Statistical Analysis 47
Head Motion Correction 51
Coregistering the Functional and Structural Data 58
Normalization 63
Spatial Smoothing 68
Temporal Filtering 73
Other Preprocessing Steps 77
Quality Assurance 78
Distortion Correction 78
Grand Mean Scaling 79
Conclusions 80
The General Linear Model 81
The FBR Method 81
Jittering 84
Microlinearity versus Macrolineahty 86
Using the General Linear Model to Implement the FBR Method 87
Modeling Baseline Activation and Systematic Non-Task-Related Variation in the BOLD
Signal 88
Designs with Multiple Stimulus Events 90
The Correlation Approach 91
Block Designs 97
A Graphical Convention for Displaying the Design Matrix 99
The General Linear Model 100
Parameter Estimation 103
Parameter Estimation in the FBR and Correlation Models 104
Hypothesis Testing via the Construction of Statistical Parametric Maps 107
Nonparametric Approaches to Hypothesis Testing 118
Percent Signal Change 119
Comparing the Correlation and FBR Methods 123
The Multiple Comparisons Problem 127
The Sidak and Bonferroni Corrections 128
Gaussian Random Fields 130
False Discovery Rate 141
Cluster-Based Methods 147
Cluster-Based Methods Using a Spatial Extent Criterion 150
Cluster-Based Methods Using a Criterion That Depends on Cluster Height and Spatial
Extent 153
Permutation-Based Solutions to the Multiple Comparisons Problem 156
Voodoo Correlations 157
Conclusions 158
7 Croup Analyses 159
Individual Differences 159
Fixed versus Random Factors in the General Linear Model 162
A Fixed Effects Group Analysis 164
A Random Effects Group Analysis 170
Comparing Fixed and Random Effects Analyses 175
Multiple Factor Experiments 176
Power Analysis 178
8 Coherence Analysis 185
Autocorrelation and Cross-Correlation 186
Power Spectrum and Cross-Power Spectrum 193
Coherence 197
Partial Coherence 209
Using the Phase Spectrum to Determine Causality 211
Conclusions 219
9 Granger Causality 221
Quantitative Measures of Causality 226
Parameter Estimation 231
Inference 234
Conditional Granger Causality 235
Comparing Granger Causality to Coherence Analysis 242
10 Principal Components Analysis 245
Principal Components Analysis 246
PCA with fMRI Data 248
Using PCA to Eliminate Noise 251
Conclusions 256
11 Independent Component Analysis 257
The Cocktail-Party Problem 257
Applying ICA to fMRI Data 258
Spatial ICA 260
Assessing Statistical Independence 261
The Importance of Non-normality in ICA 263
Preparing Data for ICA 263
ICA Algorithms 266
Minimizing Mutual Information 266
Mathematical Definitions 267
Conceptual Treatment 268
Methods That Maximize Non-normality 269
Maximum Likelihood Approaches 271
Infomax 272
Overview 272
The Infomax Learning Algorithm 274
Interpreting ICA Results 277
Determining the Relative Importance of Each Component 278
Assigning Meaning to Components 279
The Noisy ICA Model 281
Other Issues 285
Comparing ICA and GLM Approaches 287
Conclusions 289
12 Other Methods 291
Pattern Classification Techniques 291
Partial Least Squares 292
Dynamic Causal Modeling 293
Bayesian Approaches 294
Appendix A: Matrix Algebra Tutorial 297
Matrices and Their Basic Operations 297
Rank 304
Solving Linear Equations 306
Eigenvalues and Eigenvectors 310
Definitions 310
Properties 313
Appendix B: Multivariate Probability Distributions 315
Multivariate Normal Distributions 316
References 321
Index 329
|
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| indexdate | 2024-07-09T23:23:12Z |
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| isbn | 9780262015042 |
| language | English |
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| physical | XIII, 332 S. Ill., graph. Darst. |
| publishDate | 2011 |
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| publisher | MIT Press |
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| spelling | Ashby, F. Gregory Verfasser aut Statistical analysis of fMRI data F. Gregory Ashby Cambridge, Mass. [u.a.] MIT Press 2011 XIII, 332 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. [321] - 328 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022539478&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ashby, F. Gregory Statistical analysis of fMRI data |
| title | Statistical analysis of fMRI data |
| title_auth | Statistical analysis of fMRI data |
| title_exact_search | Statistical analysis of fMRI data |
| title_full | Statistical analysis of fMRI data F. Gregory Ashby |
| title_fullStr | Statistical analysis of fMRI data F. Gregory Ashby |
| title_full_unstemmed | Statistical analysis of fMRI data F. Gregory Ashby |
| title_short | Statistical analysis of fMRI data |
| title_sort | statistical analysis of fmri data |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022539478&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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