Search and Find
Service
Statistical Parametric Mapping - The Analysis of Functional Brain Images
of: William D. Penny, Karl J. Friston, John T. Ashburner
Elsevier Trade Monographs, 2006
ISBN: 9780080466507 , 689 Pages
Format: PDF, ePUB, Read online
Copy protection: DRM
Front Cover
1
Statistical Parametric Mapping
4
Copyright Page
5
Table of Contents
6
Acknowledgements
8
Part 1 Introduction
10
Chapter 1 A short history of SPM
12
INTRODUCTION
12
THE PET YEARS
14
THE fMRI YEARS
14
THE MEG-EEG YEARS
17
REFERENCES
17
Chapter 2 Statistical parametric mapping
19
INTRODUCTION
19
SPATIAL TRANSFORMS AND COMPUTATIONAL ANATOMY
20
STATISTICAL PARAMETRIC MAPPING AND THE GENERAL LINEAR MODEL
23
TOPOLOGICAL INFERENCE AND THE THEORY OF RANDOM FIELDS
27
EXPERIMENTAL AND MODEL DESIGN
29
INFERENCE IN HIERARCHICAL MODELS
37
CONCLUSION
39
REFERENCES
39
Chapter 3 Modelling brain responses
41
INTRODUCTION
41
ANATOMICAL MODELS
42
STATISTICAL MODELS
43
MODELS OF FUNCTIONAL INTEGRATION
47
CONCLUSION
53
REFERENCES
53
Part 2 Computational anatomy
56
Chapter 4 Rigid Body Registration
58
INTRODUCTION
58
RE-SAMPLING IMAGES
59
RIGID BODY TRANSFORMATIONS
61
WITHIN-MODALITY RIGID REGISTRATION
64
BETWEEN-MODALITY RIGID REGISTRATION
67
REFERENCES
70
Chapter 5 Non-linear Registration
72
INTRODUCTION
72
OBJECTIVE FUNCTIONS
73
LARGE DEFORMATION APPROACHES
81
ESTIMATING THE MAPPINGS
83
SPATIAL NORMALIZATION IN THE SPM SOFTWARE
84
EVALUATION STRATEGIES
86
REFERENCES
87
Chapter 6 Segmentation
90
INTRODUCTION
90
THE OBJECTIVE FUNCTION
91
OPTIMIZATION
94
REFERENCES
99
Chapter 7 Voxel-Based Morphometry
101
INTRODUCTION
101
PREPARING THE DATA
102
STATISTICAL MODELLING AND INFERENCE
104
REFERENCES
107
Part 3 General linear models
108
Chapter 8 The General Linear Model
110
INTRODUCTION
110
THE GENERAL LINEAR MODEL
110
INFERENCE
114
PET AND BASIC MODELS
117
fMRI MODELS
127
APPENDIX 8.1 THE AUTOREGRESSIVE MODEL OF ORDER 1 PLUS WHITE NOISE
133
APPENDIX 8.2 THE SATTERTHWAITE APPROXIMATION
133
REFERENCES
134
Chapter 9 Contrasts and Classical Inference
135
INTRODUCTION
135
CONSTRUCTING MODELS What should be included in the model?
135
CONSTRUCTING AND TESTING CONTRASTS
138
CONSTRUCTING AND TESTING F-CONTRASTS
141
CORRELATION BETWEEN REGRESSORS
145
DESIGN COMPLEXITY
146
SUMMARY
147
APPENDIX 9.1 NOTATION
147
APPENDIX 9.2 SUBSPACES
148
APPENDIX 9.3 ORTHOGONAL PROJECTION
148
REFERENCES
148
Chaper 10 Covariance Components
149
INTRODUCTION
149
SOME MATHEMATICAL EQUIVALENCES
150
ESTIMATING COVARIANCE COMPONENTS
152
CONCLUSION
156
REFERENCES
156
Chapter 11 Hierarchical Models
157
INTRODUCTION
157
TWO-LEVEL MODELS
158
PARAMETRIC EMPIRICAL BAYES
160
NUMERICAL EXAMPLE
162
BELIEF PROPAGATION
163
DISCUSSION
164
REFERENCES
164
Chapter 12 Random Effects Analysis
165
INTRODUCTION
165
RANDOM EFFECTS ANALYSIS
165
FIXED EFFECTS ANALYSIS
167
PARAMETRIC EMPIRICAL BAYES
167
PET DATA EXAMPLE
170
fMRI DATA EXAMPLE
172
DISCUSSION
172
APPENDIX 12.1 EXPECTATIONS AND TRANSFORMATIONS
173
REFERENCES
174
Chapter 13 Analysis of Variance
175
INTRODUCTION
175
ONE-WAY BETWEEN-SUBJECT ANOVA
176
ONE-WAY WITHIN-SUBJECT ANOVA
177
TWO-WAY WITHIN-SUBJECT ANOVAs
179
GENERALIZATION TO M-WAY ANOVAs
182
fMRI BASIS FUNCTIONS
184
DISCUSSION
184
APPENDIX 13.1 THE KRONECKER PRODUCT
185
APPENDIX 13.2 WITHIN-SUBJECT MODELS
185
REFERENCES
186
Chapter 14 Convolution Models for fMRI
187
INTRODUCTION
187
THE HAEMODYNAMIC RESPONSE FUNCTION (HRF)
187
TEMPORAL BASIS FUNCTIONS
189
TEMPORAL FILTERING AND AUTOCORRELATION
192
NON-LINEAR CONVOLUTION MODELS
195
A WORKED EXAMPLE
197
REFERENCES
200
Chapter 15 Efficient Experimental Design for fMRI
202
INTRODUCTION
202
TAXONOMY OF EXPERIMENTAL DESIGN
202
EVENT-RELATED fMRI, AND RANDOMIZED VERSUS BLOCKED DESIGNS
204
EFFICIENCY AND OPTIMIZATION OF fMRI DESIGNS
208
COMMON QUESTIONS What is the minimum number of events I need?
217
REFERENCES
218
Chapter 16 Hierarchical models for EEG and MEG
220
INTRODUCTION
220
SPATIAL MODELS
221
TEMPORAL MODELS
223
HYPOTHESIS TESTING WITH HIERARCHICAL MODELS
227
SUMMARY
228
REFERENCES
229
Part 4 Classical inference
230
Chapter 17 Parametric procedures
232
INTRODUCTION
232
THE BONFERRONI CORRECTION
233
RANDOM FIELD THEORY
235
DISCUSSION
239
REFERENCES
240
Chapter 18 Random Field Theory
241
INTRODUCTION
241
THE MAXIMUM TEST STATISTIC
241
THE MAXIMUM SPATIAL EXTENT OF THE TEST STATISTIC
242
SEARCHING IN SMALL REGIONS
243
ESTIMATING THE FWHM
243
FALSE DISCOVERY RATE
244
CONCLUSION
245
REFERENCES
245
Chapter 19 Topological Inference
246
INTRODUCTION
246
TOPOLOGICAL INFERENCE
246
THEORY AND DISTRIBUTIONAL APPROXIMATIONS
248
POWER ANALYSES
251
SUMMARY
253
REFERENCES
254
Chapter 20 False Discovery Rate procedures
255
INTRODUCTION
255
MULTIPLE TESTING DEFINITIONS
255
FDR METHODS
257
EXAMPLES AND DEMONSTRATIONS
258
CONCLUSION
261
REFERENCES
261
Chapter 21 Non-parametric procedures
262
INTRODUCTION
262
PERMUTATION TESTS
263
WORKED EXAMPLES
270
CONCLUSIONS
279
REFERENCES
280
Part 5 Bayesian inference
282
Chapter 22 Empirical Bayes and hierarchical models
284
INTRODUCTION
284
THEORETICAL BACKGROUND
286
EM AND COVARIANCE COMPONENT ESTIMATION
295
REFERENCES
303
Chapter 23 Posterior probability maps
304
INTRODUCTION
304
THEORY
305
EMPIRICAL DEMONSTRATIONS
307
CONCLUSION
311
REFERENCES
311
Chapter 24 Variational Bayes
312
INTRODUCTION
312
THEORY
312
EXAMPLES
315
DISCUSSION
318
APPENDIX 24.1
320
REFERENCES
320
Chapter 25 Spatio-temporal models for fMRI
322
INTRODUCTION
322
THEORY
322
RESULTS
327
DISCUSSION
330
APPENDIX 25.1
330
REFERENCES
330
Chapter 26 Spatio-temporal models for EEG
332
INTRODUCTION
332
THEORY
333
PCA
337
RESULTS
340
DISCUSSION
343
REFERENCES
344
Part 6 Biophysical models
346
Chapter 27 Forward models for fMRI
348
INTRODUCTION
348
NON-LINEAR EVOKED RESPONSES
350
THE HAEMODYNAMIC MODEL
352
KERNEL ESTIMATION
355
RESULTS AND DISCUSSION
356
DISCUSSION
357
CONCLUSION
359
REFERENCES
359
Chapter 28 Forward models for EEG
361
INTRODUCTION
361
ANALYTICAL FORMULATION Maxwell’s equations
362
NUMERICAL SOLUTION OF THE BEM EQUATION
365
ANALYTIC SOLUTION OF THE BEM EQUATION
372
DISCUSSION
373
REFERENCES
375
Chapter 29 Bayesian inversion of EEG models
376
INTRODUCTION
376
THE BAYESIAN FORMULATION OF CLASSICAL REGULARIZATION
377
A HIERARCHICAL OR PARAMETRIC EMPIRICAL BAYES APPROACH
378
RESTRICTED MAXIMUM LIKELIHOOD
378
APPLICATION TO SYNTHETIC MEG DATA
379
APPLICATION TO SYNTHETIC EEG DATA
381
CONCLUSION
383
APPENDIX 29.1 THE L-CURVE APPROACH
384
REFERENCES
384
Chapter 30 Bayesian inversion for induced responses
386
INTRODUCTION
386
THE BASIC ReML APPROACH TO DISTRIBUTED SOURCE RECONSTRUCTION
387
A TEMPORALLY INFORMED SCHEME
389
ESTIMATING RESPONSE ENERGY
390
AVERAGING OVER TRIALS
391
SOME EXAMPLES
392
DISCUSSION
397
REFERENCES
398
Chapter 31 Neuronal models of ensemble dynamics
400
INTRODUCTION
400
THEORY
402
ILLUSTRATIVE APPLICATIONS
409
CONCLUSION
412
APPENDIX 31.1 NUMERICAL SOLUTION OF FOKKER-PLANCK EQUATION
413
REFERENCES
413
Chapter 32 Neuronal models of energetics
415
INTRODUCTION
415
EEG AND fMRI INTEGRATION
415
A HEURISTIC FOR EEG-fMRI INTEGRATION
416
EMPIRICAL EVIDENCE
419
SUMMARY
421
REFERENCES
421
Chapter 33 Neuronal models of EEG and MEG
423
INTRODUCTION
423
NEURAL-MASS MODELS
423
MODELLING CORTICAL SOURCES
425
HIERARCHICAL MODELS OF CORTICAL NETWORKS
430
MECHANISMS OF ERP GENERATION
432
PHASE-RESETTING AND THE ERP
436
ONGOING AND EVENT-RELATED ACTIVITY
439
INDUCED RESPONSES AND ERPs
440
DISCUSSION
445
CONCLUSION
446
REFERENCES
447
Chapter 34 Bayesian inversion of dynamic models
450
INTRODUCTION
450
A HAEMODYNAMIC MODEL
451
PRIORS
454
SYSTEM IDENTIFICATION
455
EMPIRICAL ILLUSTRATIONS
458
CONCLUSION
461
REFERENCES
461
Chapter 35 Bayesian model selection and averaging
463
INTRODUCTION
463
CONDITIONAL PARAMETER INFERENCE
464
MODEL INFERENCE
465
MODEL AVERAGING
469
DYNAMIC CAUSAL MODELS
469
SOURCE RECONSTRUCTION
472
MULTIPLE CONSTRAINTS
472
MODEL AVERAGING
473
DISCUSSION
475
REFERENCES
475
Part 7 Connectivity
478
Chapter 36 Functional integration
480
INTRODUCTION
480
FUNCTIONAL SPECIALIZATION AND INTEGRATION
481
LEARNING AND INFERENCE IN THE BRAIN
485
IMPLICATIONS FOR CORTICAL INFRASTRUCTURE AND PLASTICITY
489
ASSESSING FUNCTIONAL ARCHITECTURES WITH BRAIN IMAGING
492
FUNCTIONAL INTEGRATION AND NEUROPSYCHOLOGY
497
CONCLUSION
498
REFERENCES
499
Chapter 37 Functional connectivity: eigenimages and multivariate analyses
501
INTRODUCTION
501
EIGENIMAGES, MULTIDIMENSIONAL SCALING AND OTHER DEVICES
502
NON-LINEAR PRINCIPAL AND INDEPENDENT COMPONENT ANALYSIS (PCA AND ICA)
508
MANCOVA AND CANONICAL IMAGE ANALYSES
511
REFERENCES
516
Chapter 38 Effective Connectivity
517
INTRODUCTION
517
IDENTIFICATION OF DYNAMIC SYSTEMS
517
STATIC MODELS
521
DYNAMIC MODELS
525
CONCLUSION
530
REFERENCES
530
Chapter 39 Non-linear coupling and kernels
531
INTRODUCTION
531
NEURONAL TRANSIENTS
532
NEURONAL CODES
534
EVIDENCE FOR NON-LINEAR COUPLING
536
THE NEURAL BASIS OF NON-LINEAR COUPLING
537
CONCLUSION
541
REFERENCES
541
Chapter 40 Multivariate autoregressive models
543
INTRODUCTION
543
THEORY
544
APPLICATION
546
DISCUSSION
547
APPENDIX 40.1
548
REFERENCES
549
Chapter 41 Dynamic Causal Models for fMRI
550
INTRODUCTION
550
THEORY
553
FACE VALIDITY – SIMULATIONS
559
PREDICTIVE VALIDITY – AN ANALYSIS OF SINGLE WORD PROCESSING
562
CONSTRUCT VALIDITY – AN ANALYSIS OF ATTENTIONAL EFFECTS ON CONNECTIONS
565
CONCLUSION
569
REFERENCES
569
Chapter 42 Dynamic causal models for EEG
570
INTRODUCTION
570
THEORY
572
BAYESIAN INFERENCE AND MODEL COMPARISON
577
EMPIRICAL STUDIES
578
CONCLUSION
582
SUMMARY
584
APPENDIX
584
REFERENCES
584
Chapter 43 Dynamic Causal Models and Bayesian selection
586
INTRODUCTION
586
INTER-HEMISPHERIC INTEGRATION IN THE VENTRAL STREAM
588
DISCUSSION
592
REFERENCES
593
Appendices
596
Appendix 1 Linear models and inference
598
INTRODUCTION
598
INFORMATION THEORY AND DEPENDENCY
598
OTHER PERSPECTIVES
599
SUMMARY
600
REFERENCES
600
Appendix 2 Dynamical systems
601
INTRODUCTION
601
EFFECTIVE CONNECTIVITY
602
INPUT-OUTPUT MODELS
603
INPUT-STATE-OUTPUT MODELS
606
MULTIVARIATE ARMA MODELS
610
CONCLUSION
611
REFERENCES
611
Appendix 3 Expectation maximization
612
INTRODUCTION
612
RELATIONSHIP TO ReML
614
REFERENCES
614
Appendix 4 Variational Bayes under the Laplace approximation
615
INTRODUCTION
615
VARIATIONAL BAYES
616
VARIATIONAL BAYES FOR NON-LINEAR MODELS
619
EXPECTATION MAXIMIZATION FOR NON-LINEAR MODELS
620
RESTRICTED MAXIMUM LIKELIHOOD FOR LINEAR MODELS
622
RESTRICTED MAXIMUM LIKELIHOOD FOR HIERARCHICAL LINEAR MODELS
623
MODEL SELECTION WITH REML
625
REFERENCES
626
Appendix 5 Kalman filtering
628
INTRODUCTION
628
THE EXTENDED KALMAN FILTER
628
REFERENCES
629
Appendix 6 Random field theory
630
INTRODUCTION
630
THEORY
630
INTEGRAL GEOMETRY
630
RANDOM FIELDS
631
EXAMPLE
632
REFERENCES
632
Index
634
Color Plates
658
All prices incl. VAT