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New methods for the analysis of trial-to-trial variability in neuroimaging studies
Weeda, W.D.
Publication date
2012
Link to publication
Citation for published version (APA):
Weeda, W. D. (2012). New methods for the analysis of trial-to-trial variability in neuroimaging
studies.
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Contents
1 Introduction 11
1.1 A general model of trial-to-trial variability . . . 12
1.1.1 Reaction time (RT) . . . 14
1.1.2 Electroencephalography (EEG) . . . 16
1.1.3 Functional Magnetic Resonance Imaging (fMRI) . . . 17
1.2 Why trial-to-trial variability is important . . . 19
1.3 Outline . . . 20
1.4 Articles resulting from this thesis . . . 21
2 Empirical Support for a Drift Diffusion Model Account of the Worst Perfor-mance Rule 23 2.1 Introduction . . . 24
2.1.1 The drift diffusion model . . . 25
2.2 Method . . . 27
2.2.1 Participants . . . 27
2.2.2 Intelligence test . . . 27
2.2.3 Response time task . . . 27
NEW METHODS FOR THE ANALYSIS OF TRIAL-TO-TRIAL VARIABILITY IN NEUROIMAGING STUDIES
2.3 Results . . . 29
2.3.1 Preprocessing . . . 29
2.3.2 Worst Performance Rule . . . 29
2.3.3 Drift diffusion model parameters . . . 30
2.4 Discussion . . . 35
2.5 Supplementary Materials. . . 37
2.5.1 Characteristics of excluded participants . . . 37
2.5.2 Behavioral measures . . . 37
2.5.3 Drift diffusion model fits . . . 39
3 Simultaneous Estimation of Waveform, Amplitude, and Latency of Single-Trial EEG/MEG Data 41 3.1 Introduction . . . 42
3.2 Methods . . . 45
3.2.1 Model . . . 45
3.2.2 Parameter estimation . . . 47
3.2.3 Estimating single-trial amplitude and latency . . . 49
3.2.4 Multiple signals . . . 50 3.2.5 Model selection . . . 50 3.3 Simulations . . . 52 3.3.1 Results . . . 52 3.4 Empirical application . . . 59 3.4.1 Methods . . . 62 3.4.2 Results . . . 63 3.5 Discussion . . . 67
4 Activated Region Fitting: A Robust High-Power Method for the Analysis of
fMRI Data 69 4.1 Introduction . . . 70 4.2 Method . . . 71 4.2.1 Input . . . 72 4.2.2 Spatial model . . . 73 4.2.3 Estimation . . . 74 4.2.4 Model selection . . . 74 4.2.5 Variance estimation . . . 75 4.2.6 Hypothesis testing . . . 76 4.2.7 Starting values . . . 78 4.2.8 Simulations . . . 78 4.3 Results . . . 80 4.3.1 Parameter recovery . . . 80 4.3.2 Variance estimation . . . 81 4.3.3 Power analysis . . . 83
4.3.4 Real data example . . . 85
4.4 Discussion . . . 87
4.4.1 Acknowledgements . . . 89
4.5 Appendix A. Noise simulations . . . 90
4.5.1 Procedure . . . 90
4.6 Appendix B. Real data Tables . . . 91
4.7 Appendix C. Algorithm overview . . . 92
5 Functional Connectivity Analysis of fMRI Data Using Parameterized Regions-of-Interest 93 5.1 Introduction . . . 94
NEW METHODS FOR THE ANALYSIS OF TRIAL-TO-TRIAL VARIABILITY IN NEUROIMAGING STUDIES
5.2 Method . . . 95
5.2.1 Connectivity analysis . . . 96
5.2.2 Single-trial amplitude estimation . . . 96
5.2.3 Connectivity estimation . . . 97
5.2.4 Testing differences in connectivity . . . 97
5.2.5 Simulations . . . 98
5.2.6 ARF connectivity estimation and standard methods . . . 100
5.2.7 Power . . . 100
5.3 Results . . . 101
5.3.1 Power . . . 104
5.3.2 False positive rate . . . 104
5.4 Empirical application . . . 105
5.4.1 Results . . . 105
5.5 Discussion . . . 107
5.6 Acknowledgements . . . 109
5.7 Appendix A. Extension to full volume fMRI analysis. . . 110
5.7.1 Spatial model . . . 110
5.8 Appendix B. Raw time-series versus trial-by-trial amplitude . . . 112
5.9 Appendix C. Consistency of correlation estimates. . . 114
5.10 Supplementary materials . . . 115
6 arf3DS4: An Integrated Framework for Localization and Connectivity Ana-lysis of fMRI Data 119 6.1 Introduction . . . 120
6.2 Activated Region Fitting . . . 122
6.2.1 Data . . . 122 8
6.2.2 Spatial model . . . 125 6.2.3 Parameter estimation . . . 127 6.2.4 Model selection . . . 127 6.2.5 Hypothesis testing . . . 128 6.2.6 Procedure . . . 128 6.2.7 Connectivity analysis . . . 129
6.3 The arf3DS4 package . . . 130
6.3.1 Setting up an experiment . . . 132
6.3.2 Creating and customizing a model . . . 136
6.3.3 Fitting a model . . . 139
6.3.4 Finding an optimal model . . . 142
6.3.5 Connectivity analysis . . . 142
6.4 The ARF example data . . . 145
6.4.1 The experiment structure . . . 146
6.4.2 Getting a feel for the data . . . 147
6.4.3 Fitting the model . . . 150
6.4.4 Selecting an optimal model . . . 153
6.4.5 Connectivity analysis . . . 155
6.5 Empirical data . . . 157
6.6 Conclusion . . . 157
6.7 Acknowledgements . . . 158
6.8 Appendix A. NIfTI files and the fmri.data class . . . 159
6.8.1 Visualizing fMRI data . . . 160
6.9 Appendix B. S4-classes . . . 160
NEW METHODS FOR THE ANALYSIS OF TRIAL-TO-TRIAL VARIABILITY IN NEUROIMAGING STUDIES
7.1 Variability in neuroimaging data . . . 164 7.1.1 Simultaneous Estimation of Waveform Amplitude and Latency 164 7.1.2 Activated Region Fitting . . . 165 7.2 Why the analysis of trial-to-trial variability is important. . . 167
References 169
Summary in Dutch / Samenvatting in het Nederlands 185
Dankwoord 193
