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Integration of EEG-FMRI in an Auditory Oddball Paradigm Using Joint Independent Component Analysis Jain Mangalathu Arumana Marquette University

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Integration of EEG-FMRI in an Auditory Oddball Paradigm Using Joint Independent Component Analysis Jain Mangalathu Arumana Marquette University Marquette University e-Publications@Marquette Dissertations (2009 -) Dissertations, Theses, and Professional Projects Integration of EEG-FMRI in an Auditory Oddball Paradigm Using Joint Independent Component Analysis Jain Mangalathu Arumana Marquette University Recommended Citation Mangalathu Arumana, Jain, "Integration of EEG-FMRI in an Auditory Oddball Paradigm Using Joint Independent Component Analysis" (2012). Dissertations (2009 -). Paper 210. http://epublications.marquette.edu/dissertations_mu/210 INTEGRATION OF EEG-FMRI IN AN AUDITORY ODDBALL PARADIGM USING JOINT INDEPENDENT COMPONENT ANALYSIS by Jain Mangalathu Arumana, B.Sc., M.Sc. A dissertation submitted to the Faculty of the Graduate School, Marquette University, in Partial Fulfillment of the Requirements for the Degree of Doctor Philosophy Milwaukee, Wisconsin August 2012 ABSTRACT INTEGRATION OF EEG-FMRI IN AN AUDITORY ODDBALL PARADIGM USING JOINT INDEPENDENT COMPONENT ANALYSIS Jain Mangalathu Arumana, B.Sc., M.Sc. Marquette University, 2012 The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. The overall objective of this dissertation is to determine the sensitivity and limitations of joint independent component analysis (jICA) within- subject for integration of ERP and fMRI data collected simultaneously in a parametric auditory oddball paradigm. The main experimental finding in this work is that jICA revealed significantly stronger and more extensive activity in brain regions associated with the auditory P300 ERP than a P300 linear regression analysis, both at the group level and within-subject. The results suggest that, with the incorporation of spatial and temporal information from both imaging modalities, jICA is more sensitive to neural sources commonly observed with ERP and fMRI compared to a linear regression analysis. Furthermore, computational simulations suggest that jICA can extract linear and nonlinear relationships between ERP and fMRI signals, as well as uncoupled sources (i.e., sources with a signal in only one imaging modality). These features of jICA can be important for assessing disease states in which the relationship between the ERP and fMRI signals is unknown, as well as pathological conditions causing neurovascular uncoupling, such as stroke. i ACKNOWDLEDGEMENTS Jain Mangalathu Arumana, B.Sc., M.Sc. I would like to thank my two advisors, Dr. Scott Beardsley and Dr. Einat Liebenthal for their constant support, patience and guidance. I appreciate the time educating me on writing manuscripts, conducting research and presenting at conferences. I appreciate the time both of them took to meet with me on a regular basis and discussing research ideas. I would also like to thank all of my colleagues at the Medical College of Wisconsin who have assisted me as a graduate student in the lab. I would like to especially thank Anjali Desai who helped me in multiple studies with EEG preparation and great feedback on fMRI analysis. I would like to thank Dr. Merav Sabri for the introduction to EEG analysis. I would also like to thank Dr. Tom Prieto and Tim Thelaner for the constant help in troubleshooting the EEG system. I would also like to thank the late Jim Verduin who introduced me and gave me insight into presentation programming. I would also like to thank Julie Peay, Tina Kostenko, Dr. Li Yiu and Dr. Vinai Roopchansingh in the biophysics department who helped me with MR scans and the protocol development. I would also like to recognize my colleagues at Marquette University, Megan, Prajakta, Tushar, Chintan, Sampada and David. All of them were always happy to help, ii participate in my studies and gave great feedback in our research meetings. A special thanks goes to David who helped me a lot with the computing cluster at Marquette. I greatly appreciate all of the volunteers who participated in my EEG-fMRI experiments, which are by far not the most comfortable studies. Without them, this work would have not been possible. Lastly, I would like to acknowledge the constant support and love of my family, my parents Theresiamma and John and my sister Sally during the time of my graduate studies. Most importantly thanks to my husband Winston for his constant support and love while I worked on my dissertation. iii TABLE OF CONTENTS ACKNOWDLEDGEMENTS............................................................................................ i LIST OF TABLES ........................................................................................................... vi LIST OF FIGURES ........................................................................................................ vii INTRODUCTION AND BACKGROUND .................................................................... 9 1.1 INTRODUCTION .............................................................................................. 2 1.2 Electroencephalography and ERPs..................................................................... 3 1.3 Functional MRI................................................................................................... 7 1.4 Relationship between neural activity and hemodynamic changes.................... 10 1.5 Temporal dynamics of information processing in brain networks ................... 11 1.6 Simultaneous fMRI and ERP............................................................................ 12 1.7 Model-based and data-driven techniques for the integration of fMRI and ERP............................................................................................................... 13 1.8 Auditory oddball paradigm............................................................................... 15 1.9 Aims and Significance of Research.................................................................. 17 Within-subject joint independent component analysis of simultaneous fMRI / ERP in an auditory oddball paradigm................................................................................... 20 2.1 INTRODUCTION ............................................................................................ 21 2.2 METHODS....................................................................................................... 25 2.2.1 Participants.................................................................................................... 25 2.2.2 Experimental Design..................................................................................... 26 2.2.3 fMRI Acquisition.......................................................................................... 28 2.2.4 EEG Acquisition........................................................................................... 29 iv 2.2.5 FMRI linear regression analysis ................................................................... 32 2.2.6 EEG preprocessing........................................................................................ 33 2.2.7 Joint Independent Component Analysis (JICA) ........................................... 34 2.2.8 Source Reconstruction .................................................................................. 40 2.2.9 Region of Interest analysis............................................................................ 41 2.3 RESULTS ......................................................................................................... 43 2.3.1 Behavioral Results ........................................................................................ 43 2.3.2 ERP Waveforms............................................................................................ 44 2.3.3 FMRI Linear Regression Maps..................................................................... 45 2.3.4 Source Reconstruction of the P300 ERP component.................................... 46 2.3.5 jICA fMRI maps ........................................................................................... 47 2.3.6 ERP jICA ...................................................................................................... 49 2.3.7 Source Reconstruction of the ERP jICA....................................................... 50 2.3.8 Region of interest analysis............................................................................ 52 2.3.9 Relationship between ERP and fMRI joint components .............................. 55 2.3.10 Multi-Channel versus Single-Channel ERP jICA..................................... 56 2.4 DISCUSSION................................................................................................... 58 Theoretical evaluation of Joint-Independent Component Analysis for the integration of multi-channel ERP and fMRI data........................................................................... 62 3.1 Introduction....................................................................................................... 63 3.2 Methods............................................................................................................. 67 3.2.1 Overview of Computational Model .............................................................. 67 3.2.2 Parametric and non-parametric experimental design.................................... 70 v 3.2.3 Simulation parameters for computational model.......................................... 71 3.2.4 Temporal and Spatial ICA ............................................................................ 72 3.2.5 JICA.............................................................................................................
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