Machine Learning and Deep Learning Applications in Neuroimaging by Gowtham Krishnan Murugesan Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy to the faculty of University of Texas at Arlington, University of Texas at Dallas and UT Southwestern Medical Center Doctor of Philosophy In Biomedical Engineering Supervising Committee: Dr. Joseph Maldjian Dr. Hanli Liu Dr. Ananth Madhuranthakam Dr. Sriraam Natarajan Dr. Won Hwa Kim Acknowledgment It is my pleasure to acknowledge the roles of several individuals who were instrumental for the completion of my Ph.D. research. First and foremost, I would like to express my sincere thanks to my supervising mentor Dr. Joseph Maldjian, who expertly guided me through my Ph.D. His unwavering enthusiasm in applying novel deep learning techniques to neuroimaging kept me constantly engaged with my research, and his personal generosity helped me make my time enjoyable at UT southwestern I would like to extend my gratitude to Dr. Hanli Liu for supporting me right from my masters and whose personality and motivation inspired me to continue my research in neuroimaging. I also would like to thank Dr. Albert Montillo for giving me an opportunity to work in UT southwestern and mentoring me to develop machine learning skills. I would like to thank the rest of my dissertation committee (Dr. Ananth Madhuranthakam, Dr. Won Hwa Kim, and Dr. Sriraam Natarajan) for their insightful comments and invaluable advice. My appreciation also extends to my lab faculties Ben Wagner, Dr. Elizabeth Davenport, Dr. Fang F Yu, Dr. Thomas O Neil, Dr. Bhavya Shah, and colleagues Chandan Ganesh, Sahil Nalawade, James Holcomb and Divya Reddy for extending their help and support in all possible ways. My gratitude goes to the faculty and staff of bioengineering departments at UT Arlington and UT Southwestern. Special thanks to Ms. Julia Rockow for all her help and encouragement from the day I arrived at UTA to the last day August 11, 2020 i Table of Contents List of Figures .............................................................................................................................................. v List of Tables .............................................................................................................................................. ix 1. Overview of Deep Learning in Neuroimaging .................................... Error! Bookmark not defined. 1.1 Introduction ................................................................................................................................. 1 1.2 Deep Learning applications in Neuroimaging .......................................................................... 4 1.3 Challenges in Applying Deep Learning Techniques in Neuroimaging .................................. 4 2 BrainNET: Inference of brain network topology using Machine Learning ................................ 14 2.1 Abstract ...................................................................................................................................... 15 2.2 Introduction ............................................................................................................................... 16 2.3 Materials and Methods ............................................................................................................. 19 2.3.1 Datasets ............................................................................................................................... 19 2.3.2 BrainNET Model Development .......................................................................................... 20 2.3.3 Analysis ............................................................................................................................... 27 2.3.4 Evaluation of inference methods on ADHD data ............................................................... 30 2.4 Experimental Results ................................................................................................................ 31 2.4.1 Simulation Data................................................................................................................... 31 2.4.2 Evaluation of inference methods on ADHD data ............................................................... 35 2.5 Discussion................................................................................................................................... 37 2.5.1 BrainNET Inference of Network Topology in Simulated fMRI Data................................. 38 2.5.2 Evaluation of inference methods on ADHD Data ............................................................... 41 2.6 Conclusion ................................................................................................................................. 43 2.7 REFERENCES .......................................................................................................................... 45 3 Resting state fMRI distinguishes subconcussive head impact exposure levels in youth and high school players over a single season of football ........................................................................................ 49 3.1 Abstract ...................................................................................................................................... 51 3.2 Introduction ............................................................................................................................... 53 3.3 Materials and Methods ............................................................................................................. 54 3.3.1 Study cohort ........................................................................................................................ 54 3.3.2 Computation of the Head Impact Exposure (HIE) measure ............................................... 55 3.3.3 MRI Data Acquisition ......................................................................................................... 56 3.3.4 Resting State fMRI Analysis and feature extraction ........................................................... 57 ii 3.3.5 Classifier training, evaluation and model selection methodology ...................................... 60 3.4 Results ........................................................................................................................................ 62 3.4.1 Performance of the classifiers using ΔGMBrainNET features ............................................... 62 3.4.2 Performance of the classifiers using ΔGMCorrelation features ............................................... 62 3.5 Discussion................................................................................................................................... 63 3.5.1 Comparison between different pipelines ............................................................................. 63 3.5.2 Analysis of the selected features ......................................................................................... 64 3.6 Conclusion ................................................................................................................................. 65 3.7 References .................................................................................................................................. 67 4 No dose Gadolinium contrast using deep learning......................................................................... 81 4.1 Abstract : ................................................................................................................................... 82 4.2 Introduction ............................................................................................................................... 83 4.3 Materials and Methods:............................................................................................................ 85 4.3.1 Data and Preprocessing ....................................................................................................... 85 Data preprocessing .............................................................................................................................. 86 4.3.2 Network Architecture .......................................................................................................... 86 4.3.3 Structural Perception Loss .................................................................................................. 87 4.4 Evaluation and Statistical Analysis ......................................................................................... 88 4.4.1 Quantitative Evaluation ....................................................................................................... 88 4.4.2 Qualitative Evaluation ......................................................................................................... 88 4.4.3 Importance of the Input MR sequence for prediction ......................................................... 89 4.5 Results ........................................................................................................................................ 89 4.5.1 Quantitative Evaluation ....................................................................................................... 89 4.5.2 Qualitative Evaluation ......................................................................................................... 91 4.5.3 Importance of the input MR sequences for prediction contrast enhancement .................... 91 4.6 Discussion..................................................................................................................................