UNIVERSITY of CALIFORNIA Los Angeles Using Machine-Learning
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UNIVERSITY OF CALIFORNIA Los Angeles Using Machine-Learning to Diagnose Perception, Feeling, and Action: A Study oF Neuroimaging, Psychometric, and Insurance Claims Data A dissertation submitted in partial satisfaction oF the requirements For the degree Doctor oF Philosophy in Neuroscience by Donald Albert Vaughn Jr. 2017 © Copyright by Donald Albert Vaughn Jr. 2017 ABSTRACT OF THE DISSERTATION Using Machine-Learning to Diagnose Perception, Feeling, and Action: A Study oF Neuroimaging, Psychometric, and Insurance Claims Data by Donald Albert Vaughn Jr. Doctor oF Philosophy in Neuroscience University oF CaliFornia, Los Angeles, 2017 ProFessor Mark S. Cohen, Chair I explored the capability oF multivariate machine learning on low-signal and bioinFormatic data to distinguish between diFFerent perceptual, emotional, and behavioral outcomes. In Chapter II, I designed and executed an experiment to evoke diFFerences in empathy on the basis oF religious identities as well as on general notions oF ingroup and outgroup. I discovered that the act oF simply learning about another’s religious identity correlated with a detectable modulation in one’s brain activity—most notably in regions related to empathy and sympathy—which identiFied subjects’ religious ingroups From outgroups with 70% accuracy. In Chapter III, I used multimodal data to distinguish between control participants and patients with one oF two body-image disorders: anorexia nervosa and body dysmorphic disorder. These data included psychometric scales oF obsessive- compulsive behaviors, depression, anxiety, and insight; white matter connectivity patterns; and neural haemodynamic activity. My models achieved 72% accuracy in ii distinguishing healthy controls From patients with anorexia nervosa or body dysmorphic disorder, and 76% accuracy in distinguishing patients with anorexia nervosa From those with body dysmorphic disorder. In Chapter IV, I extended these techniques to insurance claims data to predict the actions oF patients and doctors. I discovered that the pattern oF care delivered by doctors predicted the hospitalizations and use oF biologic drugs in patients with inFlammatory bowel disease. My diagnostic model may be used by healthcare alongside various interventions to reduce hospitalizations, decrease expenditures oF insurance companies, and improve patient quality oF liFe. iii The dissertation oF Donald Albert Vaughn Jr. is approved. Jamie Feusner Marco Iacoboni Daniel Hommes Mark S. Cohen, Committee Chair University oF CaliFornia, Los Angeles 2017 iv Dedicated to Mom, Dad, David, Mark, and Jamie v TABLE OF CONTENTS Chapter I - Introduction 1 1.1 Neuroimaging 4 1.2 ClassiFication 5 1.2.1 Signal detection theory curves 7 1.3 Odds ratios 8 1.4 Measuring distance 9 1.5 Graph theory metrics 10 1.6 Technical methods 10 1.6.1 Feature extraction From Functional data 11 1.6.2 SigniFicance testing 14 Chapter II - Predicting ingroups and outgroups From task-based Functional neuroimaging 16 2.2 Introduction 16 2.3 Methods 20 2.3.1 Participants 20 2.3.2 Behavioral Questionnaires 21 2.3.3 Stimuli 21 Baseline block 22 Experiment 1: religious aFFiliations 23 Experiment 2: Flexibility oF religious aFFiliations 25 Experiment 3: arbitrary teams 27 Behavioral response 28 2.3.4 MRI data 29 Acquisition 29 Preprocessing 29 GLM analysis 30 Group analysis 31 2.3.5 ClassiFication methods 32 ROI averaging 32 vi Modeling 33 2.4 Results 35 2.4.1 Univariate 35 Empathy network Functional localizer 35 Experiment 1: religious ingroups 38 2.4.2 Unbalanced design 43 Experiment 2: Flexibility oF religious ingroups 45 Experiment 3: arbitrary team membership 46 2.4.3 ClassiFication 48 Touch vs stab 48 Ingroup vs outgroup 49 2.5 Discussion 55 2.6 Summary oF work contributions 62 Chapter III - Predicting diagnoses oF anorexia nervosa and body dysmorphic disorder with multimodal neuroimaging and psychometric data 63 3.1 Abstract 63 3.2 Introduction 64 3.3 Methods and materials 66 3.3.1. Participants 66 3.3.2 FMRI data 67 DTI data 69 ClassiFication 70 3.4 Results 70 3.4.1 Psychometric Features 71 3.4.2 MR Features 73 3.5 ClassiFication 75 3.5.1 CTL vs non-CTL 75 3.5.2 AN vs BDD 76 3.6 Discussion 78 Chapter IV - Using insurance claims to predict and prevent IBD hospitalizations 82 4.1 Abstract 82 vii 4.2 Introduction 82 4.3 Methods and materials 86 4.3.1 Patient data 86 4.3.2 ClassiFication 88 4.3.3 Grouping 89 4.3.4 SigniFicance testing 90 4.3.5 Intervention 90 4.4 Results 91 4.4.1 Statistics 91 4.4.2 Feature extraction 91 4.4.3 Topic modeling 92 4.4.4 IBD hospitalization 93 Univariate analysis 93 Feature reduction 93 Multivariate analysis 94 Validation 99 Intervention 100 4.4.5 Biologics 102 Univariate analysis 102 Model optimization 103 Multivariate analysis 104 4.5 Discussion 106 4.5.1 Topics interpretation 108 4.5.2 Limitations 108 Alternative interventions 108 Social 109 Data 110 Applicability 110 4.5.3 Further directions 111 Chapter V – General discussion 112 5.1 Contributions to pure and applied science 112 viii 5.2 Circumstantial utility 114 5.2.1 Uncooperative patients 115 5.2.2 Unsatisfactory Forward models 116 5.3 Concluding remarks 119 Chapter VI - Appendix 122 6.1 Empathy 122 6.2 HBF 122 6.3 IBD 125 ReFerences 130 ix NOMENCLATURE 5-ASA 5-Aminosalicylic Acid ACC Anterior Cingulate Cortex ADA Adalimumab AI Anterior Insula AN Anorexia Nervosa ANN ArtiFicial Neural Network AR Autoregressive AUC Area Under the Curve AUC-ROC Area Under the ROC Curve AUC-PR Area Under the PR Curve BABS Brown Assessment oF BelieFs Scale BBR Boundary-Based Registration BDD Body Dysmorphic Disorder BEES Balanced Emotional Empathy Scale BET Brain Extraction Tool BMI Body Mass Index BOLD Blood-Oxygen-Level Dependent CBC Complete Blood Count CD Crohn’s Disease CPL Characteristic Path Length CPT Current Procedural Terminology CRP C-Reactive Protein CT Computed Tomography CTL Control CTZ Certolizumab DARPA DeFense Advanced Research Projects Agency DRG Diagnosis- Related Group DSM Diagnostic and Statistical Manual oF Mental Disorders DTI DiFFusor Tensor Imaging ED Emergency Department EEG Electroencephalography EPV Events per Explanatory Variable ESR Erythrocyte Sedimentation Rate FEAT FMRI Expert Analysis Tool FLAME FEAT Mixed EfFects Modeling FILM FMRIB’s Improved Linear Model FLIRT FMRIB’s Linear Image Registration Tool FMRI Functional Magnetic Resonance Imaging x FMRIB Oxford Centre For Functional MRI oF the Brain FNIRT FMIRB’s Non-linear Image Registration Tool fROI Functional network ROI FSL FMRIB’s SoFtware Library FWHM Full Width at Half-Minimum GE Gastroenterology/Gastroenterologist GLM General Linear Model HAM-A Hamilton Anxiety Rating Scale HDRS Hamilton Depression Rating Scale IBD InFlammatory Bowel Disease IC Independent Component ICA Independent Components Analysis ICA-AROMA ICA-based Automatic Removal oF Motion ArtiFacts ICD International ClassiFication oF Disease ICN Independent Component Network IFX InFliximab IRB Institutional Review Board ITI Inter-Trial Interval LatDA Latent Dirichlet Allocation LOR Log Odds Ratio MADRS Montgomery Asberg Depression Scale MATLAB Matrix Laboratory (Mathworks, Inc.) MCFLIRT Motion-Correction FLIRT MELODIC Multivariate Exploratory Linear Optimized Decomposition into Independent Components MNI Montreal Neurological Institute mPFC Medial PreFrontal Cortex MP-RAGE Magnetization Prepared Rapid Acquisition Gradient Echo MRI Magnetic Resonance Imaging MTX Methotrexate NAT Natalizumab NMF Non-negative Matrix Factorization OR Odds Ratio PCA Principal Components Analysis PE Parameter Estimate PET Positron Emission Tomography PPV Positive Predictive Value PR Precision-Recall ROC Receiver Operator Characteristic ROI Region oF Interest xi SD Standard Deviation SEM Standard Error oF the Mean SPM Statistical Parametric Mapping SVM Support Vector Machine TE Echo Time TMS Transcranial Magnetic Stimulation TNF Tumor Necrosis Factor TR Repetition Time UC Ulcerative Colitis UCLA University oF CaliFornia - Los Angeles YBC-EDS Yale-Brown-Cornell Eating Disorder Scale YBOCS Yale-Brown Obsessive Compulsive Scale xii ACKNOWLEDGEMENTS Chapter II is a version oF “Empathy is modulated by the religion oF another,” a manuscript in preparation with authors Don Vaughn, Ricky Savjani, Mark Cohen, and David Eagleman. Chapter III is a version oF “Predicting diagnoses oF anorexia nervosa and body dysmorphic disorder with multimodal neuroimaging and psychometric data,” a manuscript in preparation with authors Don Vaughn, Gigi Cheng, Teena Moody, AiFeng Zhang, Alex Leow, Michael Strober, Mark Cohen, and Jamie Feusner. Chapter IV is a version oF “Using insurance claims to predict and prevent hospitalizations and ineFFective use oF biologics in patients with inFlammatory bowel disease,” a manuscript in preparation with authors Don Vaughn, Welmoed K. van Deen, Wesley T. Kerr, Travis R. Meyer, Andrea Bertozzi, Daniel W. Hommes, Mark S. Cohen. Thank you to the UCLA QCBio Collaboratory and the UCLA-CaliFornia Institute oF Technology Medical Scientist Training Program (NIH T32 GM08042) and the William M. Keck Foundation For Funding portions oF my investigation. I would also like to acknowledge Sara For reminding me not to work all the time, Wesley Kerr For his mentorship and answering 1000 minutial questions; and Agatha Lenartowicz, Pamela Douglas, Ariana Anderson, Chess Stetson, Malina, Jenny Lee, and Iriss Brion. Thank you Greyhound and Southwest For thousands oF miles oF oFFice space. Y’all too, Starbucks and Peet’s. xiii BIOSKETCH Don has a BA in economics From StanFord. Don has a BS in physics From StanFord. Don worked in David Eagleman’s lab at Baylor College oF Medicine prior to his Ph.D. xiv Chapter I - Introduction I conducted my research at UCLA under the mentorship oF Dr. Mark Cohen. During this time, I worked on a number oF projects related to the Functional interpretation oF haemodynamics, including Functional near-inFrared spectroscopy, bias introduced into Functional magnetic resonance imaging (MRI) by compressive sensing reconstruction, and classiFication oF attention states using Functional MRI (fMRI). While working on classiFying attention, I began to comprehend the major role that machine learning will play in the Future oF neuroscience and medicine.