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Towards a Brain Imaging Biomarker for Parkinson’S Disease

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Towards a Brain Imaging Biomarker for Parkinson’S Disease Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson’s Disease Amir Dehsarvi Doctor of Philosophy University of York Electronic Engineering January 2018 To my Jennifer & our soon to be born baby و به پدر و مادر نازنینم با عشق... Abstract It is commonly accepted that accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson’s disease (PD) by considering the novel application of evolutionary algorithms. An additional novel element of this work is the use of evolutionary algorithms to both map and predict the functional connectivity in patients using rs-fMRI data. Specifically, Cartesian Genetic Programming was used to classify dynamic causal modelling data as well as timeseries data. The findings were validated using two other commonly used classification methods (Artificial Neural Networks and Support Vector Machines) and by employing k-fold cross-validation. Across dynamic causal modelling and timeseries analyses, findings revealed maximum accuracies of 75.21% for early stage (prodromal) PD patients in which patients reveal no motor symptoms versus healthy controls, 85.87% for PD patients versus prodromal PD patients, and 92.09% for PD patients versus healthy controls. Prodromal PD patients were classified from healthy controls with high accuracy – this is notable and represents the key finding since current methods of diagnosing prodromal PD have low reliability and low accuracy. Furthermore, Cartesian Genetic Programming provided comparable performance accuracy relative to Artificial Neural Networks and Support Vector Machines. Nevertheless, evolutionary algorithms enable us to decode the classifier in terms of understanding the data inputs that are used, more easily than in Artificial Neural Networks and Support Vector Machines. Hence, these findings underscore the relevance of both dynamic causal modelling analyses for classification and Cartesian Genetic Programming as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early stages 5-20 years prior to motor symptoms. Keywords: Evolutionary Algorithms; Cartesian Genetic Programming; Classification; Parkinson’s Disease; Prodromal Parkinson’s Disease; Resting-state fMRI; Dynamic Causal Modelling. 5 Table of Contents Abstract ..................................................................................................................................5 Table of Contents ...................................................................................................................7 List of Figures ......................................................................................................................15 List of Tables .......................................................................................................................19 Acknowledgements ..............................................................................................................21 Author’s Declaration ............................................................................................................23 Chapter 1. Introduction ..................................................................................................29 1.1. Research Aims .....................................................................................................31 1.2. Key Novel Aspects Examined in this Thesis .......................................................33 1.3. Outline of Thesis Chapters ...................................................................................34 Chapter 2. Parkinson’s Disease .....................................................................................37 2.1. Alzheimer’s Disease (AD) ...................................................................................39 2.2. Huntington’s Disease (HD) ..................................................................................40 2.3. Parkinson’s Disease (PD) .....................................................................................41 2.3.1. Historical Overview .....................................................................................41 2.3.2. Epidemiology ...............................................................................................42 7 2.3.3. Pathology ..................................................................................................... 43 2.3.4. Financial Costs ............................................................................................. 45 2.3.5. Clinical Phenotype ....................................................................................... 46 2.3.6. Prodromal PD .............................................................................................. 49 2.3.7. Assessment .................................................................................................. 49 2.3.8. Diagnosis ..................................................................................................... 51 2.3.9. Treatment ..................................................................................................... 55 2.3.10. Medication ................................................................................................... 57 2.3.11. Monitoring ................................................................................................... 60 2.3.12. Diagnostic Tools .......................................................................................... 60 2.3.13. PD Biomarkers ............................................................................................. 62 2.4. Summary .............................................................................................................. 78 Chapter 3. Functional Magnetic Resonance Imaging (fMRI) ....................................... 79 3.1. MRI ...................................................................................................................... 81 3.1.1. The BOLD Effect and Hemodynamic Response ......................................... 84 3.2. fMRI .................................................................................................................... 85 3.3. Resting-State fMRI (rs-fMRI) ............................................................................. 90 8 3.3.1. Default Mode Network (DMN) ...................................................................92 3.4. fMRI Data Analysis .............................................................................................95 3.4.1. Preprocessing ...............................................................................................96 3.5. Processing ..........................................................................................................105 3.5.1. Modelling Brain Connectivity ...................................................................105 3.5.2. Dynamic Causal Modelling (DCM) ...........................................................108 3.6. Summary ............................................................................................................110 Chapter 4. Computational Intelligence ........................................................................111 4.1. Predictive Modelling ..........................................................................................113 4.1.1. Classification ..............................................................................................113 4.1.2. Learning Algorithms ..................................................................................115 4.2. Computational Intelligence (CI) ........................................................................121 4.2.1. Artificial Neural Networks (ANN) ............................................................121 4.2.2. Genetic Programming (GP) .......................................................................122 4.2.3. Cartesian Genetic Programming (CGP) .....................................................130 4.3. Computational Intelligence Applied to Predictive Modelling ...........................134 4.4. Generalisation ....................................................................................................136 9 4.5. Classification of Timeseries Data ...................................................................... 139 4.6. Classification with Dynamical Systems ............................................................ 141 4.7. Imbalanced Data ................................................................................................ 142 4.7.1. Random Oversampling and Undersampling .............................................. 143 4.7.2. Informed Undersampling ........................................................................... 144 4.7.3. Synthetic Sampling with Data Generation (SMOTE) ............................... 144 4.7.4. Adaptive Synthetic Sampling (ADASYN) ................................................ 145 4.8. CGP Classification ............................................................................................. 145 4.9. Research Applying EAs to Classification of Medical Data ............................... 150 4.10. Computational Intelligence Approaches for Diagnosing Parkinson’s Disease 150 4.10.1. Kinematic Research ..................................................................................
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