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On the Nature of Neural Causality in Large-Scale Brain ON THE NATURE OF NEURAL CAUSALITY IN LARGE-SCALE BRAIN NETWORKS: FOUNDATIONS, MODELING, AND NONLINEAR NEURODYNAMICS by Michael Mannino A Dissertation Submitted to the Faculty of Charles E. Schmidt College of Science In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Florida Atlantic University Boca Raton, FL December 2018 Copyright 2018 by Michael Mannino ii ACKNOWLEDGEMENTS This work would really not have been possible without the many people have helped me a great deal along the way. I am so deeply indebted to my committee for their assistance, guidance, kindness, scientific brilliance, and support. Dr. Blanks was so very kind and helpful, and so thoughtful, right from the beginning. Her guidance these 5 years was just invaluable. And easily one of the most memorable courses I ever took was her TBI course! Dr. Jirsa’s scientific insight and intelligence was amazing to witness. I feel very grateful to have had him on my committee. In my first 2-3 years here, Dr. Barenholtz and I had many conversations which definitely changed my thinking! I remember one time he told me: “You are going to be doing so much computation, math and science, you will have forgotten the word ‘philosophy’!” He was right. Finally, Dr. Bressler has just been a true mentor. I am so grateful for the fact that he is so well versed in philosophy and that he was open to that part of my thinking. So many hours and hours of great conversation, learning so much about science, philosophy, life, and especially about myself. He willingness to guide me, teach me, and especially listen to me will not be forgotten. Dr.B, reality exists and is independent of our perception, at some level at least! iv I’ve had so many great discussions with people that sometimes even changed the course of my research! Conversations with Roxana Stefanesu were amazingly illuminating, they gave me…understanding. John Griffiths discussed at length with me so many necessary concepts for my research. I thank them both! I would like to thank all of my current and previous lab mates, especially Tim Meehan, who taught a great many things about our research and what its like to do PhD in neuroscience, and also Bryan Conklin and Tim West who very convincing at just the right times! And Keyla Thamsten, her superb support (moral as well as administrative) was instrumental during my time here! Rhona was so kind and helpful throughout these 5 years! I also extend a sincere “thank you” to Dr. Kelso, who founded the Center and the field of coordination dynamics, which had an immense impact on my thinking about so many things! All my parents (Mom, Bill, Dad and Ange) have been so supportive throughout the years. Words can never true convey how I feel about them, their love, and how they gave me the courage to continue and try to achieve my dreams. To my best friends, Jordi, James, and Don. I never could’ve done this without them. Jordi’s continued and unconditional encouragement was one of the things that really carried me through to completion! I will always remember that conversation on the beach we had so many years ago, during one of my (many) moments of doubt. Last, my wife Andrea. How can I ever thank her for the 5 years of sacrifices she made, giving me the chance to go after my dreams? Only by promising (sung in the voice of Captain Sisko) that, “The Best is Yet to Come.” Without her, none of v this would have been possible, or even conceivable. She has given me the strength, confidence, support I needed to be able to do this. vi ABSTRACT Author: Michael Mannino Title: On the Nature of Neural Causality in Large-Scale Brain Networks: Foundations, Modeling and Nonlinear Neurodynamics Institution: Florida Atlantic University Dissertation Advisor: Dr. Steven L. Bressler Degree: Doctor of Philosophy Year: 2018 We examine the nature of causality as it exists within large-scale brain networks by first providing a rigorous conceptual analysis of probabilistic causality as distinct from deterministic causality. We then use information-theoretic methods, including the linear autoregressive modeling technique of Wiener-Granger causality (WGC), and Shannonian transfer entropy (TE), to explore and recover causal relations between two neural masses. Time series data were generated by Stefanescu-Jirsa 3D model of two coupled network nodes in The Virtual Brain (TVB), a novel neuroinformatics platform used to model resting state large-scale networks with neural mass models. We then extended this analysis to three nodes to investigate the equivalence of a concept in probabilistic causality known as ‘screening off’ with a method of statistical ablation known as conditional vii Granger causality. Finally, we review some of the empirical and theoretical work of nonlinear neurodynamics of Walter Freeman, as well as metastable coordination dynamics and investigate what impact they have had on consciousness research. viii DEDICATION This dissertation is dedicated to my mother, Marcy. This would not have been attainable without her unwavering encouragement, support, unconditional love, and compassion. ON THE NATURE OF NEURAL CAUSALITY IN LARGE-SCALE BRAIN NETWORKS: FOUNDATIONS, MODELING, AND NONLINEAR NEURODYNAMICS LIST OF TABLES ............................................................................................... xiv LIST OF FIGURES ..............................................................................................xv LIST OF EQUATIONS ........................................................................................ xix CHAPTER 1: INTRODUCTION ............................................................................ 1 1.1 Neurocognitive Networks: A Paradigm Shift ............................................... 1 1.2 Modes of Brain Connectivity ....................................................................... 2 1.3 The need for computational modeling in brain research and the role of Brain Simulation ......................................................................................... 4 1.4 Two-Fold Approach: Nonlinear Dynamical Modeling in The Virtual Brain and Information Theoretic Causal Analysis ...................................... 5 1.5 Overview of dissertation .............................................................................. 7 CHAPTER 2: FOUNDATIONAL PERPSECTIVES ON CAUSALITY IN LARGE SCALE BRAIN NETWORKS ......................................................... 10 2.1 Introduction ............................................................................................... 10 2.2 Concepts of Causality ............................................................................... 13 2.3 The Ontology and Epistemology of Causality: Aristotle, Hume, Kant, and Russell .............................................................................................. 15 x 2.3 Causality in Classical and Modern Physics ............................................... 19 2.4. The Distinction Between Deterministic and Probabilistic Causality .......... 26 2.5. Causality in Complex Systems ................................................................. 30 2.6. Causality in the Brain ............................................................................... 32 2.7 Causality in Large-Scale Brain Networks .................................................. 34 2.8 Quantification of Probabilistic Causality in the Brain ................................. 38 2.9 Conclusions .............................................................................................. 46 CHAPTER 3: MEASURING CAUSALITY IN LARGE-SCALE BRAIN NETWORKS AT REST: NEURONAL POPULATION MODELING WITH THE VIRTUAL BRAIN ......................................................... 49 3.1 Introduction ............................................................................................... 49 3.2 Results ...................................................................................................... 52 SJ3D Model Output of Summed Modes ...................................................... 52 Parametric Variation .................................................................................... 57 K12 (excitatory to inhibitory connectivity) ...................................................... 58 K21 (inhibitory to excitatory connectivity) ...................................................... 59 K11 (excitatory to excitatory connectivity) ..................................................... 59 r (adaptation parameter controlling slow variables) ..................................... 60 Conduction Delay ....................................................................................... 63 Coupling Scaling Factor (Global Coupling Strength) ................................... 64 3.3 Discussion................................................................................................. 66 3.4 Methods and Modeling .............................................................................. 72 Modeling with The Virtual Brain (TVB) ........................................................ 72 xi The Neural Mass Model, local and global parameterizations ...................... 73 Causal Analysis ........................................................................................... 79 3.5 Conclusion ................................................................................................ 87 CHAPTER 4: ANALYZING CAUSAL RELATIONS BETWEEN THREE NODES USING THE VIRTUAL BRAIN....................................................... 88 4.1 Introduction
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