Afferents Integration and Neural Adaptive Control of Breathing by Chung Tin B.Eng., Mechanical Engineering, The University of Hong Kong, 2002 S.M., Mechanical Engineering, Massachusetts Institute of Technology, 2004 SUBMITTED TO THE DEPARTMENT OF MECHANICAL ENGINEERING IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN MECHANICAL ENGINEERING AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY JUNE 2011 @ 2011 Massachusetts Institute of Technology. All rights reserved. Signature of A uthor ..................... ....... .... ...* .. ........ ................................... Dep a ent of Mechanical Engineering May 19,2011 C ertified by ........................................... ............... .... ........................................... / Chi-Sang Poon Principal Research Scientist of Health Sciences & Technology Thesis Supervisor C ertified by ................................9 .- .. ............ y .... ... .................................... Neville Hogan Sun Jae Professor of Mechanical Engineering Professor of Brain and Cognitive Sciences TesiComnuittee Chair A ccepted by ................................................................. ... ............................................ David E. Hardt Ralph E. and Eloise F. Cross Professor of Mechanical Engineering Chairman, Committee on Graduate Students Afferents Integration and Neural Adaptive Control of Breathing by Chung Tin Submitted to the Department of Mechanical Engineering On May 19, 2011, in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Mechanical Engineering Abstract The respiratory regulatory system is one of the most extensively studied homeostatic systems in the body. Despite its deceptively mundane physiological function, the mechanism underlying the robust control of the motor act of breathing in the face of constantly changing internal and external challenges throughout one's life is still poorly understood. Traditionally, control of breathing has been studied with a highly reductionist approach, with specific stimulus-response relationships being taken to reflect distinct feedback/feedforward control laws. It is assumed that the overall respiratory response could be described as the linear sum of all unitary stimulus-response relationships under a Sherringtonian framework. Such a divide-and-conquer approach has proven useful in predicting the independent effects of specific chemical and mechanical inputs. However, it has limited predictive power for the respiratory response in realistic disease states when multiple factors come into play. Instead, vast amounts of evidence have revealed the existence of complex interactions of various afferent-efferent signals in defining the overall respiratory response. This thesis aims to explore the nonlinear interaction of afferents in respiratory control. In a series of computational simulations, it was shown that the respiratory response in humans during muscular exercise under a variety of pulmonary gas exchange defects is consistent with an optimal interaction of mechanical and chemical afferents. This provides a new understanding on the impacts of pulmonary gas exchange on the adaptive control of the exercise respiratory response. Furthermore, from a series of in- vivo neurophysiology experiments in rats, it was discovered that certain respiratory neurons in the dorsolateral pons in the rat brainstem integrate central and peripheral chemoreceptor afferent signals in a hypoadditive manner. Such nonlinear interaction evidences classical (Pavlovian) conditioning of chemoreceptor inputs that modulate the respiratory rhythm and motor output. These findings demonstrate a powerful gain modulation function for control of breathing by the lower brain. The computational and experimental studies in this thesis reveal a form of associative learning important for adaptive control of respiratory regulation, at both behavioral and neuronal levels. Our results shed new light for future experimental and theoretical elucidation of the mechanism of respiratory control from an integrative modeling perspective. Thesis Supervisor: Chi-Sang Poon Principle Research Scientist of Health Sciences & Technology Thesis Committee: Neville Hogan (Thesis Committee Chair) Sun Jae Professor of Mechanical Engineering Professor of Brain and Cognitive Sciences Kamal Youcef-Toumi Professor of Mechanical Engineering Acknowledgements Earning a PhD. Degree is hard, especially from MIT. It would have been impossible for me without the help and support of many people throughout the process. I would like to first thank my advisor, Dr. Chi-Sang Poon, who offered me the opportunity to pursue a research project totally distinct from my previous background. Furthermore, his advice and guidance throughout these years have paved the road for me towards becoming an independent scientist. I would also like to thank Prof. Neville Hogan and Prof. Kamal Youcef-Toumi for their invaluable feedback and guidance in shaping the thesis in its best possible form. Members of Poon Lab have been wonderful. Dr. Gang Song has taught me all the neuroscience, physiological and experimental techniques that open my road towards biological research. Thank to Dr. Yunguo Yu for teaching me the skills for performing multielectrode recording, as well as for being a good friend. I am indebted to Dr. Ping Yu and Martha Adams for their generous help on carrying out the experiments. Dr. Paula Feinberg-Zadek has been very kind to help me proofread my thesis writing. I would also like to thank all the past and present members of Poon Lab for their friendship and support all these years. It has been a great time working with all of you. I must thank our beloved Leslie Regan at the Graduate Office of Mechanical Engineering for her help and care in many occasions through my life as a graduate student in MIT. I would like to thank the Croucher Foundation and American Heart Associative for their generous financial support for my graduate study. I have been very lucky to meet so many fantastic friends here who have kept me moving toward the finish line. My special thank to Sally Kwok and Edmond Lee and their baby Natalie. Sally and Edmond have been like brother and sister to me and they treat me like one of their family. Natalie has been one of the best cheerleaders for me since she was born. I owe countless emotional, as well as nutritional, support from them. My special thanks also go to Samuel Au, who has been both a mentor and a friend to me. Thank to Sam for helping me to settle down in MIT when I first came. We have had enormous sharing both in science and in life over the years. I will always remember the fun times when we went playing soccer, snowboarding and hiking together. My life here would have been a very different story without the friendship from all of you: Patrick Sit, Louis Wong, Ivy Lee, Raymond Lam, Ming Yan Poon, Preston Li, Albert Kong, Albert Lok, David Choa, Ricky Tong, Victor Yeung and many more. Thanks for your company and support and fun. Thanks to my friends in Hong Kong and anywhere in the world. Thanks for being my friends and for caring about me since the day we met. I owe my deepest gratitude to my parents. They have always offered me the best they could since the very first day and they support unconditionally every move I have made in life. I can never thank you enough. Chung Tin Cambridge, Massachusetts May 2011 Table of Contents Chapter 1 Introduction...........................................................................15 1.1 THESIS OUTLINE ........................................................................................ 18 1.2 REFERENCES............................................................................................... 19 Chapter 2 Background .......................................................................... 21 2.1 RESPIRATORY FEEDBACK ..................................................................... 21 2.1.1 Mechanical Feedback............................................................................. 22 2.1.2 Chemical Feedback................................................................................. 23 2.2 BRAINSTEM RESPIRATORY CONTROL CENTER................ 23 2.2.1 Dorsal respiratory group (DRG)............................................................ 24 2.2.2 Ventral respiratory group (VRG)............................................................. 24 2.2.3 Pontine respiratory region...................................................................... 25 2.3 Traditional view of respiratory control and limitations ................................. 26 2.4 Evidence of intelligent respiratory control.................................................... 27 2.4.1 Nonassociative learning in the chemical and mechanical feedback pathways 28 2.4.2 Clinical implications from models of nonassociative learning............... 29 2.4.3 Associative interaction of vagal and carotid chemoafferent inputs...... 30 2.5 Summary ........................................................................................................ 30 2.6 REFERENCES............................................................................................... 31 Chapter 3 Integrative and Reductionist Approaches to Modeling of Control of Breathing............................................................................... 37 3.1 INTROD U CTION ........................................................................................... 37 3.2 REDUCTIONIST VIEW OF BIOLOGICAL