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Detecting and Characterizing Large-Scale Human Brain DETECTING AND CHARACTERIZING LARGE-SCALE HUMAN BRAIN NETWORKS A DISSERTATION SUBMITTED TO THE PROGRAM IN BIOMEDICAL INFORMATICS AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Kaustubh Satyendra Supekar August 2010 © 2010 by Kaustubh Satyendra Supekar. All Rights Reserved. Re-distributed by Stanford University under license with the author. This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/ This dissertation is online at: http://purl.stanford.edu/pm061tq8052 ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Mark Musen, Primary Adviser I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Michael Greicius I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Vinod Menon I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Daniel Rubin Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost Graduate Education This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives. iii Abstract Understanding human brain function is one of the most important endeavors in mod- ern science. There is growing evidence that cognitive functions are executed by large- scale networks, comprising multiple interacting anatomically-connected brain areas. Al- though considerable progress has been made in understanding which specific brain areas are involved in particular cognitive functions, very little is known about the integrative functioning of large-scale brain networks. This is due in part to the lack of methods to pursue this line of research. This dissertation describes computational methods for de- tecting and characterizing large-scale human brain networks, combining data from task- free functional magnetic resonance imaging (fMRI) and structural diffusion tensor imag- ing (DTI), two complementary brain imaging modalities. Application of our methods to task-free fMRI and DTI data obtained from a wide range of subject populations pro- vided new insights into how large-scale human brain networks develop, mature, and get disrupted in psychiatric and neurological disorders. More generally, this work demon- strates the power of our multimodal network-analytic approach to obtain a system-level understanding of brain function across the human lifespan. iv Acknowledgements I thank my committee: Mark Musen, Vinod Menon, Daniel Rubin, and Michael Gri- sious. The mentorship, and more importantly, the incredible support and encourage- ment I have received from them, throughout the process, has been truly inspiring and awesome. I am grateful to my colleagues and collaborators: Bob Dougherty, Booil Jo, Catie Chang, Elena Rykhlevskaia, Hitha Amin, Leeza Kondos, Lucina Uddin, Meghan Meyer, Sridhar Devarajan, and Srikanth Ryali. I acknowledge Bruce Miller, Gary Glover, Jose Anguiano, and Katherine Prater for their assistance with participant recruitment and data collection. I am also grateful to Carol Maxwell, Christine Hilliard, and MaryJeanne Oliva for their compassion and care. I thank my friends and BMI students, including, but not limited to, Alex Morgan, Amit Kaushal, Chintan Patel, Holger Lewen, Madhura Maideo, Marina Sirota, Maureen Hillenmeyer, Mayura Bhandarkar, Nigam Shah, Nikesh Kotecha, Noah Zimmerman, Ray Lin, Rong Xu, and Seema Joshi. Last, but certainly not least, I would like to thank my mom, dad, and sister: without your unconditional love and support, this would have not been possible; I dedicate this dissertation to you. v Contents Abstract iv Acknowledgements v 1 Introduction 1 2 Background and Methods 10 2.1 Detecting large-scale human brain networks . 11 2.1.1 Large-scale human brain networks . 11 2.1.2 Task-free fMRI . 14 2.1.3 Identifying nodes . 15 2.1.4 Identifying edges between the nodes . 35 2.2 Quantifying the topology of large-scale human brain networks . 43 2.2.1 Global topology . 48 2.2.2 Subnetwork topology . 54 2.2.3 Regional topology . 55 2.2.4 Statistical analysis . 58 vi 2.3 Assessing structure–function relationships in large-scale human brain net- works ............................................ 59 2.3.1 Measuring structural connectivity underlying large-scale human brain networks using DTI . 61 2.3.2 Correlating large-scale human brain networks with measures of structural connectivity . 66 2.4 Comparing large-scale human brain networks across subject populations 66 2.4.1 Comparing network topology . 68 2.4.2 Comparing brain connectivity . 71 2.5 Summary . 74 3 Large-Scale Brain Networks in Alzheimer’s Disease 75 3.1 Abstract . 75 3.2 Introduction . 76 3.3 Materials and Methods . 78 3.4 Results . 86 3.4.1 Comparison of global topology of large-scale brain networks in AD participants and age-matched healthy controls . 87 3.4.2 Specificity and sensitivity of global topological metrics in distin- guishing AD participants from age-matched healthy controls . 93 3.4.3 Comparison of regional connectivity of large-scale brain networks in AD participants and age-matched healthy controls . 93 3.4.4 Reproducibility of findings . 95 vii 3.5 Discussion . 95 4 Development of Large-Scale Brain Networks in Children 99 4.1 Abstract . 100 4.2 Introduction . 101 4.3 Materials and Methods . 104 4.4 Results . 115 4.4.1 Comparison of global topology of large-scale brain networks in children and young-adults . 116 4.4.2 Comparison of global connectivity of large-scale brain networks in children and young-adults . 118 4.4.3 Comparison of subnetwork topology of large-scale brain networks in children and young-adults . 120 4.4.4 Comparison of subnetwork connectivity of large-scale brain net- works in children and young-adults . 122 4.4.5 Comparison of regional topology of large-scale brain networks in children and young-adults . 122 4.4.6 Comparison of regional connectivity of large-scale brain networks in children and young-adults . 124 4.4.7 Developmental changes in regional connectivity with wiring dis- tance . 124 4.4.8 Comparison of structure–function relationships within large-scale brain networks in children and young-adults . 127 viii 4.4.9 Comparison of large-scale brain networks consisting of functionally- defined nodes in children and young-adults . 130 4.5 Discussion . 131 5 Conclusions and Future Directions 142 5.1 Conclusions . 142 5.2 Future directions . 145 ix List of Tables 2.1 Comparative analysis of widely used anatomical atlases against desiderata of a brain parcellation scheme . 22 2.2 Simulated datasets . 30 2.3 Percent error in partitioning voxels into correct clusters . 33 3.1 Demography and MMSE scores . 86 4.1 Demographic and cognitive profile. 115 4.2 Brain regions identified as network hubs in children and young-adults. 125 x List of Figures 2.1 Flowchart depicting steps involved in detecting large-scale human brain networks using task-free fMRI data. 12 2.2 Schematic representation of brain network as a graph. 13 2.3 Results of a typical fMRI study. 14 2.4 Correlated spontaneous activity pattern observed during the task-free con- dition . 16 2.5 Anatomy-based schemes to parcellate the human brain . 20 2.6 A simulated 64x64 image slice with 2 regions shown with two representa- tive voxel timeseries. 32 2.7 Illustrative example of application of wavelet transform . 38 2.8 Flowchart depicting steps involved in identifying an edge between a pair of nodes . 40 2.9 Small-world network (0 < p < 1) as an intermediate state between regular lattice-like network (p = 0) and random network (p = 1). 47 2.10 Illustration of key network properties. 48 xi 2.11 Flowchart depicting steps involved in quantifying structural connectivity between nodes of large-scale human brain networks. 63 3.1 Global organization of large-scale brain networks in AD participants and controls, at three frequency intervals of interest. 88 3.2 Global organization of large-scale brain networks in AD participants and controls, at the low frequency interval. 89 3.3 Regional organization of large-scale brain networks in AD participants and controls. 91 3.4 Global efficiency of large-scale brain networks in AD participants and controls. 92 3.5 Specificity and sensitivity of quantitative metrics of global organization of large-scale brain networks in distinguishing AD participants from con- trols. 94 4.1 Developmental changes in global organization of large-scale brain networks.117 4.2 Developmental changes in hierarchical organization of large-scale brain networks. 119 4.3 Developmental changes in subnetwork organization of large-scale brain networks. 121 4.4 Developmental changes in subnetwork connectivity of large-scale brain networks. 123 4.5 Regional connectivity in children and young-adults. 126 xii 4.6 Developmental changes in regional connectivity with DTI-based wiring distance. 128 4.7 Structure–function relationships within large-scale brain networks in chil- dren and young-adults. 129 xiii Chapter 1 Introduction Understanding human brain function is one of the most important endeavors in modern science. It is fascinating that 1.5 liters of tissue, can give rise to complex human behaviors. The earliest notable systematic investigation of human brain function was performed by German physician Franz Gall. In the early 19t h century, Gall attempted to ascribe cognitive functions onto specific brain areas by relating personality traits with the size of bumps and fissures in the skull (Gall and Spurzheim, 1810).
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