Whole-brain cortical panellation: A hierarchical method

based on dMRI tractography

DISSERTATION

in partial fulfillment of the requirements for the degree of

Doctor of Engineering Doktoringenieur (Dr.-Ing.)

developed in the Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Methods & Development Unit "Cortical Networks and Cognitive Functions"

submitted to the Faculty of Computer Science and Automation of the Ilmenau University of Technology

[Fakultät für Informatik und Automatisierung der Technischen Universität Ilmenau)

by Dipl.-Ing. David Moreno-Dominguez born on 16. November 1983 in Valladolid, Spain

Date of Submission: 19. May 2014

1. 2. 3.

Reviewers:

VII

TABLE OF CONTENTS

Abstract I

Zusammenfassung III

Abbreviations V

Table of contents VII

1. Introduction 1 1.1 Overview 1 1.2 The human brain 1 1.3 Structural mapping and parcellation 4 1.4 Anatomical connectivity 5

1.4.1 Connectivity as a structural trait 5 1.4.2 The organization of white matter 6 1.4.3 Measuring anatomical connectivity 7

1.5 Connectivity based brain parcellation 9 1.6 Main contributions and overview of this thesis 10

2. Brain analysis based on water diffusion measured by MRI 15 2.1 Overview 15 2.2 dMRI imaging 15

2.2.1 Basics of MRI 15 2.2.2 Measuring diffusion 17 2.2.3 Modeling fiber orientation 20

2.2.3.1 The diffusion tensor 20 2.2.3.2 Modeling multiple fibers 22

2.3 Tractography 24 2.3.1 Deterministic tractography 24 2.3.2 Probabilistic tractography 26 2.3.3 Global tractography 28

2.4 dMRI connectivity based parcellation 29 2.4.1 Basis of connectivity based parcellation 29 2.4.2 Review of current dMRI-connectivity based parcellation methods . . . . 29 2.4.3 Limitations of the current methods and motivation for this work 34

3. A hierarchical method for whole-brain 37 connectivity-based panellation 3.1 Overview 37 3.2 Data acquisition and preprocessing 37 3.3 Tractogram distance measure 40 3.4 Hierarchical clustering 42

3.4.1 Agglomerative vs. divisive hierarchical clustering 42 3.4.2 Graph methods 43 3.4.3 Centroid method 44

3.4.3.1 Basic method 44 3.4.3.2 Neighborhood restriction 45 3.4.3.3 Homogeneous merging 46

3.4.4 The hierarchical tree or dendrogram 47 3.5 Assessing the quality of the trees 48 3.6 Tree processing 48

3.6.1 Confounds and challenges for dendrogram interpretation 48 3.6.2 Dendrogram preprocessing pipeline 50

3.7 Dendrogram comparison 53 3.7.1 Introduction 53 3.7.2 Leaf matching across trees 53 3.7.3 Matching quality 54 3.7.4 Tree cophenetic correlation coefficient [tCPCC] 55 3.7.5 Weighted triples similarity (wTriples) 56 3.7.6 Restricting compared structure based on matching quality 56

3.8 Interpretation of hierarchical information 57 3.8.1 Introduction 57 3.8.2 Partition extraction algorithms 57

3.8.2.1 Dendrogram partitioning 57 3.8.2.2 Minimum guaranteed intra-cluster similarity (Horizontal cut) . . 58 3.8.2.3 Cluster spread vs. separation [SS] index 58 3.8.2.4 Minimum cluster size difference 59 3.8.2.5 Efficient hierarchical search of partitions 59 3.8.2.6 Stable boundaries 60

3.8.3 Visualization of clustering results 60 3.8.4 Interactive hierarchical exploration module 61 3.8.5 Partition color matching across datasets 62

IX

4. A proof-of-principle study on multi-granularity dMRI-based 64 whole-brain characterization 4.1 Overview 64 4.2 Selecting a linkage method 64 4.3 Cleaning the dendrograms 68 4.4 Comparing whole connectivity structure across datasets 69 4.5 Exploring the hierarchy: single subject partitioning 74

5. Approaches and challenges in validation of tractography-based clustering 81 5.1 Overview 81 5.2 The challenge of verification and validation in brain dMRI based methods . . 81

5.2.1 Introduction 81 5.2.2 Verification and validation in tractography 81 5.2.3 Verification and validation in tractography-based clustering 83

5.3 Circumstantial validation of our clustering algorithm 85

6. Discussion 89 6.1 Tractography-based parcellation 89 6.2 Advantages and limitations of hierarchical clustering 90 6.3 Meta-leaf matching 92 6.4 Tree comparison 93 6.5 Extraction of partitions 93 6.6 Relationship to other multi-granularity methods 94 6.7 Fine-tuning of parameters 96 6.8 Biological validity 96

7. Summary and outlook 99

Publications of the author arising from this work 103

References 105

Acknowledgements 115

List of Figures 117

Erklärung 119

Appendix 121