Connectomics in neuroImaging : second international workshop, CNI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : proceedings

This book constitutes the refereed proceedings of the Second International Workshop on Connectomics in NeuroImaging, CNI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 15 full papers presented were carefully reviewed and selected from 20 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications. Preface 6 Organization 7 Contents 8 Towards Ultra-High Resolution 3D Reconstruction of a Whole Rat Brain from 3D-PLI Data 10 1 Introduction 10 2 Method 12 2.1 Registration of High-Resolution 3D-PLI Data 12 2.2 Registration of Ultra-High Resolution 3D-PLI Data 15 3 Experimental Results 16 4 Conclusion 17 References 18 FOD-Based Registration for Susceptibility Distortion Correction in Connectome Imaging 20 1 Introduction 20 2 Methods 22 2.1 FOD Image Registration 23 2.2 Merging Data from Different PEs 24 3 Experimental Results 24 4 Conclusions and Discussion 27 References 27 GIFE: Efficient and Robust Group-Wise Isometric Fiber Embedding 29 1 Introduction 29 2 GIFE: Group-Wise Isometric Fiber Embedding 30 2.1 The Classical MDS 31 2.2 The Classical Multidimensional Extrapolating (cMDE) 31 3 Experimental Results 33 3.1 Randomly Sampled Tractograms 33 3.2 Common Optic Radiation Fiber Bundle Extraction 35 4 Conclusion 36 References 36 Multi-modal Brain Tensor Factorization: Preliminary Results with AD Patients 38 1 Introduction 38 2 Background 39 3 Method 40 3.1 B-Tensor Construction 40 3.2 B-Tensor Factorization 41 4 Experiments 42 4.1 Data 42 4.2 Analysis and Results 42 5 Discussion 44 6 Conclusion 45 References 45 Intact Connectional Morphometricity Learning Using Multi-view Morphological Brain Networks with Application to Autism Spectrum Disorder 47 1 Introduction 48 2 Intact Connectional Brain Morphometricity Learning 49 3 Results and Discussion 53 4 Conclusion 54 References 54 Neonatal Morphometric Similarity Networks Predict Atypical Brain Development Associated with Preterm Birth 56 1 Introduction 57 2 Methods 58 2.1 Participants and Data Acquisition 58 2.2 Data Preprocessing 59 2.3 Data Harmonization 60 2.4 Network Construction 60 2.5 Regression Model 60 2.6 Feature Selection 61 2.7 Measuring Brain Maturation 61 3 Results and Discussion 61 3.1 Data Harmonisation 61 3.2 Morphometric Similarity Networks 62 4 Conclusions 63 References 64 Heritability Estimation of Reliable Connectomic Features 67 1 Introduction 68 2 Method 68 3 Results 70 4 Conclusion 74 References 74 Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains 76 1 Introduction 76 2 Related Work and Technical Background 77 3 Methods 79 4 Results 81 5 Discussion 83 References 84 Riemannian Regression and Classification Models of Brain Networks Applied to Autism 87 1 Introduction 87 1.1 Contribution 88 2 Methods 88 2.1 SPD Matrices 89 2.2 ABIDE 90 3 Results 90 3.1 Classification 90 3.2 Regression 93 4 Conclusion 95 References 95 Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields 97 1 Introduction 97 2 A Bayesian Model for Voxel Reassignment 98 2.1 Approximate Posterior Inference 100 2.2 Implementation Details 100 2.3 Baseline Comparisons 101 3 Experimental Results 102 3.1 Evaluating Resting State Network Cohesion 102 3.2 Motor Network Concordance, as Validated by Task-fMRI 104 4 Conclusion 105 References 106 Data-Specific Feature Selection Method Identification for Most Reproducible Connectomic Feature Discovery Fingerprinting Brain States 108 1 Introduction 109 2 Multi-graph Based Identification of Data-Specific Feature Selection Method for Reproducible Discriminative Feature Discovery 110 3 Results and Discussion 112 4 Conclusion 114 References 114 Towards Effective Functional Connectome Fingerprinting 116 1 Introduction 116 2 Data 118 3 Methods 119 3.1 FC Fingerprinting 119 3.2 Studying the Effect of Sample Size 119 3.3 Studying the Effect of Granularity of Parcellation 120 3.4 Determining Elements of RSFC that are Highly Relevant for Fingerprinting 120 4 Results 122 4.1 The Effect of Sample Size on Fingerprinting 122 4.2 The Effect of Parcellation Granularity on Fingerprinting 122 4.3 Determining Elements of RSFC that Are Highly Relevant for Fingerprinting 123 5 Conclusion 124 References 125 Connectivity-Driven Brain Parcellation via Consensus Clustering 126 1 Introduction 126 2 Methods 127 2.1 Continuous Connectome 127 2.2 Graph Clustering 127 2.3 Consensus Clustering 128 2.4 Comparison Metrics 130 3 Experiments 132 3.1 Data Description 132 3.2 Experimental Pipeline 132 3.3 Results 132 4 Conclusion 134 References 134 GRAND: Unbiased Connectome Atlas of Brain Network by Groupwise Graph Shrinkage and Network Diffusion 136 Abstract 136 1 Introduction 137 2 Methods 138 2.1 Model the Manifold of Connectome Data Using a Hypergraph 138 2.2 Network Diffusion for Individual Brain Networks 139 2.3 Construction of Connectome Atlas by Groupwise Graph Shrinkage and Network Diffusion (GRAND) 140 3 Experiments 141 3.1 Experiment on Simulated Network Data 141 3.2 Experiment on Real Brain Network Data 142 4 Conclusions 144 References 144 Structural Subnetwork Evolution Across the Life-Span: Rich-Club, Feeder, Seeder 145 1 Introduction 145 2 Materials and Methods 147 2.1 Study Design and Patient Population 147 2.2 Group Connectomes and Rich-Club Organization 147 2.3 Subnetwork Definition 148 2.4 Network Analysis 148 3 Results 149 3.1 Group Connectome, Rich-Club and Subnetwork Definition 149 3.2 Subnetwork Network Analysis 151 4 Discussion 151 References 153 Author Index 155 Front Matter ....Pages I-X Towards Ultra-High Resolution 3D Reconstruction of a Whole Rat Brain from 3D-PLI Data (Sharib Ali, Martin Schober, Philipp Schlömer, Katrin Amunts, Markus Axer, Karl Rohr)....Pages 1-10 FOD-Based Registration for Susceptibility Distortion Correction in Connectome Imaging (Yuchuan Qiao, Wei Sun, Yonggang Shi)....Pages 11-19 GIFE: Efficient and Robust Group-Wise Isometric Fiber Embedding (Junyan Wang, Yonggang Shi)....Pages 20-28 Multi-modal Brain Tensor Factorization: Preliminary Results with AD Patients (Göktekin Durusoy, Abdullah Karaaslanlı, Demet Yüksel Dal, Zerrin Yıldırım, Burak Acar)....Pages 29-37 Intact Connectional Morphometricity Learning Using Multi-view Morphological Brain Networks with Application to Autism Spectrum Disorder (Alaa Bessadok, Islem Rekik)....Pages 38-46 Neonatal Morphometric Similarity Networks Predict Atypical Brain Development Associated with Preterm Birth (Paola Galdi, Manuel Blesa, Gemma Sullivan, Gillian J. Lamb, David Q. Stoye, Alan J. Quigley et al.)....Pages 47-57 Heritability Estimation of Reliable Connectomic Features (Linhui Xie, Enrico Amico, Paul Salama, Yu-chien Wu, Shiaofen Fang, Olaf Sporns et al.)....Pages 58-66 Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains (Keri L. Anderson, Jeffrey S. Anderson, Sourabh Palande, Bei Wang)....Pages 67-77 Riemannian Regression and Classification Models of Brain Networks Applied to Autism (Eleanor Wong, Jeffrey S. Anderson, Brandon A. Zielinski, P. Thomas Fletcher)....Pages 78-87 Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields (Naresh Nandakumar, Niharika S. D’Souza, Jeff Craley, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar et al.)....Pages 88-98 Data-Specific Feature Selection Method Identification for Most Reproducible Connectomic Feature Discovery Fingerprinting Brain States (Nicolas Georges, Islem Rekik, for the Alzheimers’s Disease Neuroimaging Initiative)....Pages 99-106 Towards Effective Functional Connectome Fingerprinting (Kendrick Li, Gowtham Atluri)....Pages 107-116 Connectivity-Driven Brain Parcellation via Consensus Clustering (Anvar Kurmukov, Ayagoz Musabaeva, Yulia Denisova, Daniel Moyer, Boris Gutman)....Pages 117-126 GRAND: Unbiased Connectome Atlas of Brain Network by Groupwise Graph Shrinkage and Network Diffusion (Guorong Wu, Brent Munsell, Paul Laurienti, Moo K. Chung)....Pages 127-135 Structural Subnetwork Evolution Across the Life-Span: Rich-Club, Feeder, Seeder (Markus D. Schirmer, Ai Wern Chung)....Pages 136-145 Back Matter ....Pages 147-147