Connectomics in NeuroImaging : Third International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings

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Preface 6 Organization 8 Contents 9 Unsupervised Feature Selection via Adaptive Embedding and Sparse Learning for Parkinson's Disease Diagnosis 11 1 Introduction 11 2 Methodology 13 2.1 System Overview 13 2.2 Notation 13 2.3 Proposed Method 14 3 Experiments 15 3.1 Image Preprocessing 15 3.2 Experimental Setting 16 3.3 Classification Performance 16 3.4 Regression Performance 18 4 Conclusion 19 References 19 A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity 20 1 Introduction 20 2 A Graph Neural Network for Node Identification 22 2.1 Baseline Comparisons 24 3 Experimental Results 25 3.1 Motor Class Identification 26 3.2 Language Class Identification 27 4 Conclusion 29 References 29 Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia States 31 1 Introduction 31 2 Method 33 2.1 Proposed Graph-Based Structural Element Matching Technique 34 2.2 Morphological Operators 35 2.3 Graph Morphology Based Genetic Algorithm (GMGA) 36 3 Results and Discussion 37 4 Conclusion 40 References 40 Covariance Shrinkage for Dynamic Functional Connectivity 42 1 Introduction 42 2 Covariance Shrinkage for dFC 43 2.1 EWMA-Based dFC Estimation Using Continuous Sliding Windows 43 2.2 Linear Covariance Shrinkage 44 2.3 Efficient Implementation 44 3 Experiments 45 3.1 Data Sets 45 3.2 Implementations 47 3.3 Findings on the Synthetic Data 47 3.4 Results on the Real rs-fMRI Data 47 3.5 Networks Extraction from High Resolution Data 49 4 Conclusion 50 References 51 Rapid Acceleration of the Permutation Test via Transpositions 52 1 Introduction 52 2 Preliminary 53 3 Methods 54 4 Application 60 5 Discussion 61 References 62 Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism 64 1 Introduction 65 2 Materials and Methods 66 2.1 Subjects and rs-FMRI Data Preprocessing 66 2.2 Group Connectomes, Hub Organisation and Subnetworks 66 2.3 Computing Heat Kernels and Their Features 66 2.4 Experimental Design 67 3 Results 68 4 Discussion 69 References 72 A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences 74 1 Introduction 74 2 Related Works 75 3 Methods 75 3.1 Hotelling's T2 Test 75 3.2 Mass Univariate Edge-wise Analysis 77 3.3 Mass Multivariate Edge-wise Analysis 77 4 Experiments 78 4.1 Datasets 78 4.2 Preprocessing 78 4.3 Evaluation and Competing Methods 78 4.4 Visualization of Anatomical Locations of Significant Edges 79 5 Results 79 6 Discussion and Conclusions 81 References 82 Adversarial Connectome Embedding for Mild Cognitive Impairment Identification Using Cortical Morphological Networks 84 1 Introduction 85 2 Method 86 3 Results and Discussion 89 4 Conclusion 91 References 91 A Machine Learning Framework for Accurate Functional Connectome Fingerprinting and an Application of a Siamese Network 93 1 Introduction 93 2 Data 95 3 Methods 95 3.1 FC Generation 95 3.2 ML Framework for FC Fingerprinting 95 3.3 Siamese Networks 96 3.4 Graph Generation 98 3.5 Siamese GCN Implementation 99 3.6 Traditional Classification Techniques 99 4 Results 100 4.1 FC Fingerprinting Performance 100 4.2 Exploring the Similarity Computation Component of Siamese Network 101 5 Conclusion 102 References 103 Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation 105 1 Introduction 105 2 Data 107 3 Methods 107 3.1 Computing Parcellations 107 3.2 Computing Test-Retest Reliability Using ICC 108 4 Results 109 4.1 Impact of Parcellation on Test-Retest Reliability 109 4.2 Impact of Granularity of Parcellation on Test-Retest Reliability 112 5 Conclusion 113 References 113 Constraining Disease Progression Models Using Subject Specific Connectivity Priors 116 1 Introduction 116 2 Event-Based Models and the Connectivity Prior 117 2.1 The Event-Based Model 117 2.2 Connectome Prior via Path Probability 118 2.3 Optimizing 119 3 Experiments 120 3.1 Data 120 3.2 Experimental Pipeline 122 4 Results 123 5 Conclusion 124 References 125 Hemodynamic Matrix Factorization for Functional Magnetic Resonance Imaging 127 1 Introduction 128 2 Hemodynamic Matrix Factorization 128 3 Experiments and Results 130 3.1 Experimental Setup 130 3.2 Neural Activation 131 3.3 Comparison of HMF and ICA 131 4 Discussion 134 References 135 Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An Application to Autism 136 1 Introduction 137 2 Materials and Methods 138 2.1 Study Design and Patient Population 138 2.2 RsfMRI Preprocessing and Group Connectomes 138 2.3 Network Dependency Index Subnetworks 139 2.4 Network Measures 139 2.5 Statistical Analysis 140 3 Results 140 4 Discussion 142 References 146 Author Index 148 Front Matter ....Pages i-x Unsupervised Feature Selection via Adaptive Embedding and Sparse Learning for Parkinson’s Disease Diagnosis (Zhongwei Huang, Haijun Lei, Guoliang Chen, Shiqi Li, Hancong Li, Ahmed Elazab et al.)....Pages 1-9 A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity (Naresh Nandakumar, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman)....Pages 10-20 Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia States (Oumaima Ben Khelifa, Islem Rekik)....Pages 21-31 Covariance Shrinkage for Dynamic Functional Connectivity (Nicolas Honnorat, Ehsan Adeli, Qingyu Zhao, Adolf Pfefferbaum, Edith V. Sullivan, Kilian Pohl)....Pages 32-41 Rapid Acceleration of the Permutation Test via Transpositions (Moo K. Chung, Linhui Xie, Shih-Gu Huang, Yixian Wang, Jingwen Yan, Li Shen)....Pages 42-53 Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism (Markus D. Schirmer, Ai Wern Chung)....Pages 54-63 A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences (Javid Dadashkarimi, Siyuan Gao, Erin Yeagle, Stephanie Noble, Dustin Scheinost)....Pages 64-73 Adversarial Connectome Embedding for Mild Cognitive Impairment Identification Using Cortical Morphological Networks (Alin Banka, Islem Rekik)....Pages 74-82 A Machine Learning Framework for Accurate Functional Connectome Fingerprinting and an Application of a Siamese Network (Ali Shojaee, Kendrick Li, Gowtham Atluri)....Pages 83-94 Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation (Jianfeng Zeng, Anh The Dang, Gowtham Atluri)....Pages 95-105 Constraining Disease Progression Models Using Subject Specific Connectivity Priors (Anvar Kurmukov, Yuji Zhao, Ayagoz Mussabaeva, Boris Gutman)....Pages 106-116 Hemodynamic Matrix Factorization for Functional Magnetic Resonance Imaging (Michael Hütel, Michela Antonelli, Jinendra Ekanayake, Sebastien Ourselin, Andrew Melbourne)....Pages 117-125 Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An Application to Autism (Ai Wern Chung, Markus D. Schirmer)....Pages 126-137 Back Matter ....Pages 139-139 This book constitutes the refereed proceedings of the Third International Workshop on Connectomics in NeuroImaging, CNI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 13 full papers presented were carefully reviewed and selected from 14 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.