Connectomics in neuroimaging : first International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings

Description

This book constitutes the refereed proceedings of the First International Workshop on Connectomics in NeuroImaging, CNI 2017, held in conjunction with MICCAI 2017 in Quebec City, Canada, in September 2017. The 19 full papers presented were carefully reviewed and selected from 26 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. Front Matter ....Pages I-VIII Connectome of Autistic Brains, Global Versus Local Characterization (Saida S. Mohamed, Nancy Duong Nguyen, Eiko Yoneki, Alessandro Crimi)....Pages 1-8 Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment (Yu Zhang, Han Zhang, Xiaobo Chen, Dinggang Shen)....Pages 9-16 Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity Network (Xiuyi Jia, Han Zhang, Ehsan Adeli, Dinggang Shen)....Pages 17-24 Discriminative Log-Euclidean Kernels for Learning on Brain Networks (Jonathan Young, Du Lei, Andrea Mechelli)....Pages 25-34 Interactive Computation and Visualization of Structural Connectomes in Real-Time (Maxime Chamberland, William Gray, Maxime Descoteaux, Derek K. Jones)....Pages 35-41 Pairing-based Ensemble Classifier Learning using Convolutional Brain Multiplexes and Multi-view Brain Networks for Early Dementia Diagnosis (Anna Lisowska, Islem Rekik, The Alzheimers Disease Neuroimaging Initiative)....Pages 42-50 High-order Connectomic Manifold Learning for Autistic Brain State Identification (Mayssa Soussia, Islem Rekik)....Pages 51-59 A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort (Archana Venkataraman, Nicholas Wymbs, Mary Beth Nebel, Stewart Mostofsky)....Pages 60-69 FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from Functional MRI (Atif Riaz, Muhammad Asad, S. M. Masudur Rahman Al-Arif, Eduardo Alonso, Danai Dima, Philip Corr et al.)....Pages 70-78 Identifying Subnetwork Fingerprints in Structural Connectomes: A Data-Driven Approach (Brent C. Munsell, Eric Hofesmann, John Delgaizo, Martin Styner, Leonardo Bonilha)....Pages 79-88 A Simple and Efficient Cylinder Imposter Approach to Visualize DTI Fiber Tracts (Lucas L. Nesi, Chris Rorden, Brent C. Munsell)....Pages 89-97 Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference (Sourabh Palande, Vipin Jose, Brandon Zielinski, Jeffrey Anderson, P. Thomas Fletcher, Bei Wang)....Pages 98-107 “Evaluating Acquisition Time of rfMRI in the Human Connectome Project for Early Psychosis. How Much Is Enough?” (Sylvain Bouix, Sophia Swago, John D. West, Ofer Pasternak, Alan Breier, Martha E. Shenton)....Pages 108-115 Early Brain Functional Segregation and Integration Predict Later Cognitive Performance (Han Zhang, Weiyan Yin, Weili Lin, Dinggang Shen)....Pages 116-124 Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra (David S. Lee, Amber M. Leaver, Katherine L. Narr, Roger P. Woods, Shantanu H. Joshi)....Pages 125-133 Topological Network Analysis of Electroencephalographic Power Maps (Yuan Wang, Moo K. Chung, Daniela Dentico, Antoine Lutz, Richard J. Davidson)....Pages 134-142 Region-Wise Stochastic Pattern Modeling for Autism Spectrum Disorder Identification and Temporal Dynamics Analysis (Eunji Jun, Heung-Il Suk)....Pages 143-151 A Whole-Brain Reconstruction Approach for FOD Modeling from Multi-Shell Diffusion MRI (Wei Sun, Junling Li, Yonggang Shi)....Pages 152-160 Topological Distances Between Brain Networks (Moo K. Chung, Hyekyoung Lee, Victor Solo, Richard J. Davidson, Seth D. Pollak)....Pages 161-170 Back Matter ....Pages 171-171