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Connectomics in NeuroImaging

First International Workshop, CNI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings

  • Conference proceedings
  • © 2017

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Editors:
  1. Guorong Wu

    1. University of North Carolina at Chapel Hill, Chapel Hill, USA

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  2. Paul Laurienti

    1. Wake Forest School of Medicine, Winston-Salem, USA

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  3. Leonardo Bonilha

    1. Medical University of South Carolina, Charleston, USA

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  4. Brent C. Munsell

    1. College of Charleston, Charleston, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10511)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: CNI 2017.

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About this book

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.


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Table of contents (19 papers)

  1. Front Matter

    Pages I-VIII
    Download chapter PDF

  2. Connectome of Autistic Brains, Global Versus Local Characterization

    • Saida S. Mohamed, Nancy Duong Nguyen, Eiko Yoneki, Alessandro Crimi
    Pages 1-8

  3. 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

  4. Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity Network

    • Xiuyi Jia, Han Zhang, Ehsan Adeli, Dinggang Shen
    Pages 17-24

  5. Discriminative Log-Euclidean Kernels for Learning on Brain Networks

    • Jonathan Young, Du Lei, Andrea Mechelli
    Pages 25-34

  6. Interactive Computation and Visualization of Structural Connectomes in Real-Time

    • Maxime Chamberland, William Gray, Maxime Descoteaux, Derek K. Jones
    Pages 35-41

  7. 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

  8. High-order Connectomic Manifold Learning for Autistic Brain State Identification

    • Mayssa Soussia, Islem Rekik
    Pages 51-59

  9. 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

  10. 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

  11. Identifying Subnetwork Fingerprints in Structural Connectomes: A Data-Driven Approach

    • Brent C. Munsell, Eric Hofesmann, John Delgaizo, Martin Styner, Leonardo Bonilha
    Pages 79-88

  12. A Simple and Efficient Cylinder Imposter Approach to Visualize DTI Fiber Tracts

    • Lucas L. Nesi, Chris Rorden, Brent C. Munsell
    Pages 89-97

  13. 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

  14. “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

  15. Early Brain Functional Segregation and Integration Predict Later Cognitive Performance

    • Han Zhang, Weiyan Yin, Weili Lin, Dinggang Shen
    Pages 116-124

  16. 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

  17. Topological Network Analysis of Electroencephalographic Power Maps

    • Yuan Wang, Moo K. Chung, Daniela Dentico, Antoine Lutz, Richard J. Davidson
    Pages 134-142

  18. Region-Wise Stochastic Pattern Modeling for Autism Spectrum Disorder Identification and Temporal Dynamics Analysis

    • Eunji Jun, Heung-Il Suk
    Pages 143-151

  19. A Whole-Brain Reconstruction Approach for FOD Modeling from Multi-Shell Diffusion MRI

    • Wei Sun, Junling Li, Yonggang Shi
    Pages 152-160

  20. Topological Distances Between Brain Networks

    • Moo K. Chung, Hyekyoung Lee, Victor Solo, Richard J. Davidson, Seth D. Pollak
    Pages 161-170
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Other volumes

  1. Connectomics in NeuroImaging

Editors and Affiliations

  • University of North Carolina at Chapel Hill, Chapel Hill, USA

    Guorong Wu

  • Wake Forest School of Medicine, Winston-Salem, USA

    Paul Laurienti

  • Medical University of South Carolina, Charleston, USA

    Leonardo Bonilha

  • College of Charleston, Charleston, USA

    Brent C. Munsell

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