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ORCID1. CNI@MICCAI 2017: Quebec City, QC, Canada
- Guorong Wu, Paul J. Laurienti, Leonardo Bonilha, Brent C. Munsell:
Connectomics in NeuroImaging - First International Workshop, CNI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings. Lecture Notes in Computer Science 10511, Springer 2017, ISBN 978-3-319-67158-1 - Saida S. Mohamed, Nancy Duong Nguyen, Eiko Yoneki, Alessandro Crimi
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Connectome of Autistic Brains, Global Versus Local Characterization. 1-8 - Yu Zhang, Han Zhang, Xiaobo Chen, Dinggang Shen:
Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment. 9-16 - Xiuyi Jia, Han Zhang, Ehsan Adeli, Dinggang Shen:
Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity Network. 17-24 - Jonathan Young, Du Lei, Andrea Mechelli:
Discriminative Log-Euclidean Kernels for Learning on Brain Networks. 25-34 - Maxime Chamberland, William Gray, Maxime Descoteaux, Derek K. Jones:
Interactive Computation and Visualization of Structural Connectomes in Real-Time. 35-41 - Anna Lisowska, Islem Rekik:
Pairing-based Ensemble Classifier Learning using Convolutional Brain Multiplexes and Multi-view Brain Networks for Early Dementia Diagnosis. 42-50 - Mayssa Soussia, Islem Rekik:
High-order Connectomic Manifold Learning for Autistic Brain State Identification. 51-59 - Archana Venkataraman, Nicholas F. Wymbs, Mary Beth Nebel, Stewart Mostofsky:
A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort. 60-69 - Atif Riaz, Muhammad Asad, S. M. Masudur Rahman Al-Arif, Eduardo Alonso, Danai Dima, Philip Corr, Greg G. Slabaugh:
FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from Functional MRI. 70-78 - Brent C. Munsell, Eric Hofesmann, John Delgaizo, Martin Styner, Leonardo Bonilha:
Identifying Subnetwork Fingerprints in Structural Connectomes: A Data-Driven Approach. 79-88 - Lucas Leandro Nesi, Chris Rorden, Brent C. Munsell:
A Simple and Efficient Cylinder Imposter Approach to Visualize DTI Fiber Tracts. 89-97 - Sourabh Palande, Vipin Jose, Brandon A. Zielinski, Jeffrey S. Anderson, P. Thomas Fletcher, Bei Wang:
Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference. 98-107 - Sylvain Bouix, Sophia Swago, John D. West, Ofer Pasternak, Alan Breier, Martha Elizabeth Shenton:
"Evaluating Acquisition Time of rfMRI in the Human Connectome Project for Early Psychosis. How Much Is Enough?". 108-115 - Han Zhang, Weiyan Yin, Weili Lin, Dinggang Shen:
Early Brain Functional Segregation and Integration Predict Later Cognitive Performance. 116-124 - David S. Lee, Amber M. Leaver, Katherine L. Narr, Roger P. Woods, Shantanu H. Joshi:
Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra. 125-133 - Yuan Wang, Moo K. Chung, Daniela Dentico, Antoine Lutz, Richard J. Davidson:
Topological Network Analysis of Electroencephalographic Power Maps. 134-142 - Eunji Jun, Heung-Il Suk:
Region-Wise Stochastic Pattern Modeling for Autism Spectrum Disorder Identification and Temporal Dynamics Analysis. 143-151 - Wei Sun, Junling Li, Yonggang Shi:
A Whole-Brain Reconstruction Approach for FOD Modeling from Multi-Shell Diffusion MRI. 152-160 - Moo K. Chung, Hyekyoung Lee, Victor Solo, Richard J. Davidson, Seth D. Pollak:
Topological Distances Between Brain Networks. 161-170
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