Connectomics in neuroimaging : first International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings PDF
Preview Connectomics in neuroimaging : first International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings
Guorong Wu Paul Laurienti Leonardo Bonilha Brent C. Munsell (Eds.) 1 1 Connectomics 5 0 1 S in NeuroImaging C N L First International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017 Quebec City, QC, Canada, September 14, 2017 Proceedings 123 Lecture Notes in Computer Science 10511 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, Lancaster, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Zurich, Switzerland John C. Mitchell Stanford University, Stanford, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Dortmund, Germany Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbrücken, Germany More information about this series at http://www.springer.com/series/7412 Guorong Wu Paul Laurienti (cid:129) Leonardo Bonilha Brent C. Munsell (Eds.) (cid:129) Connectomics in NeuroImaging First International Workshop, CNI 2017 Held in Conjunction with MICCAI 2017 Quebec City, QC, Canada, September 14, 2017 Proceedings 123 Editors Guorong Wu Leonardo Bonilha University of NorthCarolina Medical University of SouthCarolina at ChapelHill Charleston, SC ChapelHill, NC USA USA BrentC. Munsell PaulLaurienti Collegeof Charleston WakeForest School of Medicine Charleston, SC Winston-Salem, NC USA USA ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notesin Computer Science ISBN 978-3-319-67158-1 ISBN978-3-319-67159-8 (eBook) DOI 10.1007/978-3-319-67159-8 LibraryofCongressControlNumber:2017952863 LNCSSublibrary:SL6–ImageProcessing,ComputerVision,PatternRecognition,andGraphics ©SpringerInternationalPublishingAG2017 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynow knownorhereafterdeveloped. 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Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface The First International Workshop on Connectomics inNeuroImaging (CNI 2017) was held in Quebec City, Canada, on September 14th, 2017, in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Connectomicsisthestudyofwholebrainmapsofconnectivity,commonlyreferred to as the brain connectome, which focuses on quantifying, visualizing, and under- standing brain network organization, including its applications in neuroimaging. The primary academic objective is to bring together computational researchers (computer scientists, data scientists, and computation neuroscientists) to discuss new advance- ments in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies. The secondary aca- demicobjectivetoattractneuroscientistsandclinicianstoshowrecentmethodological advancements in connectomics, and how they are successfully applied in various neuroimaging applications. CNI 2017 was held as a single-track workshop, which included: four keynote speakers (Bharat Biswal, Chris Rorden, Boris Bernhardt, and Moo Chung), oral paper presentations, poster sessions, and software demonstrations. The quality of submissions to our workshop was very high. Authors were asked to submit8pagesinLNCSformatforreview.Atotalof26papersweresubmittedtothe workshopinresponsetothecallforpapers.Eachofthe26papersunderwentarigorous double-blind peer-review process, with each paper being reviewed by at least two (typically three) reviewers from the Program Committee, which was composed of 31 well-known experts in the field of connectomics. Based on the reviewing scores and critiques, the best 19 papers were accepted for presentation at the workshop, and chosen to be included in this Springer LNCS volume. The large variety of connec- tomics techniques, applied in neuroimaging applications, were well represented at the CNI 2017 workshop. We are grateful to the Program Committee for reviewing the submitted papers and giving constructive comments andcritiques, tothe authors for submitting high-quality papers,to thepresenters for excellent presentations, andto all theCNI 2017 attendees who came to Quebec City from all around the world. September 2017 Guorong Wu Paul Laurienti Leonardo Bonilha Brent Munsell Organization Program Committee Pierre Besson Aix-Marseille Université, France Sylvain Bouix Harvard Medical School, USA Dante Chialvo Universidad Nacional de San Martin, Argentine Ai Wern Chung Harvard Medical School, USA Jessica Cohen University of North Carolina, USA Eran Dayan University of North Carolina, USA Simon Davis Duke University, USA Maxime Descôteaux Université de Sherbrooke, Canada Yong Fan University of Pennsylvania, USA Wei Gao Cedars-Sinai Hospital, USA Ghassan Hamarneh Simon Fraser University, Canada Yong He Beijing Normal University, China Daniel Kaufer University of North Carolina, USA Renaud Lopes University of Lille Nord de France, France Barbara Marebwa Medical University of South Carolina, USA Emilie Mckinnon Medical University of South Carolina, USA Vinod Menon Stanford University, USA Iman Mohammad-Rezazadeh University of California Los Angeles, USA Lauren O’Donnell Harvard Medical School, USA Ziwen Peng Southern China Normal University, China Luiz Pessoa University of Maryland, USA Islem Rekik University of Dundee, UK Mert Sabuncu Cornell University, USA Dustin Scheinost Yale University, USA Markus Schirmer Harvard Medical School, USA Li Shen Indiana University, USA Martha Shenton Harvard Medical School, USA Martin Styner University of North Carolina, USA Heung-ll Suk Korea University, South Korea Yihong Yang NIH/NIDA, USA Hungtu Zhu University of Texas MD Anderson Cancer Center, USA Contents Connectome of Autistic Brains, Global Versus Local Characterization. . . . . . 1 Saida S. Mohamed, Nancy Duong Nguyen, Eiko Yoneki, and Alessandro Crimi Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment. . . . . . . . . . . . . . . . . 9 Yu Zhang, Han Zhang, Xiaobo Chen, and Dinggang Shen Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity Network. . . . . . . . . . . . . . 17 Xiuyi Jia, Han Zhang, Ehsan Adeli, and Dinggang Shen Discriminative Log-Euclidean Kernels for Learning on Brain Networks. . . . . 25 Jonathan Young, Du Lei, and Andrea Mechelli Interactive Computation and Visualization of Structural Connectomes in Real-Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Maxime Chamberland, William Gray, Maxime Descoteaux, and Derek K. Jones Pairing-based Ensemble Classifier Learning using Convolutional Brain Multiplexes and Multi-view Brain Networks for Early Dementia Diagnosis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Anna Lisowska, Islem Rekik, and The Alzheimers Disease Neuroimaging Initiative High-order Connectomic Manifold Learning for Autistic Brain State Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Mayssa Soussia and Islem Rekik A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort . . . . . . . . . . . . . . . . . . . . 60 Archana Venkataraman, Nicholas Wymbs, Mary Beth Nebel, and Stewart Mostofsky FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from Functional MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Atif Riaz, Muhammad Asad, S.M. Masudur Rahman Al-Arif, Eduardo Alonso, Danai Dima, Philip Corr, and Greg Slabaugh VIII Contents Identifying Subnetwork Fingerprints in Structural Connectomes: A Data-Driven Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Brent C. Munsell, Eric Hofesmann, John Delgaizo, Martin Styner, and Leonardo Bonilha A Simple and Efficient Cylinder Imposter Approach to Visualize DTI Fiber Tracts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Lucas L. Nesi, Chris Rorden, and Brent C. Munsell Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference . . . . . . . . . . . . . . . . . 98 Sourabh Palande, Vipin Jose, Brandon Zielinski, Jeffrey Anderson, P. Thomas Fletcher, and Bei Wang “Evaluating Acquisition Time of rfMRI in the Human Connectome Project for Early Psychosis. How Much Is Enough?”. . . . . . . . . . . . . . . . . . 108 Sylvain Bouix, Sophia Swago, John D. West, Ofer Pasternak, Alan Breier, and Martha E. Shenton EarlyBrainFunctionalSegregationandIntegrationPredictLaterCognitive Performance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Han Zhang, Weiyan Yin, Weili Lin, and Dinggang Shen Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 David S. Lee, Amber M. Leaver, Katherine L. Narr, Roger P. Woods, and Shantanu H. Joshi Topological Network Analysis of Electroencephalographic Power Maps. . . . . 134 Yuan Wang, Moo K. Chung, Daniela Dentico, Antoine Lutz, and Richard J. Davidson Region-Wise Stochastic Pattern Modeling for Autism Spectrum Disorder Identification and Temporal Dynamics Analysis . . . . . . . . . . . . . . . . . . . . . 143 Eunji Jun and Heung-Il Suk A Whole-Brain Reconstruction Approach for FOD Modeling from Multi-Shell Diffusion MRI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Wei Sun, Junling Li, and Yonggang Shi Topological Distances Between Brain Networks . . . . . . . . . . . . . . . . . . . . . 161 Moo K. Chung, Hyekyoung Lee, Victor Solo, Richard J. Davidson, and Seth D. Pollak Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Connectome of Autistic Brains, Global Versus Local Characterization B Saida S. Mohamed1,5( ), Nancy Duong Nguyen1,3, Eiko Yoneki4, and Alessandro Crimi2 1 African Institute for Mathematical Sciences of Tanzania, Bagamoyo, Tanzania 2 African Institute for Mathematical Sciences of Ghana, Biriwa, Ghana 3 School of Mathematics and Statistics, University College Dublin, Dublin, Ireland 4 Computer Laboratory, University of Cambridge, Cambridge, UK 5 Faculty of Science, Cairo University, Giza, Egypt [email protected] Abstract. The underlying neural mechanisms of autism spectrum dis- orders(ASD)remainsunclear.Mostofthepreviousstudiesbasedoncon- nectomics todiscriminate ASDfromtypically developing (TD)subjects focused either on global graph metrics or specific discriminant connec- tions.Inthispaperweinvestigatewhetherthereisacorrelationbetween localandglobalfeatures,andwhetherthecharacterizationthatdiscrim- inates ASD from TD subjects is primarily given by widespread network differences, or the difference lies in specific local connections which are just captured by global metrics. Namely, whether miswiring of brain connectionsrelatedtoASDislocalizedordiffuse.Thepresentedresults suggest that the widespread hypothesis is more likely. · · · · Keywords: ASD Connectome Tractography Autism Graphmetrics 1 Introduction A connectome is a mathematical representation of the brain as a network com- prising a set of nodes and edges that relate them [17]. Nodes represent distinct homogeneous brain regions generally defined by a brain atlas. Edges represent connectivity, either functional given by co-activation in time of functional sig- nal,orstructuralgivenbythefibersphysicallyconnectingtheareas.Somebrain pathologies investigated by using connectomes have been considered either by their effect in specific local connections or by their impact to the global brain network. For instance, with Alzheimer’s disease there is an overall disruption of structural and functional connectivity [13]. Schizophrenia is considered the “disconnection”diseasewithseveralmiswiringsbetweenbrainareas[20].Stroke and gliomas are mostly focal lesions and many studies have shown disruptions in structural and functional connectivity related to the focal damage, though subsequent changes on the global organization might be present [9]. (cid:2)c SpringerInternationalPublishingAG2017 G.Wuetal.(Eds.):CNI2017,LNCS10511,pp.1–8,2017. DOI:10.1007/978-3-319-67159-81
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