LEADER 10572nam 22008295 450 001 996466266203316 005 20200706192552.0 010 $a3-319-67159-6 024 7 $a10.1007/978-3-319-67159-8 035 $a(CKB)4100000000587145 035 $a(DE-He213)978-3-319-67159-8 035 $a(MiAaPQ)EBC6281933 035 $a(MiAaPQ)EBC5579846 035 $a(Au-PeEL)EBL5579846 035 $a(OCoLC)1003193842 035 $a(PPN)204533619 035 $a(EXLCZ)994100000000587145 100 $a20170901d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aConnectomics in NeuroImaging$b[electronic resource] $eFirst International Workshop, CNI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings /$fedited by Guorong Wu, Paul Laurienti, Leonardo Bonilha, Brent C. Munsell 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (VIII, 171 p. 67 illus.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v10511 300 $aIncludes index. 311 $a3-319-67158-8 327 $aIntro -- Preface -- Organization -- Contents -- Connectome of Autistic Brains, Global Versus Local Characterization -- 1 Introduction -- 1.1 Connectomes and Autism Spectrum Disorder -- 1.2 Global Metrics -- 1.3 Local Connectivity Differences -- 2 Methods -- 3 Data and Experimental Settings -- 3.1 Pre-processing and Connectome Construction -- 3.2 Experimental Settings -- 4 Results and Discussions -- 5 Conclusion -- References -- Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognit ... -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Multi-frequency High-Order FC Networks -- 2.2 Feature Extraction and Classification -- 3 Experiments -- 3.1 Data -- 3.2 Performance Evaluation -- 3.3 Intra-spectrum and Inter-spectrum HONs -- 4 Conclusion -- Acknowledgements -- References -- Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity N ... -- Abstract -- 1 Introduction -- 2 Materials -- 3 High-Order BFCN Construction -- 4 Experiments -- 5 Conclusion -- Acknowledgements -- References -- Discriminative Log-Euclidean Kernels for Learning on Brain Networks -- 1 Introduction -- 2 Methods and Materials -- 2.1 Subjects -- 2.2 Image Data -- 2.3 Image Processing -- 2.4 Brain Network Construction -- 2.5 Gaussian Process Classification -- 2.6 The Discriminative Log-Euclidean Kernel -- 2.7 Impementation Details -- 2.8 Classification Experiments -- 2.9 Group Difference Experiments -- 3 Results and Discussion -- 4 Conclusions -- References -- Interactive Computation and Visualization of Structural Connectomes in Real-Time -- 1 Introduction -- 2 Methods -- 2.1 Structural Connectivity -- 3 Visualization -- 4 Results -- 5 Discussion -- 6 Conclusion -- References. 327 $aPairing-based Ensemble Classifier Learning using Convolutional Brain Multiplexes and Multi-view Brain Networks for Early Dementia Diagnosis -- 1 Introduction -- 2 Ensemble Classifier Using Paired CCA-Mapped Convolutional Brain Mutliplexes for eMCI/NC Classification -- 3 Results and Discussion -- 4 Conclusion -- References -- High-order Connectomic Manifold Learning for Autistic Brain State Identification -- 1 Introduction -- 2 High-Order Connectomic Manifold Learning for Unsupervised Clustering of Autistic and Healthy Brains -- 3 Results and Discussion -- 4 Conclusion -- References -- A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort -- 1 Introduction -- 2 Generative Model of Abnormal Communities -- 3 Population Study of Autism -- 4 Conclusion -- References -- FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from Functional MRI -- 1 Introduction -- 2 Method -- 2.1 Data and Preprocessing -- 2.2 Functional Connectivity Through FCNet -- 2.3 Feature Selection and Classification -- 3 Experiments and Results -- 4 Conclusion -- References -- Identifying Subnetwork Fingerprints in Structural Connectomes: A Data-Driven Approach -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Participants, MRI Acquisition, and Connectome Reconstruction -- 2.2 Subnetwork Feature -- 2.3 Person Identification Model and Performance Evaluation -- 2.4 Feature Selection and Majority Vote Subnetwork Feature -- 3 Results -- 4 Discussion -- 5 Conclusion -- Acknowledgement -- References -- A Simple and Efficient Cylinder Imposter Approach to Visualize DTI Fiber Tracts -- Abstract -- 1 Introduction -- 2 Proposed Method -- 2.1 Cylinder Imposter -- 2.2 End Imposter -- 3 Experiments -- 4 Limitations -- 5 Conclusion -- 6 Implementation -- References. 327 $aRevisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference -- 1 Introduction -- 2 Technical Background -- 2.1 Structural Covariance Network -- 2.2 Graph Filtration -- 2.3 Statistical Inference -- 3 Methods -- 3.1 Data Preprocessing -- 3.2 Structural Covariance Networks and Statistical Inference -- 4 Results -- 5 Conclusion and Discussion -- References -- "Evaluating Acquisition Time of rfMRI in the Human Connectome Project for Early Psychosis. How Much ... -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Participants -- 2.2 Data Acquisition -- 2.3 Data Preprocessing -- 2.4 Statistical Analysis -- 3 Results -- 3.1 Correlation Matrix Reliability -- 3.2 Network Rank Reliability -- 4 Discussion and Conclusion -- Acknowledgments -- References -- Early Brain Functional Segregation and Integration Predict Later Cognitive Performance -- Abstract -- 1 Introduction -- 2 Methods and Results -- 2.1 An Early Developing Triple Network Model -- 2.2 Individual Prediction of Later Cognitive Performance -- 3 Discussion and Conclusions -- 4 Future Works and Clinical Implications -- References -- Measuring Brain Connectivity via Shape Analysis of fMRI Time Courses and Spectra -- 1 Introduction -- 2 FMRI Shape Analysis of Time Courses and Spectra -- 2.1 Elastic Functional Data Analysis of fMRI Signals Using SRVFs -- 2.2 fMRI Alignment and Registration: -- 3 Results -- 3.1 Visualization of Elastic Functional Alignment -- 3.2 Measuring Brain Connectivity After Elastic fMRI Alignment -- 4 Discussion -- References -- Topological Network Analysis of Electroencephalographic Power Maps -- 1 Introduction -- 2 Methods -- 3 Simulations -- 4 Real Data Application -- 5 Discussion -- References -- Region-Wise Stochastic Pattern Modeling for Autism Spectrum Disorder Identification and Temporal Dynamics Analysis. 327 $a1 Introduction -- 2 Materials and Image Processing -- 3 ROI-Wise Temporal Dynamics Estimation -- 3.1 ROI-Wise HMMs Modeling -- 3.2 Feature Extraction and Classifier Learning -- 3.3 Measuring Temporal Dynamics -- 4 Experimental Settings and Results -- 4.1 Choosing Optimal Number of States in HMMs -- 4.2 Performance Comparison -- 4.3 Regional Importance and Temporal Dynamics Analysis -- 5 Conclusion -- References -- A Whole-Brain Reconstruction Approach for FOD Modeling from Multi-Shell Diffusion MRI -- 1 Introduction -- 2 Method -- 2.1 Voxel-Wise FOD Modeling from Multi-Shell Imaging -- 2.2 Whole-Brain FOD Modeling with Spatial Regularity -- 2.3 Spatial Regularization via Hyper-spherical Smoothing -- 3 Experiments -- 3.1 Simulation -- 3.2 HCP Data for Locus Coeruleus Bundle Reconstruction -- 4 Discussion and Conclusion -- References -- Topological Distances Between Brain Networks -- 1 Introduction -- 2 Matrix Norms -- 3 Gromov-Hausdorff Distance -- 4 Kolmogorov-Smirnov Distance -- 5 Comparisons -- 6 Application -- 7 Discussion -- References -- Author Index. 330 $aThis book constitutes the refereed proceedings of the First International Workshop on Connectomics in NeuroImaging, CNI 2017, held in conjunction with MICCAI 2017 in Quebec City, Canada, in September 2017. The 19 full papers presented were carefully reviewed and selected from 26 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v10511 606 $aOptical data processing 606 $aArtificial intelligence 606 $aComputer graphics 606 $aPattern recognition 606 $aComputer organization 606 $aComputer science?Mathematics 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aComputer Systems Organization and Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13006 606 $aDiscrete Mathematics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17028 615 0$aOptical data processing. 615 0$aArtificial intelligence. 615 0$aComputer graphics. 615 0$aPattern recognition. 615 0$aComputer organization. 615 0$aComputer science?Mathematics. 615 14$aImage Processing and Computer Vision. 615 24$aArtificial Intelligence. 615 24$aComputer Graphics. 615 24$aPattern Recognition. 615 24$aComputer Systems Organization and Communication Networks. 615 24$aDiscrete Mathematics in Computer Science. 676 $a612.82 702 $aWu$b Guorong$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLaurienti$b Paul$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBonilha$b Leonardo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMunsell$b Brent C$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466266203316 996 $aConnectomics in NeuroImaging$92263619 997 $aUNISA