04726nam 22006735 450 99646644780331620200810140646.03-030-32391-910.1007/978-3-030-32391-2(CKB)4100000009522854(DE-He213)978-3-030-32391-2(MiAaPQ)EBC5968222(PPN)256201544(EXLCZ)99410000000952285420191008d2019 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierConnectomics in NeuroImaging[electronic resource] Third International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /edited by Markus D. Schirmer, Archana Venkataraman, Islem Rekik, Minjeong Kim, Ai Wern Chung1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (X, 139 p. 53 illus., 51 illus. in color.) Image Processing, Computer Vision, Pattern Recognition, and Graphics ;11848Includes index.3-030-32390-0 Unsupervised Feature Selection via Adaptive Embedding and Sparse Learning for Parkinson's Disease Diagnosis -- A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity -- Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia States -- Covariance Shrinkage for Dynamic Functional Connectivity -- Rapid Acceleration of the Permutation Test via Transpositions -- Heat kernels with functional connectomes reveal atypical energy transport in peripheral subnetworks in autism -- A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences -- Adversarial Connectome Embedding for Mild Cognitive Impairment Identification using Cortical Morphological Networks -- A Machine Learning Framework for Accurate Functional Connectome Fingerprinting and an Application of a Siamese Network -- Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation -- Constraining Disease Progression Models Using Subject Specific Connectivity Priors -- Hemodynamic Matrix Factorization for Functional Magnetic Resonance Imaging -- Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An application to autism.This book constitutes the refereed proceedings of the Third International Workshop on Connectomics in NeuroImaging, CNI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 13 full papers presented were carefully reviewed and selected from 14 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.Image Processing, Computer Vision, Pattern Recognition, and Graphics ;11848Artificial intelligenceData miningComputersOptical data processingArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Models and Principleshttps://scigraph.springernature.com/ontologies/product-market-codes/I18016Computer Imaging, Vision, Pattern Recognition and Graphicshttps://scigraph.springernature.com/ontologies/product-market-codes/I22005Artificial intelligence.Data mining.Computers.Optical data processing.Artificial Intelligence.Data Mining and Knowledge Discovery.Models and Principles.Computer Imaging, Vision, Pattern Recognition and Graphics.612.82Schirmer Markus Dedthttp://id.loc.gov/vocabulary/relators/edtVenkataraman Archanaedthttp://id.loc.gov/vocabulary/relators/edtRekik Islemedthttp://id.loc.gov/vocabulary/relators/edtKim Minjeongedthttp://id.loc.gov/vocabulary/relators/edtChung Ai Wernedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996466447803316Connectomics in NeuroImaging2263619UNISA