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Titlebook: Connectomics in NeuroImaging; First International Guorong Wu,Paul Laurienti,Brent C. Munsell Conference proceedings 2017 Springer Internat

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發(fā)表于 2025-3-21 16:51:25 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Connectomics in NeuroImaging
副標(biāo)題First International
編輯Guorong Wu,Paul Laurienti,Brent C. Munsell
視頻videohttp://file.papertrans.cn/236/235641/235641.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Connectomics in NeuroImaging; First International  Guorong Wu,Paul Laurienti,Brent C. Munsell Conference proceedings 2017 Springer Internat
描述.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..
出版日期Conference proceedings 2017
關(guān)鍵詞architecture; artificial intelligence; classification; classification accuracy; classifiers; cluster anal
版次1
doihttps://doi.org/10.1007/978-3-319-67159-8
isbn_softcover978-3-319-67158-1
isbn_ebook978-3-319-67159-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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The uniform output regulation problem,ising tool for imaging-based brain disease diagnosis. Conventional low-order FC network (LON) usually characterizes pairwise temporal correlation of rs-fMRI signals between any pair of brain regions. Meanwhile, high-order FC network (HON) has provided an alternative brain network modeling strategy,
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https://doi.org/10.1007/0-8176-4465-2 them using graph theory. As a result, it can be non-intuitive to grasp the contribution of each edge within a graph, both at a local and global scale. Here, we introduce a new platform that enables tractography-based networks to be explored in a highly interactive real-time fashion. The framework a
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https://doi.org/10.1007/0-8176-4465-2vity (functional MRI). However, how early dementia affects the morphology of the cortical surface remains poorly understood. In this paper, we first introduce . architecture which stacks multiple networks, each quantifying a cortical attribute (e.g., thickness). Second, to model the relationship bet
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Alexey Pavlov,Nathan Wouw,Henk Nijmeijericine. Recently, machine learning techniques typically use .-.-. or .-. connectivity features to understand how the brain is organized, and then use this information to predict the clinical outcome. Unfortunately, computational models that are trained with these types of features are very localized
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