@article{816d86dd10e243e4b816626ba5301415,
title = "Visual analysis of brain networks using sparse regression models",
abstract = "Studies of the human brain network are becoming increasingly popular in the fields of neuroscience, computer science, and neurology. Despite this rapidly growing line of research, gaps remain on the intersection of data analytics, interactive visual representation, and the human intelligence—all needed to advance our understanding of human brain networks. This article tackles this challenge by exploring the design space of visual analytics. We propose an integrated framework to orchestrate computational models with comprehensive data visualizations on the human brain network. The framework targets two fundamental tasks: the visual exploration of multi-label brain networks and the visual comparison among brain networks across different subject groups. During the first task, we propose a novel interactive user interface to visualize sets of labeled brain networks; in our second task, we introduce sparse regression models to select discriminative features from the brain network to facilitate the comparison. Through user studies and quantitative experiments, both methods are shown to greatly improve the visual comparison performance. Finally, real-world case studies with domain experts demonstrate the utility and effectiveness of our framework to analyze reconstructions of human brain connectivity maps. The perceptually optimized visualization design and the feature selection model calibration are shown to be the key to our significant findings.",
keywords = "Brain network, Connectome, Feature selection, Visual analysis",
author = "Lei, {S. H.I.} and Hanghang Tong and Madelaine Daianu and Feng Tian and Thompson, {Paul M.}",
note = "Funding Information: Data collection and sharing for this project was funded by the Alzheimer{\textquoteright}s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer{\textquoteright}s Association; Alzheimer{\textquoteright}s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development, LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer, Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer{\textquoteright}s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. This work is also supported by China National 973 project under Grant 2014CB340301, NSFC under Grants 61379088, 61772504 and 61422212, DTRA under the Grant number HDTRA1-16-0017, Army Research Office under the Contract number W911NF-16-1-0168, NIH under the Grant number R01LM011986, and a Baidu gift. Part of this article was published in the Proceedings of IEEE International Conference on Data Mining series (ICDM 2015) entitled “BrainQuest: Perception-Guided Brain Network Comparison.” Funding Information: Data collection and sharing for this project was funded by the Alzheimer?s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer?s Association; Alzheimer?s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development, LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer, Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer?s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. This work is also supported by China National 973 project under Grant 2014CB340301, NSFC under Grants 61379088, 61772504 and 61422212, DTRA under the Grant number HDTRA1-16-0017, Army Research Office under the Contract number W911NF-16-1-0168, NIH under the Grant number R01LM011986, and a Baidu gift. Part of this article was published in the Proceedings of IEEE International Conference on Data Mining series (ICDM 2015) entitled ?BrainQuest: Perception-Guided Brain Network Comparison.? Publisher Copyright: {\textcopyright} 2018 ACM.",
year = "2018",
month = feb,
doi = "10.1145/3023363",
language = "English (US)",
volume = "12",
journal = "ACM Transactions on Knowledge Discovery from Data",
issn = "1556-4681",
publisher = "Association for Computing Machinery (ACM)",
number = "1",
}