A toolbox for brain network construction and classification (BrainNetClass)
Brain functional network has been increasingly used in understanding brain functions and diseases. While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation‐based functional network and group‐level compari...
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creator | Zhou, Zhen Chen, Xiaobo Zhang, Yu Hu, Dan Qiao, Lishan Yu, Renping Yap, Pew‐Thian Pan, Gang Zhang, Han Shen, Dinggang |
description | Brain functional network has been increasingly used in understanding brain functions and diseases. While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation‐based functional network and group‐level comparisons. We introduce a “Brain Network Construction and Classification (BrainNetClass)” toolbox to promote more advanced brain network construction methods to the filed, including some state‐of‐the‐art methods that were recently developed to capture complex and high‐order interactions among brain regions. The toolbox also integrates a well‐accepted and rigorous classification framework based on brain connectome features toward individualized disease diagnosis in a hope that the advanced network modeling could boost the subsequent classification. BrainNetClass is a MATLAB‐based, open‐source, cross‐platform toolbox with both graphical user‐friendly interfaces and a command line mode targeting cognitive neuroscientists and clinicians for promoting reliability, reproducibility, and interpretability of connectome‐based, computer‐aided diagnosis. It generates abundant classification‐related results from network presentations to contributing features that have been largely ignored by most studies to grant users the ability of evaluating the disease diagnostic model and its robustness and generalizability. We demonstrate the effectiveness of the toolbox on real resting‐state functional MRI datasets. BrainNetClass (v1.0) is available at https://github.com/zzstefan/BrainNetClass. |
doi_str_mv | 10.1002/hbm.24979 |
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While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation‐based functional network and group‐level comparisons. We introduce a “Brain Network Construction and Classification (BrainNetClass)” toolbox to promote more advanced brain network construction methods to the filed, including some state‐of‐the‐art methods that were recently developed to capture complex and high‐order interactions among brain regions. The toolbox also integrates a well‐accepted and rigorous classification framework based on brain connectome features toward individualized disease diagnosis in a hope that the advanced network modeling could boost the subsequent classification. BrainNetClass is a MATLAB‐based, open‐source, cross‐platform toolbox with both graphical user‐friendly interfaces and a command line mode targeting cognitive neuroscientists and clinicians for promoting reliability, reproducibility, and interpretability of connectome‐based, computer‐aided diagnosis. It generates abundant classification‐related results from network presentations to contributing features that have been largely ignored by most studies to grant users the ability of evaluating the disease diagnostic model and its robustness and generalizability. We demonstrate the effectiveness of the toolbox on real resting‐state functional MRI datasets. BrainNetClass (v1.0) is available at https://github.com/zzstefan/BrainNetClass.</description><identifier>ISSN: 1065-9471</identifier><identifier>EISSN: 1097-0193</identifier><identifier>DOI: 10.1002/hbm.24979</identifier><identifier>PMID: 32163221</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Brain ; Brain - diagnostic imaging ; Brain - physiology ; brain connectome ; Brain research ; Classification ; Cognitive ability ; Connectome - methods ; Construction methods ; Diagnosis ; Diagnostic systems ; dynamic functional connectivity ; functional connectivity ; Functional magnetic resonance imaging ; Functionals ; Graphical user interface ; Humans ; Image Processing, Computer-Assisted - methods ; Interfaces ; Life Sciences & Biomedicine ; machine learning ; Magnetic Resonance Imaging - methods ; Nerve Net - diagnostic imaging ; Nerve Net - physiology ; Neuroimaging ; Neurosciences ; Neurosciences & Neurology ; prediction ; Radiology, Nuclear Medicine & Medical Imaging ; Science & Technology ; Software ; sparse representation ; toolbox</subject><ispartof>Human brain mapping, 2020-07, Vol.41 (10), p.2808-2826</ispartof><rights>2020 The Authors. published by Wiley Periodicals, Inc.</rights><rights>2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.</rights><rights>COPYRIGHT 2020 John Wiley & Sons, Inc.</rights><rights>2020. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation‐based functional network and group‐level comparisons. We introduce a “Brain Network Construction and Classification (BrainNetClass)” toolbox to promote more advanced brain network construction methods to the filed, including some state‐of‐the‐art methods that were recently developed to capture complex and high‐order interactions among brain regions. The toolbox also integrates a well‐accepted and rigorous classification framework based on brain connectome features toward individualized disease diagnosis in a hope that the advanced network modeling could boost the subsequent classification. 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Chen, Xiaobo ; Zhang, Yu ; Hu, Dan ; Qiao, Lishan ; Yu, Renping ; Yap, Pew‐Thian ; Pan, Gang ; Zhang, Han ; Shen, Dinggang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5109-737d97339fd7a04792b6b3c831dda48bd972dc2dc83adcb0afc3320f1bb5abe43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Brain</topic><topic>Brain - diagnostic imaging</topic><topic>Brain - physiology</topic><topic>brain connectome</topic><topic>Brain research</topic><topic>Classification</topic><topic>Cognitive ability</topic><topic>Connectome - methods</topic><topic>Construction methods</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>dynamic functional connectivity</topic><topic>functional connectivity</topic><topic>Functional magnetic resonance imaging</topic><topic>Functionals</topic><topic>Graphical user interface</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Interfaces</topic><topic>Life Sciences & Biomedicine</topic><topic>machine learning</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Nerve Net - diagnostic imaging</topic><topic>Nerve Net - physiology</topic><topic>Neuroimaging</topic><topic>Neurosciences</topic><topic>Neurosciences & Neurology</topic><topic>prediction</topic><topic>Radiology, Nuclear Medicine & Medical Imaging</topic><topic>Science & Technology</topic><topic>Software</topic><topic>sparse representation</topic><topic>toolbox</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Zhen</creatorcontrib><creatorcontrib>Chen, Xiaobo</creatorcontrib><creatorcontrib>Zhang, Yu</creatorcontrib><creatorcontrib>Hu, Dan</creatorcontrib><creatorcontrib>Qiao, Lishan</creatorcontrib><creatorcontrib>Yu, Renping</creatorcontrib><creatorcontrib>Yap, Pew‐Thian</creatorcontrib><creatorcontrib>Pan, Gang</creatorcontrib><creatorcontrib>Zhang, Han</creatorcontrib><creatorcontrib>Shen, Dinggang</creatorcontrib><collection>Wiley Online Library (Open Access Collection)</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Human brain mapping</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Zhen</au><au>Chen, Xiaobo</au><au>Zhang, Yu</au><au>Hu, Dan</au><au>Qiao, Lishan</au><au>Yu, Renping</au><au>Yap, Pew‐Thian</au><au>Pan, Gang</au><au>Zhang, Han</au><au>Shen, Dinggang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A toolbox for brain network construction and classification (BrainNetClass)</atitle><jtitle>Human brain mapping</jtitle><stitle>HUM BRAIN MAPP</stitle><addtitle>Hum Brain Mapp</addtitle><date>2020-07</date><risdate>2020</risdate><volume>41</volume><issue>10</issue><spage>2808</spage><epage>2826</epage><pages>2808-2826</pages><issn>1065-9471</issn><eissn>1097-0193</eissn><abstract>Brain functional network has been increasingly used in understanding brain functions and diseases. While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation‐based functional network and group‐level comparisons. We introduce a “Brain Network Construction and Classification (BrainNetClass)” toolbox to promote more advanced brain network construction methods to the filed, including some state‐of‐the‐art methods that were recently developed to capture complex and high‐order interactions among brain regions. The toolbox also integrates a well‐accepted and rigorous classification framework based on brain connectome features toward individualized disease diagnosis in a hope that the advanced network modeling could boost the subsequent classification. BrainNetClass is a MATLAB‐based, open‐source, cross‐platform toolbox with both graphical user‐friendly interfaces and a command line mode targeting cognitive neuroscientists and clinicians for promoting reliability, reproducibility, and interpretability of connectome‐based, computer‐aided diagnosis. It generates abundant classification‐related results from network presentations to contributing features that have been largely ignored by most studies to grant users the ability of evaluating the disease diagnostic model and its robustness and generalizability. We demonstrate the effectiveness of the toolbox on real resting‐state functional MRI datasets. BrainNetClass (v1.0) is available at https://github.com/zzstefan/BrainNetClass.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>32163221</pmid><doi>10.1002/hbm.24979</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-6645-8810</orcidid><orcidid>https://orcid.org/0000-0001-9940-1637</orcidid><orcidid>https://orcid.org/0000-0002-9169-5885</orcidid><orcidid>https://orcid.org/0000-0002-7934-5698</orcidid><orcidid>https://orcid.org/0000-0003-4087-6544</orcidid><orcidid>https://orcid.org/0000-0002-4049-6181</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Brain Brain - diagnostic imaging Brain - physiology brain connectome Brain research Classification Cognitive ability Connectome - methods Construction methods Diagnosis Diagnostic systems dynamic functional connectivity functional connectivity Functional magnetic resonance imaging Functionals Graphical user interface Humans Image Processing, Computer-Assisted - methods Interfaces Life Sciences & Biomedicine machine learning Magnetic Resonance Imaging - methods Nerve Net - diagnostic imaging Nerve Net - physiology Neuroimaging Neurosciences Neurosciences & Neurology prediction Radiology, Nuclear Medicine & Medical Imaging Science & Technology Software sparse representation toolbox |
title | A toolbox for brain network construction and classification (BrainNetClass) |
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