Hierarchical Spectral Consensus Clustering for Group Analysis of Functional Brain Networks

A central question in cognitive neuroscience is how cognitive functions depend on the integration of specialized widely distributed brain regions. In recent years, graph theoretical methods have been used to characterize the structure of the brain functional connectivity. In order to understand the...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on biomedical engineering Ročník 62; číslo 9; s. 2158 - 2169
Hlavní autoři:	Ozdemir, Alp, Bolanos, Marcos, Bernat, Edward, Aviyente, Selin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.09.2015
Témata:	Algorithms Biomedical measurement Brain - physiology Brain Mapping - methods Cluster Analysis Clustering algorithms Communities Consensus clustering electroencephalogram (EEG) Electroencephalography Female Fiedler vector functional connectivity Humans Male Nerve Net - physiology Partitioning algorithms spectral clustering Symmetric matrices Vectors functional connectivity Fiedler vector Consensus clustering electroencephalogram (EEG) spectral clustering
ISSN:	0018-9294, 1558-2531, 1558-2531
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	A central question in cognitive neuroscience is how cognitive functions depend on the integration of specialized widely distributed brain regions. In recent years, graph theoretical methods have been used to characterize the structure of the brain functional connectivity. In order to understand the organization of functional connectivity networks, it is important to determine the community structure underlying these complex networks. Moreover, the study of brain functional networks is confounded by the fact that most neurophysiological studies consists of data collected from multiple subjects; thus, it is important to identify communities representative of all subjects. Typically, this problem is addressed by averaging the data across subjects which omits the variability across subjects or using voting methods, which requires a priori knowledge of cluster labels. In this paper, we propose a hierarchical consensus spectral clustering approach to address these problems. Furthermore, new information-theoretic criteria are introduced for selecting the optimal community structure. The proposed framework is applied to electroencephalogram data collected during a study of error-related negativity to better understand the community structure of functional networks involved in the cognitive control.
AbstractList	A central question in cognitive neuroscience is how cognitive functions depend on the integration of specialized widely distributed brain regions. In recent years, graph theoretical methods have been used to characterize the structure of the brain functional connectivity. In order to understand the organization of functional connectivity networks, it is important to determine the community structure underlying these complex networks. Moreover, the study of brain functional networks is confounded by the fact that most neurophysiological studies consists of data collected from multiple subjects; thus, it is important to identify communities representative of all subjects. Typically, this problem is addressed by averaging the data across subjects which omits the variability across subjects or using voting methods, which requires a priori knowledge of cluster labels. In this paper, we propose a hierarchical consensus spectral clustering approach to address these problems. Furthermore, new information-theoretic criteria are introduced for selecting the optimal community structure. The proposed framework is applied to electroencephalogram data collected during a study of error-related negativity to better understand the community structure of functional networks involved in the cognitive control. A central question in cognitive neuroscience is how cognitive functions depend on the integration of specialized widely distributed brain regions. In recent years, graph theoretical methods have been used to characterize the structure of the brain functional connectivity. In order to understand the organization of functional connectivity networks, it is important to determine the community structure underlying these complex networks. Moreover, the study of brain functional networks is confounded by the fact that most neurophysiological studies consists of data collected from multiple subjects; thus, it is important to identify communities representative of all subjects. Typically, this problem is addressed by averaging the data across subjects which omits the variability across subjects or using voting methods, which requires a priori knowledge of cluster labels. In this paper, we propose a hierarchical consensus spectral clustering approach to address these problems. Furthermore, new information-theoretic criteria are introduced for selecting the optimal community structure. The proposed framework is applied to electroencephalogram data collected during a study of error-related negativity to better understand the community structure of functional networks involved in the cognitive control.A central question in cognitive neuroscience is how cognitive functions depend on the integration of specialized widely distributed brain regions. In recent years, graph theoretical methods have been used to characterize the structure of the brain functional connectivity. In order to understand the organization of functional connectivity networks, it is important to determine the community structure underlying these complex networks. Moreover, the study of brain functional networks is confounded by the fact that most neurophysiological studies consists of data collected from multiple subjects; thus, it is important to identify communities representative of all subjects. Typically, this problem is addressed by averaging the data across subjects which omits the variability across subjects or using voting methods, which requires a priori knowledge of cluster labels. In this paper, we propose a hierarchical consensus spectral clustering approach to address these problems. Furthermore, new information-theoretic criteria are introduced for selecting the optimal community structure. The proposed framework is applied to electroencephalogram data collected during a study of error-related negativity to better understand the community structure of functional networks involved in the cognitive control.
Author	Bernat, Edward Aviyente, Selin Bolanos, Marcos Ozdemir, Alp
Author_xml	– sequence: 1 givenname: Alp surname: Ozdemir fullname: Ozdemir, Alp email: ozdemira@msu.edu organization: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA – sequence: 2 givenname: Marcos surname: Bolanos fullname: Bolanos, Marcos email: bolanosm@cna.org organization: CNA Corporation – sequence: 3 givenname: Edward surname: Bernat fullname: Bernat, Edward email: ebernat@umd.edu organization: Department of Psychology, University of Maryland, 1147 Biology/Psychology Building College Park, MD – sequence: 4 givenname: Selin surname: Aviyente fullname: Aviyente, Selin email: aviyente@egr.msu.edu organization: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/25807564$$D View this record in MEDLINE/PubMed
BookMark	eNp9kMFuGyEURVHlqrbTfkAVKWKZzbg8YBhmmViOHSltF0033YwwftOQjMGBGVX--2LZySKLrB7v6lxAZ0pGPngk5CuwGQCrv91ff1_MOINyxiWUlRAfyATKUhe8FDAiE8ZAFzWv5ZhMU3rMq9RSfSJjXmpWlUpOyJ-Vw2iifXDWdPTXDm0f82EefEKfhkTn3ZB6jM7_pW2IdBnDsKNX3nT75BINLb0ZvO1dyAm9jsZ5-gP7fyE-pc_kY2u6hF9O84z8vlncz1fF3c_l7fzqrrCCQ1_oWmiwbK21YspUG4NGG63qNaoWeKuAMcE3xgjD2g0XFUi1loDacGSqzPkZuTzeu4vhecDUN1uXLHad8RiG1EDFFM9oJTN6cUKH9RY3zS66rYn75sVHBuAI2BhSiti-IsCag_Pm4Lw5OG9OznOnetOxrjcHJVml695tnh-bDhFfX8rflarW4j8fm45K
CODEN	IEBEAX
CitedBy_id	crossref_primary_10_1109_ACCESS_2025_3567703 crossref_primary_10_3389_fninf_2018_00048 crossref_primary_10_1109_TBME_2016_2616474 crossref_primary_10_1371_journal_pone_0212470 crossref_primary_10_1016_j_compbiomed_2024_109435 crossref_primary_10_1016_j_jii_2017_02_005 crossref_primary_10_1016_j_ijpsycho_2018_04_013 crossref_primary_10_1016_j_jnca_2022_103513 crossref_primary_10_1109_MSP_2022_3149471 crossref_primary_10_1016_j_bspc_2024_106763 crossref_primary_10_1109_TCYB_2020_2974776 crossref_primary_10_1371_journal_pcbi_1012765 crossref_primary_10_1038_s41598_023_35232_2 crossref_primary_10_1109_TSIPN_2017_2668146 crossref_primary_10_1038_s41598_021_85210_9 crossref_primary_10_1002_hbm_25545 crossref_primary_10_1016_j_neuroimage_2022_119082 crossref_primary_10_1109_TSP_2016_2529586 crossref_primary_10_1109_TSIPN_2017_2707662 crossref_primary_10_1007_s41109_016_0007_y crossref_primary_10_1088_1741_2552_aaac36 crossref_primary_10_1109_TNSE_2018_2869862 crossref_primary_10_1109_TSIPN_2025_3527888
Cites_doi	10.1016/j.physd.2006.09.008 10.1073/pnas.0601602103 10.1016/j.clinph.2005.08.033 10.1137/1.9781611972757.25 10.21136/CMJ.1975.101357 10.1093/cercor/bhq291 10.1103/PhysRevLett.97.238103 10.1109/ICDM.2004.10100 10.1016/S0987-7053(02)00301-5 10.1002/hbm.20353 10.1093/brain/awp086 10.1109/92.748202 10.1016/j.patrec.2009.11.001 10.1093/brain/awn018 10.1007/s11222-007-9033-z 10.1109/TSP.2011.2144589 10.1109/ICDM.2007.73 10.1109/TIT.1968.1054157 10.1016/0165-0173(94)00016-I 10.1093/bioinformatics/btq569 10.1093/cercor/bhr269 10.3389/neuro.11.037.2009 10.1038/35067550 10.1103/PhysRevLett.104.118701 10.1371/journal.pone.0013701 10.1038/nrn2575 10.1109/18.61115 10.1007/BFb0097982 10.1186/1471-2105-6-260 10.1002/hbm.21000 10.1093/cercor/bhn216 10.1371/journal.pcbi.0010042 10.1016/S0730-725X(99)00102-2 10.1142/S0218127407019251 10.1371/journal.pcbi.1000100 10.1016/j.neurobiolaging.2008.07.019 10.1093/brain/awn262 10.1002/hbm.21030 10.1038/17120 10.1016/j.clinph.2012.06.005 10.1103/PhysRevLett.81.3291 10.1137/1.9781611972740.35 10.1016/S1388-2457(02)00322-X 10.1016/S1053-8119(03)00062-4 10.1186/1753-4631-1-3 10.1016/j.neuropsychologia.2010.11.024 10.1109/ICDMW.2010.63 10.1111/j.1467-9280.1993.tb00586.x 10.1523/JNEUROSCI.4137-08.2009 10.1073/pnas.95.15.8420 10.1016/j.jneumeth.2012.10.004 10.1016/j.clinph.2004.06.001 10.1111/j.1467-9280.2007.01899.x 10.1177/001316446002000104 10.1016/0013-4694(91)90062-9 10.1016/j.jneumeth.2007.09.026 10.1145/1015330.1015407 10.1016/j.ejor.2010.08.012 10.3389/fnins.2010.00200 10.1016/j.patrec.2009.09.011 10.1145/1217299.1217303 10.1063/1.3129783 10.1038/nature03288 10.1073/pnas.0605965104 10.1016/S0165-0270(01)00372-7 10.1002/sim.4780100512 10.1016/j.clinph.2009.12.010 10.1523/JNEUROSCI.0333-10.2010 10.1148/radiol.11110380 10.1142/S0129065707000932 10.1016/j.tics.2004.06.006 10.1109/TITB.2004.834406 10.1016/j.neuroimage.2010.03.081
ContentType	Journal Article
DBID	97E RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8
DOI	10.1109/TBME.2015.2415733
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic
DatabaseTitleList	MEDLINE MEDLINE - Academic
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine Engineering
EISSN	1558-2531
EndPage	2169
ExternalDocumentID	25807564 10_1109_TBME_2015_2415733 7064698
Genre	orig-research Research Support, U.S. Gov't, Non-P.H.S Journal Article
GrantInformation_xml	– fundername: National Science Foundation grantid: CCF-0728984; CAREER CCF-0746971; CCF-1218377 funderid: 10.13039/100000001
GroupedDBID	--- -~X .55 .DC .GJ 0R~ 29I 4.4 53G 5GY 5RE 5VS 6IF 6IK 6IL 6IN 85S 97E AAJGR AARMG AASAJ AAWTH AAYJJ ABAZT ABJNI ABQJQ ABVLG ACGFO ACGFS ACIWK ACKIV ACNCT ACPRK ADZIZ AENEX AETIX AFFNX AFRAH AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CHZPO CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IEGSK IFIPE IFJZH IPLJI JAVBF LAI MS~ O9- OCL P2P RIA RIE RIL RNS TAE TN5 VH1 VJK X7M ZGI ZXP AAYXX CITATION CGR CUY CVF ECM EIF NPM PKN RIG 7X8
ID	FETCH-LOGICAL-c321t-89381c0b88606a7daea8a869be6f12f610032daa3a0fd237146b41e8a2e065aa3
IEDL.DBID	RIE
ISICitedReferencesCount	30
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000360394900008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0018-9294 1558-2531
IngestDate	Sat Sep 27 23:00:11 EDT 2025 Wed Feb 19 02:07:57 EST 2025 Tue Nov 18 21:36:21 EST 2025 Sat Nov 29 05:34:17 EST 2025 Wed Aug 27 02:53:45 EDT 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	9
Keywords	functional connectivity Fiedler vector Consensus clustering electroencephalogram (EEG) spectral clustering
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c321t-89381c0b88606a7daea8a869be6f12f610032daa3a0fd237146b41e8a2e065aa3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
PMID	25807564
PQID	1706206574
PQPubID	23479
PageCount	12
ParticipantIDs	proquest_miscellaneous_1706206574 pubmed_primary_25807564 crossref_primary_10_1109_TBME_2015_2415733 ieee_primary_7064698 crossref_citationtrail_10_1109_TBME_2015_2415733
PublicationCentury	2000
PublicationDate	2015-Sept. 2015-9-00 2015-Sep 20150901
PublicationDateYYYYMMDD	2015-09-01
PublicationDate_xml	– month: 09 year: 2015 text: 2015-Sept.
PublicationDecade	2010
PublicationPlace	United States
PublicationPlace_xml	– name: United States
PublicationTitle	IEEE transactions on biomedical engineering
PublicationTitleAbbrev	TBME
PublicationTitleAlternate	IEEE Trans Biomed Eng
PublicationYear	2015
Publisher	IEEE
Publisher_xml	– name: IEEE
References	ref57 ref13 ref56 ref12 ref59 ref15 ref58 ref14 ref55 ref11 ref54 ng (ref52) 2001 ref10 ref17 ref16 ref19 ref18 frackowiak (ref26) 2003 rosenberg (ref66) 0 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 lin (ref53) 0 ref3 ref6 ref5 ref79 ref35 fiedler (ref39) 1975; 25 ref34 ref36 ref75 ref31 ref74 ref30 ref77 ref33 ref76 ref32 ref2 ref1 ref38 rodriguez (ref40) 1999; 397 strehl (ref37) 2003; 3 rijsbergen (ref65) 1979; 30 ref71 ref70 ref73 ref72 ref68 ref24 ref67 ref23 ref69 ref25 ref64 ref20 ref63 ref22 ref21 michel (ref78) 2004; 115 ref28 ref27 ref29 ref60 ref62 ref61
References_xml	– ident: ref35 doi: 10.1016/j.physd.2006.09.008 – ident: ref60 doi: 10.1073/pnas.0601602103 – ident: ref73 doi: 10.1016/j.clinph.2005.08.033 – ident: ref50 doi: 10.1137/1.9781611972757.25 – volume: 25 start-page: 619 year: 1975 ident: ref39 article-title: A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory publication-title: Czechoslovak Math J doi: 10.21136/CMJ.1975.101357 – ident: ref17 doi: 10.1093/cercor/bhq291 – ident: ref34 doi: 10.1103/PhysRevLett.97.238103 – ident: ref57 doi: 10.1109/ICDM.2004.10100 – ident: ref43 doi: 10.1016/S0987-7053(02)00301-5 – ident: ref20 doi: 10.1002/hbm.20353 – ident: ref7 doi: 10.1093/brain/awp086 – ident: ref59 doi: 10.1109/92.748202 – ident: ref61 doi: 10.1016/j.patrec.2009.11.001 – ident: ref18 doi: 10.1093/brain/awn018 – ident: ref31 doi: 10.1007/s11222-007-9033-z – ident: ref45 doi: 10.1109/TSP.2011.2144589 – ident: ref56 doi: 10.1109/ICDM.2007.73 – ident: ref46 doi: 10.1109/TIT.1968.1054157 – ident: ref28 doi: 10.1016/0165-0173(94)00016-I – ident: ref69 doi: 10.1093/bioinformatics/btq569 – volume: 30 start-page: 374 year: 1979 ident: ref65 article-title: Information retrieval publication-title: J Amer Soc Inf Sci – ident: ref14 doi: 10.1093/cercor/bhr269 – start-page: 410 year: 0 ident: ref66 article-title: V-measure: A conditional entropy-based external cluster evaluation measure publication-title: Proc Joint Conf Empirical Methods Natural Lang Process Comput Natural Lang Learn – ident: ref33 doi: 10.3389/neuro.11.037.2009 – ident: ref3 doi: 10.1038/35067550 – volume: 3 start-page: 583 year: 2003 ident: ref37 article-title: Cluster ensembles-A knowledge reuse framework for combining multiple partitions publication-title: J Mach Learn Res – ident: ref22 doi: 10.1103/PhysRevLett.104.118701 – ident: ref13 doi: 10.1371/journal.pone.0013701 – ident: ref48 doi: 10.1038/nrn2575 – ident: ref67 doi: 10.1109/18.61115 – ident: ref55 doi: 10.1007/BFb0097982 – ident: ref49 doi: 10.1186/1471-2105-6-260 – ident: ref44 doi: 10.1002/hbm.21000 – ident: ref5 doi: 10.1093/cercor/bhn216 – ident: ref27 doi: 10.1371/journal.pcbi.0010042 – ident: ref29 doi: 10.1016/S0730-725X(99)00102-2 – ident: ref32 doi: 10.1142/S0218127407019251 – ident: ref21 doi: 10.1371/journal.pcbi.1000100 – ident: ref10 doi: 10.1016/j.neurobiolaging.2008.07.019 – ident: ref2 doi: 10.1093/brain/awn262 – ident: ref15 doi: 10.1002/hbm.21030 – volume: 397 start-page: 430 year: 1999 ident: ref40 article-title: Perception's shadow: Long-distance synchronization of human brain activity publication-title: Nature doi: 10.1038/17120 – start-page: 849 year: 2001 ident: ref52 article-title: On spectral clustering: Analysis and an algorithm publication-title: Advances in neural information processing systems – ident: ref74 doi: 10.1016/j.clinph.2012.06.005 – ident: ref42 doi: 10.1103/PhysRevLett.81.3291 – ident: ref58 doi: 10.1137/1.9781611972740.35 – ident: ref9 doi: 10.1016/S1388-2457(02)00322-X – ident: ref1 doi: 10.1016/S1053-8119(03)00062-4 – ident: ref47 doi: 10.1186/1753-4631-1-3 – ident: ref19 doi: 10.1016/j.neuropsychologia.2010.11.024 – ident: ref62 doi: 10.1109/ICDMW.2010.63 – ident: ref71 doi: 10.1111/j.1467-9280.1993.tb00586.x – ident: ref76 doi: 10.1523/JNEUROSCI.4137-08.2009 – ident: ref25 doi: 10.1073/pnas.95.15.8420 – ident: ref75 doi: 10.1016/j.jneumeth.2012.10.004 – volume: 115 start-page: 2195 year: 2004 ident: ref78 article-title: EEG source imaging publication-title: Clin Neurophysiol doi: 10.1016/j.clinph.2004.06.001 – ident: ref72 doi: 10.1111/j.1467-9280.2007.01899.x – ident: ref63 doi: 10.1177/001316446002000104 – ident: ref70 doi: 10.1016/0013-4694(91)90062-9 – ident: ref11 doi: 10.1016/j.jneumeth.2007.09.026 – ident: ref54 doi: 10.1145/1015330.1015407 – ident: ref68 doi: 10.1016/j.ejor.2010.08.012 – ident: ref36 doi: 10.3389/fnins.2010.00200 – ident: ref51 doi: 10.1016/j.patrec.2009.09.011 – ident: ref38 doi: 10.1145/1217299.1217303 – ident: ref23 doi: 10.1063/1.3129783 – ident: ref24 doi: 10.1038/nature03288 – ident: ref77 doi: 10.1073/pnas.0605965104 – year: 2003 ident: ref26 publication-title: Human Brain Function – start-page: 655 year: 0 ident: ref53 article-title: Power iteration clustering publication-title: Proc 27th Int Conf Mach Learn – ident: ref41 doi: 10.1016/S0165-0270(01)00372-7 – ident: ref64 doi: 10.1002/sim.4780100512 – ident: ref8 doi: 10.1016/j.clinph.2009.12.010 – ident: ref16 doi: 10.1523/JNEUROSCI.0333-10.2010 – ident: ref12 doi: 10.1148/radiol.11110380 – ident: ref6 doi: 10.1142/S0129065707000932 – ident: ref79 doi: 10.1016/j.tics.2004.06.006 – ident: ref30 doi: 10.1109/TITB.2004.834406 – ident: ref4 doi: 10.1016/j.neuroimage.2010.03.081
SSID	ssj0014846
Score	2.3431697
Snippet	A central question in cognitive neuroscience is how cognitive functions depend on the integration of specialized widely distributed brain regions. In recent...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	2158
SubjectTerms	Algorithms Biomedical measurement Brain - physiology Brain Mapping - methods Cluster Analysis Clustering algorithms Communities Consensus clustering electroencephalogram (EEG) Electroencephalography Female Fiedler vector functional connectivity Humans Male Nerve Net - physiology Partitioning algorithms spectral clustering Symmetric matrices Vectors
Title	Hierarchical Spectral Consensus Clustering for Group Analysis of Functional Brain Networks
URI	https://ieeexplore.ieee.org/document/7064698 https://www.ncbi.nlm.nih.gov/pubmed/25807564 https://www.proquest.com/docview/1706206574
Volume	62
WOSCitedRecordID	wos000360394900008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-2531 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014846 issn: 0018-9294 databaseCode: RIE dateStart: 19640101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB6qiOjB96O-iOBJXJtN001ytKWllxYPCsXLkmazIJRWbOvvdya7XRRU8LYsm-zjm-x8yUzmA7iRce5cJi3-_YSPpOU8sjqREVJZQXw1U5kNYhNqONSjkXmswV21F8Z7H5LP_D0dhlh-NnNLWiprKPSfidFrsKaUKvZqVREDqYtNOTzGASyMLCOYMTeNp_agS0lcrXtyV6pJ2jmiRVV4E_nNHQV9ld-pZnA5vd3_Pewe7JTUkj0UtrAPNT89gO0vBQcPYHNQhtIP4aX_SnuPgxTKhJEMPa15MBLwJPWLOetMllREAdsxJLYsLFKxVQ0TNstZD11isZLI2iQ0wYZFSvn8CJ573adOPyqFFiLXFPEiQs6iY8fHWuN0xiI63mpEzIx9ksciR4bFmyKztml5ngmq8ZeMZey1FR4ZDJ4_hvXpbOpPgQmkgKYlfGKNl9xZg50Zp7RDIqpyw-vAV987dWUVchLDmKRhNsJNSmilhFZaolWH26rJW1GC46-LDwmK6sIShTpcr0BNcfxQUMRO_Ww5T6l8kMC3ULIOJwXaVeOVkZz93Ok5bNGti4yzC1hfvC_9JWy4j8Xr_P0KjXSkr4KRfgLds9_U
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFD54w8uDt3mZ1wg-iXVplrXJo4pjohs-TBBfSpamIIxNdvH3e07aFQUVfCulCWm_pOfLOTnnAziXYWZtKg3-_YQLpOE8MCqSAVJZQXw1jVPjxSbiTke9vOinObgsc2Gcc_7wmbuiSx_LT4d2Sq6yWoz2M9JqHhYbUoowz9YqYwZS5Wk5PMQlLLQsYpgh17XuTfuOjnE1rshgxXVSzxENqsMbyW8GySus_E42vdFpbvxvuJuwXpBLdp3Phi2Yc4NtWPtScnAblttFML0Cr603yj72Yih9RkL05PVgJOFJ-hdjdtufUhkFbMeQ2jLvpmKzKiZsmLEmGsXcl8huSGqCdfJD5eMdeG7edW9bQSG1ENi6CCcBshYVWt5TCjc0BvFxRiFmuueiLBQZcixeF6kxdcOzVFCVv6gnQ6eMcMhh8P4uLAyGA7cPTCAJ1A3hIqOd5NZo7EzbWFmkonGmeRX47HsntqhDTnIY_cTvR7hOCK2E0EoKtKpwUTZ5z4tw_PVwhaAoHyxQqMLZDNQEVxCFRczADafjhAoICXyLWFZhL0e7bDybJAc_d3oKK61u-zF5vO88HMIqDSM_f3YEC5PR1B3Dkv2YvI1HJ36qfgJjPOIz
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Hierarchical+Spectral+Consensus+Clustering+for+Group+Analysis+of+Functional+Brain+Networks&rft.jtitle=IEEE+transactions+on+biomedical+engineering&rft.au=Ozdemir%2C+Alp&rft.au=Bolanos%2C+Marcos&rft.au=Bernat%2C+Edward&rft.au=Aviyente%2C+Selin&rft.date=2015-09-01&rft.issn=0018-9294&rft.eissn=1558-2531&rft.volume=62&rft.issue=9&rft.spage=2158&rft.epage=2169&rft_id=info:doi/10.1109%2FTBME.2015.2415733&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TBME_2015_2415733
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9294&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9294&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9294&client=summon