Accurately Modeling the Resting Brain Functional Correlations Using Wave Equation With Spatiotemporal Varying Hypergraph Laplacian

How spontaneous brain neural activities emerge from the underlying anatomical architecture, characterized by structural connectivity (SC), has puzzled researchers for a long time. Over the past decades, much effort has been directed toward the graph modeling of SC, in which the brain SC is generally...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on medical imaging Vol. 41; no. 12; pp. 3787 - 3798
Main Authors:	Wang, Yanjiang, Ma, Jichao, Chen, Xue, Liu, Baodi
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Brain Brain connectivity Brain modeling Correlation Couplings Formulas (mathematics) Graph representations Graph theory Graphical representations Laplace equations Mathematical models Modelling Neural activity Neural networks Predictive models Propagation resonance Resonant frequencies spatiotemporal varying hypergraph Laplacian structure-function relation wave equation Wave equations Wave propagation
ISSN:	0278-0062, 1558-254X, 1558-254X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	How spontaneous brain neural activities emerge from the underlying anatomical architecture, characterized by structural connectivity (SC), has puzzled researchers for a long time. Over the past decades, much effort has been directed toward the graph modeling of SC, in which the brain SC is generally considered as relatively invariant. However, the graph representation of SC is unable to directly describe the connections between anatomically unconnected brain regions and fail to model the negative functional correlations. Here, we extend the static graph model to a spatiotemporal varying hypergraph Laplacian diffusion (STV-HGLD) model to describe the propagation of the spontaneous neural activity in human brain by incorporating the Laplacian of the hypergraph representation of the structural connectome (<inline-formula> <tex-math notation="LaTeX">{h} </tex-math></inline-formula>SC) into the regular wave equation. Theoretical solution shows that the dynamic functional couplings between brain regions fluctuate in the form of an exponential wave regulated by the spatiotemporal varying Laplacian of <inline-formula> <tex-math notation="LaTeX">{h} </tex-math></inline-formula>SC. Empirical study suggests that the cortical wave might give rise to resonance with SC during the self-organizing interplay between excitation and inhibition among brain regions, which orchestrates the cortical waves propagating with harmonics emanating from the <inline-formula> <tex-math notation="LaTeX">{h} </tex-math></inline-formula>SC while being bound by the natural frequencies of SC. Besides, the average statistical dependencies between brain regions, normally defined as the functional connectivity (FC), arises just at the moment before the cortical wave reaches the steady state after the wave spreads across all the brain regions. Comprehensive tests on four extensively studied empirical brain connectome datasets with different resolutions confirm our theory and findings.
AbstractList	How spontaneous brain neural activities emerge from the underlying anatomical architecture, characterized by structural connectivity (SC), has puzzled researchers for a long time. Over the past decades, much effort has been directed toward the graph modeling of SC, in which the brain SC is generally considered as relatively invariant. However, the graph representation of SC is unable to directly describe the connections between anatomically unconnected brain regions and fail to model the negative functional correlations. Here, we extend the static graph model to a spatiotemporal varying hypergraph Laplacian diffusion (STV-HGLD) model to describe the propagation of the spontaneous neural activity in human brain by incorporating the Laplacian of the hypergraph representation of the structural connectome ([Formula Omitted]SC) into the regular wave equation. Theoretical solution shows that the dynamic functional couplings between brain regions fluctuate in the form of an exponential wave regulated by the spatiotemporal varying Laplacian of [Formula Omitted]SC. Empirical study suggests that the cortical wave might give rise to resonance with SC during the self-organizing interplay between excitation and inhibition among brain regions, which orchestrates the cortical waves propagating with harmonics emanating from the [Formula Omitted]SC while being bound by the natural frequencies of SC. Besides, the average statistical dependencies between brain regions, normally defined as the functional connectivity (FC), arises just at the moment before the cortical wave reaches the steady state after the wave spreads across all the brain regions. Comprehensive tests on four extensively studied empirical brain connectome datasets with different resolutions confirm our theory and findings. How spontaneous brain neural activities emerge from the underlying anatomical architecture, characterized by structural connectivity (SC), has puzzled researchers for a long time. Over the past decades, much effort has been directed toward the graph modeling of SC, in which the brain SC is generally considered as relatively invariant. However, the graph representation of SC is unable to directly describe the connections between anatomically unconnected brain regions and fail to model the negative functional correlations. Here, we extend the static graph model to a spatiotemporal varying hypergraph Laplacian diffusion (STV-HGLD) model to describe the propagation of the spontaneous neural activity in human brain by incorporating the Laplacian of the hypergraph representation of the structural connectome ( h SC) into the regular wave equation. Theoretical solution shows that the dynamic functional couplings between brain regions fluctuate in the form of an exponential wave regulated by the spatiotemporal varying Laplacian of h SC. Empirical study suggests that the cortical wave might give rise to resonance with SC during the self-organizing interplay between excitation and inhibition among brain regions, which orchestrates the cortical waves propagating with harmonics emanating from the h SC while being bound by the natural frequencies of SC. Besides, the average statistical dependencies between brain regions, normally defined as the functional connectivity (FC), arises just at the moment before the cortical wave reaches the steady state after the wave spreads across all the brain regions. Comprehensive tests on four extensively studied empirical brain connectome datasets with different resolutions confirm our theory and findings.How spontaneous brain neural activities emerge from the underlying anatomical architecture, characterized by structural connectivity (SC), has puzzled researchers for a long time. Over the past decades, much effort has been directed toward the graph modeling of SC, in which the brain SC is generally considered as relatively invariant. However, the graph representation of SC is unable to directly describe the connections between anatomically unconnected brain regions and fail to model the negative functional correlations. Here, we extend the static graph model to a spatiotemporal varying hypergraph Laplacian diffusion (STV-HGLD) model to describe the propagation of the spontaneous neural activity in human brain by incorporating the Laplacian of the hypergraph representation of the structural connectome ( h SC) into the regular wave equation. Theoretical solution shows that the dynamic functional couplings between brain regions fluctuate in the form of an exponential wave regulated by the spatiotemporal varying Laplacian of h SC. Empirical study suggests that the cortical wave might give rise to resonance with SC during the self-organizing interplay between excitation and inhibition among brain regions, which orchestrates the cortical waves propagating with harmonics emanating from the h SC while being bound by the natural frequencies of SC. Besides, the average statistical dependencies between brain regions, normally defined as the functional connectivity (FC), arises just at the moment before the cortical wave reaches the steady state after the wave spreads across all the brain regions. Comprehensive tests on four extensively studied empirical brain connectome datasets with different resolutions confirm our theory and findings. How spontaneous brain neural activities emerge from the underlying anatomical architecture, characterized by structural connectivity (SC), has puzzled researchers for a long time. Over the past decades, much effort has been directed toward the graph modeling of SC, in which the brain SC is generally considered as relatively invariant. However, the graph representation of SC is unable to directly describe the connections between anatomically unconnected brain regions and fail to model the negative functional correlations. Here, we extend the static graph model to a spatiotemporal varying hypergraph Laplacian diffusion (STV-HGLD) model to describe the propagation of the spontaneous neural activity in human brain by incorporating the Laplacian of the hypergraph representation of the structural connectome (<inline-formula> <tex-math notation="LaTeX">{h} </tex-math></inline-formula>SC) into the regular wave equation. Theoretical solution shows that the dynamic functional couplings between brain regions fluctuate in the form of an exponential wave regulated by the spatiotemporal varying Laplacian of <inline-formula> <tex-math notation="LaTeX">{h} </tex-math></inline-formula>SC. Empirical study suggests that the cortical wave might give rise to resonance with SC during the self-organizing interplay between excitation and inhibition among brain regions, which orchestrates the cortical waves propagating with harmonics emanating from the <inline-formula> <tex-math notation="LaTeX">{h} </tex-math></inline-formula>SC while being bound by the natural frequencies of SC. Besides, the average statistical dependencies between brain regions, normally defined as the functional connectivity (FC), arises just at the moment before the cortical wave reaches the steady state after the wave spreads across all the brain regions. Comprehensive tests on four extensively studied empirical brain connectome datasets with different resolutions confirm our theory and findings.
Author	Wang, Yanjiang Liu, Baodi Ma, Jichao Chen, Xue
Author_xml	– sequence: 1 givenname: Yanjiang orcidid: 0000-0001-9910-7884 surname: Wang fullname: Wang, Yanjiang email: yjwang@upc.edu.cn organization: College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China – sequence: 2 givenname: Jichao surname: Ma fullname: Ma, Jichao email: majichao0110@126.com organization: College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China – sequence: 3 givenname: Xue surname: Chen fullname: Chen, Xue email: wmtmdlove@163.com organization: Institute of Digital Medicine and Computer-Assisted Surgery, Qingdao University, Qingdao, China – sequence: 4 givenname: Baodi surname: Liu fullname: Liu, Baodi email: liubaodi@upc.edu.cn organization: College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
BookMark	eNp9kc1vEzEQxS1URNPCHYmLJS5cNoy_18cStbRSKiRoKbeV4ziNK2e9tb1IufKX420qDj1wGo_1ezN6807QUR97h9B7AnNCQH--ub6aU6B0zoiWAOoVmhEh2oYK_usIzYCqtgGQ9Bid5PwAQLgA_QYdM6EpYRxm6M-ZtWMyxYU9vo5rF3x_j8vW4e8ul-n9JRnf44uxt8XH3gS8iCm5YKYu49s8MXfmt8Pnj-PTJ77zZYt_DFNT3G6IqYp-mrSfyMv94NJ9MsMWL80QjPWmf4teb0zI7t1zPUW3F-c3i8tm-e3r1eJs2VhGeWmIAWWUXGstgBBhtXMrtqYbayU1fE01V9RoaTmxVhPFdGvEykpYMdNqqjQ7RZ8Oc4cUH8dqr9v5bF0IpndxzB2VWknFKZ_Qjy_Qhzim6r5SigtCWyKgUnCgbIo5J7fphuR31WlHoJvy6Wo-3ZRP95xPlcgXEuvL09lKvXP4n_DDQeidc__26JZroJL9BYJPnl0
CODEN	ITMID4
CitedBy_id	crossref_primary_10_1007_s10115_025_02468_5 crossref_primary_10_1088_1741_2552_ad39a6 crossref_primary_10_1007_s12124_024_09853_9
Cites_doi	10.1007/s13311-018-0663-y 10.1038/44730 10.1177/1073858417728032 10.1038/nrn2201 10.1103/PhysRevE.90.012707 10.1016/j.neucom.2019.06.068 10.1371/journal.pbio.0060159 10.1073/pnas.0701519104 10.1523/JNEUROSCI.1091-13.2013 10.1109/TGRS.2018.2867570 10.3390/math9182345 10.1016/j.ijpsycho.2015.02.011 10.1002/hbm.20737 10.1109/JAS.2017.7510880 10.1016/j.neuroimage.2013.12.039 10.1016/j.media.2020.101799 10.1016/j.neuroimage.2009.10.003 10.1126/science.1238411 10.1038/s41598-017-18769-x 10.1038/s41467-018-04614-w 10.1016/j.neuroimage.2007.02.012 10.1038/s41467-019-12765-7 10.1002/hbm.460020107 10.1089/brain.2015.0408 10.1523/JNEUROSCI.4423-13.2014 10.1073/pnas.0811168106 10.1016/j.neucom.2020.11.021 10.1016/j.neuroimage.2013.09.071 10.1371/journal.pcbi.1005325 10.1016/j.neuroimage.2018.02.016 10.1093/cercor/bhr234 10.1073/pnas.1219562110 10.1038/nrn2575 10.1073/pnas.1315529111 10.1016/j.neuroimage.2016.04.050 10.1016/j.neuroimage.2014.04.038 10.1109/TMI.2014.2341732 10.1371/journal.pcbi.1008310 10.1038/srep10532 10.1038/nature18933 10.1038/ncomms10340 10.1109/ACCESS.2020.3039837
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E RIA RIE AAYXX CITATION 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8
DOI	10.1109/TMI.2022.3196007
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library (IEL) (UW System Shared) CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Nursing & Allied Health Premium Biotechnology and BioEngineering Abstracts MEDLINE - Academic
DatabaseTitle	CrossRef Materials Research Database Civil Engineering Abstracts Aluminium Industry Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Ceramic Abstracts Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database Nursing & Allied Health Premium Engineered Materials Abstracts Biotechnology Research Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic
DatabaseTitleList	Materials Research Database MEDLINE - Academic
Database_xml	– sequence: 1 dbid: RIE name: IEL url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine Engineering
EISSN	1558-254X
EndPage	3798
ExternalDocumentID	10_1109_TMI_2022_3196007 9849026
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 62072468 funderid: 10.13039/501100001809
GroupedDBID	--- -DZ -~X .GJ 0R~ 29I 4.4 53G 5GY 5RE 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT ACPRK AENEX AETIX AFRAH AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IBMZZ ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 VH1 AAYXX CITATION 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8
ID	FETCH-LOGICAL-c324t-1a07a76d9950115c9eeb3d2fcc62a4d29472a96c41cc917398a5bc60b3a892793
IEDL.DBID	RIE
ISICitedReferencesCount	5
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000907324600026&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0278-0062 1558-254X
IngestDate	Thu Oct 02 06:09:09 EDT 2025 Mon Jun 30 06:34:32 EDT 2025 Sat Nov 29 05:14:10 EST 2025 Tue Nov 18 22:28:57 EST 2025 Wed Aug 27 02:15:09 EDT 2025
IsPeerReviewed	false
IsScholarly	true
Issue	12
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-c324t-1a07a76d9950115c9eeb3d2fcc62a4d29472a96c41cc917398a5bc60b3a892793
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0001-9910-7884
PMID	35921340
PQID	2745128150
PQPubID	85460
PageCount	12
ParticipantIDs	proquest_journals_2745128150 crossref_primary_10_1109_TMI_2022_3196007 crossref_citationtrail_10_1109_TMI_2022_3196007 proquest_miscellaneous_2697674249 ieee_primary_9849026
PublicationCentury	2000
PublicationDate	2022-12-01
PublicationDateYYYYMMDD	2022-12-01
PublicationDate_xml	– month: 12 year: 2022 text: 2022-12-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on medical imaging
PublicationTitleAbbrev	TMI
PublicationYear	2022
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref35 ref13 ref34 ref37 ref15 ref14 ref30 ref33 ref11 ref32 ref10 goñi (ref12) 2014; 111 ref2 ref1 ref39 ref17 ref38 ref16 ref19 ref18 zhou (ref31) 2006 o’reilly (ref36) 2020 ref24 ref23 ref26 ref25 ref20 ref42 ref41 ref22 ref44 ref21 ref43 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40
References_xml	– ident: ref38 doi: 10.1007/s13311-018-0663-y – ident: ref40 doi: 10.1038/44730 – ident: ref42 doi: 10.1177/1073858417728032 – ident: ref5 doi: 10.1038/nrn2201 – start-page: 65 year: 2020 ident: ref36 publication-title: Computational Cognitive Neuroscience – ident: ref25 doi: 10.1103/PhysRevE.90.012707 – ident: ref33 doi: 10.1016/j.neucom.2019.06.068 – ident: ref4 doi: 10.1371/journal.pbio.0060159 – ident: ref18 doi: 10.1073/pnas.0701519104 – ident: ref20 doi: 10.1523/JNEUROSCI.1091-13.2013 – ident: ref32 doi: 10.1109/TGRS.2018.2867570 – ident: ref30 doi: 10.3390/math9182345 – ident: ref16 doi: 10.1016/j.ijpsycho.2015.02.011 – ident: ref7 doi: 10.1002/hbm.20737 – ident: ref14 doi: 10.1109/JAS.2017.7510880 – ident: ref27 doi: 10.1016/j.neuroimage.2013.12.039 – ident: ref26 doi: 10.1016/j.media.2020.101799 – ident: ref11 doi: 10.1016/j.neuroimage.2009.10.003 – ident: ref1 doi: 10.1126/science.1238411 – ident: ref24 doi: 10.1038/s41598-017-18769-x – ident: ref44 doi: 10.1038/s41467-018-04614-w – ident: ref2 doi: 10.1016/j.neuroimage.2007.02.012 – ident: ref43 doi: 10.1038/s41467-019-12765-7 – ident: ref6 doi: 10.1002/hbm.460020107 – ident: ref22 doi: 10.1089/brain.2015.0408 – ident: ref21 doi: 10.1523/JNEUROSCI.4423-13.2014 – ident: ref8 doi: 10.1073/pnas.0811168106 – ident: ref29 doi: 10.1016/j.neucom.2020.11.021 – ident: ref3 doi: 10.1016/j.neuroimage.2013.09.071 – ident: ref23 doi: 10.1371/journal.pcbi.1005325 – ident: ref28 doi: 10.1016/j.neuroimage.2018.02.016 – ident: ref41 doi: 10.1093/cercor/bhr234 – ident: ref9 doi: 10.1073/pnas.1219562110 – ident: ref10 doi: 10.1038/nrn2575 – volume: 111 start-page: 833 year: 2014 ident: ref12 article-title: Resting-brain functional connectivity predicted by analytic measures of network communication publication-title: Proc Nat Acad Sci USA doi: 10.1073/pnas.1315529111 – ident: ref35 doi: 10.1016/j.neuroimage.2016.04.050 – ident: ref15 doi: 10.1016/j.neuroimage.2014.04.038 – ident: ref17 doi: 10.1109/TMI.2014.2341732 – ident: ref34 doi: 10.1371/journal.pcbi.1008310 – ident: ref39 doi: 10.1038/srep10532 – start-page: 1601 year: 2006 ident: ref31 article-title: Learning with hypergraphs: Clustering, classification, and embedding publication-title: Proc Neural Inf Process Syst – ident: ref37 doi: 10.1038/nature18933 – ident: ref19 doi: 10.1038/ncomms10340 – ident: ref13 doi: 10.1109/ACCESS.2020.3039837
SSID	ssj0014509
Score	2.430817
Snippet	How spontaneous brain neural activities emerge from the underlying anatomical architecture, characterized by structural connectivity (SC), has puzzled...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	3787
SubjectTerms	Brain Brain connectivity Brain modeling Correlation Couplings Formulas (mathematics) Graph representations Graph theory Graphical representations Laplace equations Mathematical models Modelling Neural activity Neural networks Predictive models Propagation resonance Resonant frequencies spatiotemporal varying hypergraph Laplacian structure-function relation wave equation Wave equations Wave propagation
Title	Accurately Modeling the Resting Brain Functional Correlations Using Wave Equation With Spatiotemporal Varying Hypergraph Laplacian
URI	https://ieeexplore.ieee.org/document/9849026 https://www.proquest.com/docview/2745128150 https://www.proquest.com/docview/2697674249
Volume	41
WOSCitedRecordID	wos000907324600026&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: IEL customDbUrl: eissn: 1558-254X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014509 issn: 0278-0062 databaseCode: RIE dateStart: 19820101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS9xAEB9UiuhDP7TFa61soS-Fxttskt3MoxUPCyqltXpvYXezocJx1553Ql_7l3dmkwsWS6FvC9l8wMxk5rcz8xuAt66RyhYNJjZkhgCKN4lL6yZx5Awa53KfxgP9qzNzcVGOx_hpDd73vTAhhFh8Fg55GXP59cwv-ahsiGWOhBnWYd0Y3fZq9RmDvGjLORQzxkqtVilJicPL848EBJU6ZHUjn7gFm1mBTGUm__BGcbzKg39ydDSjJ__3iU_hcRdQiqNWA57BWpjuwPY9msEd2DzvEui78OvI-yWzQ0x-Ch6Dxs3ogmJA8ZnZNmj9gUdGiBF5u_aQUBzz-I6uYE7EAgNxbe-COPnRkoSL65vFN_ElFmZ3PFcTcWXn3D4lTgnlziMntjizXP5Fyvgcvo5OLo9Pk24MQ-Ip2lokqZXGGl0jFhw_egwEwGvVeK-VzWuFuVEWtc9T7wn8ZVjawnktXWZLVGT_L2BjOpuGPRDBalkGa4PGLPeuQROwKUrtah28CekAhitxVL7jKOdRGZMqYhWJFcmyYllWnSwH8K6_43vLz_GPvbsssH5fJ6sB7K8kXnUGfFsRWC84yVjIAbzpL5PpcT7FTsNsSXs0MhUSAdiXf3_yK9ji97fVL_uwsZgvw2t45O8WN7fzA9LicXkQtfg3nOTuzQ
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3faxQxEB5qlVof_NEqnlaN4Ivg9rLZJLt5rKXHFe8O0bPt25Jks7Rw3On1ruCrf7kz2b1FUQTfApssgZlk5svMfAPwxtVcWFWbxIYsR4Di88SlVZ04NAa1c9Kn8UH_bJRPJsXFhfm4Be-6WpgQQkw-C4c0jLH8auHX9FTWN4U0iBluwW0lpeBNtVYXM5CqSegQxBnLtdgEJbnpT8enCAWFOCSFQ6u4CzuZMkRmxn-zR7HByh-3cjQ1gwf_t8mHcL91KdlRowOPYCvM9-DeL0SDe7AzbkPo-_DjyPs18UPMvjNqhEbl6Ay9QPaJ-DZw_J6aRrAB2rvmmZAdUwOPNmWOxRQDdm5vAjv51tCEs_Or1SX7HFOzW6arGTuzSyqgYkPEucvIis1GlhLAUB0fw5fByfR4mLSNGBKP_tYqSS3Pba4rYxR5kN4EhOCVqL3XwspKGJkLa7SXqfcI_zJTWOW85i6zhRF4AzyB7fliHp4CC1bzIlgbtMmkd7XJg6lVoV2lg89D2oP-Rhylb1nKqVnGrIxohZsSZVmSLMtWlj1426342jB0_GPuPgmsm9fKqgcHG4mX7RG-LhGuKwozKt6D191nPHwUUbHzsFjjHG2IDAkh7LO___kV3B1Ox6NydDr58Bx2aS9NLswBbK-W6_AC7vib1dX18mXU5Z-KHvEs
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=Accurately+Modeling+the+Resting+Brain+Functional+Correlations+Using+Wave+Equation+With+Spatiotemporal+Varying+Hypergraph+Laplacian&rft.jtitle=IEEE+transactions+on+medical+imaging&rft.au=Wang%2C+Yanjiang&rft.au=Ma%2C+Jichao&rft.au=Chen%2C+Xue&rft.au=Liu%2C+Baodi&rft.date=2022-12-01&rft.pub=IEEE&rft.issn=0278-0062&rft.volume=41&rft.issue=12&rft.spage=3787&rft.epage=3798&rft_id=info:doi/10.1109%2FTMI.2022.3196007&rft_id=info%3Apmid%2F35921340&rft.externalDocID=9849026
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0278-0062&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0278-0062&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0278-0062&client=summon