Deep causality variational autoencoder network for identifying the potential biomarkers of brain disorders

Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs t...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on neural systems and rehabilitation engineering Vol. 32; p. 1
Main Authors:	Alfakih, Amani, Xia, Zhengwang, Ali, Bahzar, Mamoon, Saqib, Lu, Jianfeng
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Alzheimer Disease - diagnostic imaging Alzheimer's disease Alzheimer’s Disease (AD) Autism Autism Spectrum Disorder (ASD) Autoencoder Biomarkers Brain Brain - diagnostic imaging Brain mapping Brain modeling Causal inference Causality Data exchange Data models Diseases fMRI Functional magnetic resonance imaging Humans Magnetic Resonance Imaging - methods Medical imaging Neurodegenerative diseases Neuroimaging Neuroimaging - methods Time series Time series analysis
ISSN:	1534-4320, 1558-0210, 1558-0210
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs that characterize the causal relationship between brain regions based on time series data. To address this issue, we designed a novel model named deep causal variational autoencoder (CVAE) to estimate the causal relationship between brain regions. This network contains a causal layer that can estimate the causal relationship between different brain regions directly. Compared with previous approaches, our method relaxes many constraints on the structure of underlying causal graph. Our proposed method achieves excellent performance on both the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Autism Brain Imaging Data Exchange 1 (ABIDE1) databases. Moreover, the experimental results show that deep CVAE has promising applications in the field of brain disease identification.
AbstractList	Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs that characterize the causal relationship between brain regions based on time series data. To address this issue, we designed a novel model named deep causal variational autoencoder (CVAE) to estimate the causal relationship between brain regions. This network contains a causal layer that can estimate the causal relationship between different brain regions directly. Compared with previous approaches, our method relaxes many constraints on the structure of underlying causal graph. Our proposed method achieves excellent performance on both the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Autism Brain Imaging Data Exchange 1 (ABIDE1) databases. Moreover, the experimental results show that deep CVAE has promising applications in the field of brain disease identification. Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs that characterize the causal relationship between brain regions based on time series data. To address this issue, we designed a novel model named deep causal variational autoencoder (CVAE) to estimate the causal relationship between brain regions. This network contains a causal layer that can estimate the causal relationship between different brain regions directly. Compared with previous approaches, our method relaxes many constraints on the structure of underlying causal graph. Our proposed method achieves excellent performance on both the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Autism Brain Imaging Data Exchange 1 (ABIDE1) databases. Moreover, the experimental results show that deep CVAE has promising applications in the field of brain disease identification.Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs that characterize the causal relationship between brain regions based on time series data. To address this issue, we designed a novel model named deep causal variational autoencoder (CVAE) to estimate the causal relationship between brain regions. This network contains a causal layer that can estimate the causal relationship between different brain regions directly. Compared with previous approaches, our method relaxes many constraints on the structure of underlying causal graph. Our proposed method achieves excellent performance on both the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Autism Brain Imaging Data Exchange 1 (ABIDE1) databases. Moreover, the experimental results show that deep CVAE has promising applications in the field of brain disease identification.
Author	Alfakih, Amani Lu, Jianfeng Xia, Zhengwang Mamoon, Saqib Ali, Bahzar
Author_xml	– sequence: 1 givenname: Amani orcidid: 0009-0005-0168-0567 surname: Alfakih fullname: Alfakih, Amani organization: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 2 givenname: Zhengwang orcidid: 0000-0002-6815-5856 surname: Xia fullname: Xia, Zhengwang organization: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 3 givenname: Bahzar surname: Ali fullname: Ali, Bahzar organization: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 4 givenname: Saqib orcidid: 0000-0002-8392-5118 surname: Mamoon fullname: Mamoon, Saqib organization: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 5 givenname: Jianfeng orcidid: 0000-0002-9190-507X surname: Lu fullname: Lu, Jianfeng organization: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38113163$$D View this record in MEDLINE/PubMed
BookMark	eNp9kUtvEzEURkeoiD7gDyCELLFhk-D3Y4lKC5UqkCB7y-O5U5xOxsH2gPLv60lChbpgZcs63-ere86bkzGO0DSvCV4Sgs2H1dcf36-WFFO2ZIxzY8Sz5owIoReYEnwy3xlfcEbxaXOe8xpjoqRQL5pTpglhRLKzZv0JYIu8m7IbQtmh3y4FV0Ic3YDcVCKMPnaQ0AjlT0z3qI8JhQ7GEvpdGO9Q-QloG8v8UBNtiBuX7iFlFHvUJhdG1IUcU63IL5vnvRsyvDqeF83q-mp1-WVx--3zzeXH24XnkpaFNk573NZRjegZGKkN7Rh2nfCCYO-06ony4GjXMqc6jCXzVLaKSaNarNhFc3Oo7aJb220KdaKdjS7Y_UNMd9alEvwA1oCSnQNfmwSv-3Ccir5vDe455tyZ2vX-0LVN8dcEudhNyB6GwY0Qp2ypwZwIqRWv6Lsn6DpOqa5xpohQjGs-F749UlO7ge5xvL9CKkAPgE8x5wT9I0Kwna3bvXU7W7dH6zWkn4R8KHuLpSoY_h99c4gGAPjnLyYl1YY9AJmnuuQ
CODEN	ITNSB3
CitedBy_id	crossref_primary_10_1080_00949655_2025_2516793 crossref_primary_10_1109_TNSRE_2025_3562662 crossref_primary_10_34133_hds_0282 crossref_primary_10_1002_ima_70005 crossref_primary_10_1016_j_engappai_2025_111877
Cites_doi	10.3390/make1010019 10.1109/TMI.2019.2933160 10.1109/TPAMI.2015.2511754 10.1016/j.media.2019.101630 10.1609/aaai.v34i04.5921 10.1016/j.neuroimage.2013.07.019 10.1109/ICCV.2017.74 10.1007/s11517-022-02558-4 10.1023/A:1012487302797 10.1016/j.neuroimage.2016.07.058 10.1371/journal.pone.0015238 10.1109/JBHI.2018.2875456 10.1137/1.9781611977172.48 10.1038/npp.2009.115 10.1007/s00429-010-0283-8 10.1007/s11571-020-09630-5 10.26599/TST.2021.9010081 10.1109/JBHI.2019.2946676 10.1109/JBHI.2023.3265364 10.1016/j.neulet.2014.06.043 10.1016/j.jneumeth.2020.108884 10.1002/SERIES1345 10.31083/j.jin2102057 10.1002/hbm.23240 10.1093/cercor/bhs270 10.1016/j.jneumeth.2017.03.006 10.1038/s41598-021-87411-8 10.1038/s41598-021-93190-z 10.1198/jasa.2009.0126 10.1016/j.actbio.2021.06.024 10.1609/aaai.v37i7.26031 10.1007/BF02985802 10.1016/j.nicl.2014.07.003 10.1016/j.neuroimage.2010.02.059 10.1016/j.neucom.2021.06.083
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
DBID	97E ESBDL RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8 DOA
DOI	10.1109/TNSRE.2023.3344995
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed 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 Neurosciences 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 DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) 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 Neurosciences 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 MEDLINE - Academic
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 4 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Occupational Therapy & Rehabilitation
EISSN	1558-0210
EndPage	1
ExternalDocumentID	oai_doaj_org_article_9e76daecdb354316a425ffb90f4044a9 38113163 10_1109_TNSRE_2023_3344995 10366289
Genre	orig-research Research Support, Non-U.S. Gov't Journal Article
GrantInformation_xml	– fundername: Natural Science Foundation of Jiangsu Province grantid: BK20221487 funderid: 10.13039/501100004608
GroupedDBID	--- -~X 0R~ 29I 4.4 5GY 6IK 97E AAFWJ AAJGR AASAJ AAWTH ABAZT ABVLG ACGFO ACGFS ACIWK ACPRK AENEX AFPKN AFRAH ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS ESBDL F5P GROUPED_DOAJ HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL OK1 P2P RIA RIE RNS 53G 5VS AAYXX AETIX AGSQL AIBXA CITATION EJD H~9 CGR CUY CVF ECM EIF NPM RIG 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8
ID	FETCH-LOGICAL-c462t-89a8c0b17695f3e96892d30ad5c510ca87f17cea2db3a7d0063c26b73697b073
IEDL.DBID	RIE
ISICitedReferencesCount	6
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001144547700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1534-4320 1558-0210
IngestDate	Fri Oct 03 12:53:12 EDT 2025 Thu Jul 10 23:15:24 EDT 2025 Mon Jul 14 07:42:15 EDT 2025 Wed Feb 19 01:58:20 EST 2025 Sat Nov 29 01:47:18 EST 2025 Tue Nov 18 22:37:42 EST 2025 Wed Aug 27 02:35:10 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c462t-89a8c0b17695f3e96892d30ad5c510ca87f17cea2db3a7d0063c26b73697b073
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-6815-5856 0000-0002-8392-5118 0009-0005-0168-0567 0000-0002-9190-507X 0009-0006-4311-1212
OpenAccessLink	https://ieeexplore.ieee.org/document/10366289
PMID	38113163
PQID	2915734849
PQPubID	85423
PageCount	1
ParticipantIDs	doaj_primary_oai_doaj_org_article_9e76daecdb354316a425ffb90f4044a9 proquest_miscellaneous_2904156874 ieee_primary_10366289 pubmed_primary_38113163 proquest_journals_2915734849 crossref_primary_10_1109_TNSRE_2023_3344995 crossref_citationtrail_10_1109_TNSRE_2023_3344995
PublicationCentury	2000
PublicationDate	2024-01-01
PublicationDateYYYYMMDD	2024-01-01
PublicationDate_xml	– month: 01 year: 2024 text: 2024-01-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: New York
PublicationTitle	IEEE transactions on neural systems and rehabilitation engineering
PublicationTitleAbbrev	TNSRE
PublicationTitleAlternate	IEEE Trans Neural Syst Rehabil Eng
PublicationYear	2024
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	ref13 ref12 ref34 ref15 ref37 ref14 ref36 ref31 ref11 ref33 ref10 Kingma (ref30) 2013 ref32 Defferrard (ref28); 29 ref2 ref1 ref17 ref16 ref38 ref18 Zhou (ref22) 2023 Bruna (ref26) 2013 ref24 ref23 ref25 ref42 ref41 Yu (ref19) ref44 ref21 ref43 Springenberg (ref39) 2014 ref29 ref8 ref9 ref4 Kalainathan (ref20) 2022; 23 ref3 ref6 ref5 Guyon (ref35) 2002; 46 Kipf (ref27) 2016 ref40 Hoyer (ref7); 21
References_xml	– ident: ref14 doi: 10.3390/make1010019 – ident: ref40 doi: 10.1109/TMI.2019.2933160 – ident: ref18 doi: 10.1109/TPAMI.2015.2511754 – year: 2013 ident: ref26 article-title: Spectral networks and locally connected networks on graphs publication-title: arXiv:1312.6203 – ident: ref42 doi: 10.1016/j.media.2019.101630 – ident: ref21 doi: 10.1609/aaai.v34i04.5921 – ident: ref5 doi: 10.1016/j.neuroimage.2013.07.019 – ident: ref38 doi: 10.1109/ICCV.2017.74 – ident: ref43 doi: 10.1007/s11517-022-02558-4 – volume: 46 start-page: 389 issue: 1 year: 2002 ident: ref35 article-title: Gene selection for cancer classification using support vector machines publication-title: Mach. Learn. doi: 10.1023/A:1012487302797 – ident: ref11 doi: 10.1016/j.neuroimage.2016.07.058 – ident: ref9 doi: 10.1371/journal.pone.0015238 – ident: ref3 doi: 10.1109/JBHI.2018.2875456 – volume: 21 start-page: 1 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref7 article-title: Nonlinear causal discovery with additive noise models – ident: ref29 doi: 10.1137/1.9781611977172.48 – year: 2023 ident: ref22 article-title: On the opportunity of causal deep generative models: A survey and future directions publication-title: arXiv:2301.12351 – ident: ref24 doi: 10.1038/npp.2009.115 – ident: ref37 doi: 10.1007/s00429-010-0283-8 – volume: 23 start-page: 9831 issue: 1 year: 2022 ident: ref20 article-title: Structural agnostic modeling: Adversarial learning of causal graphs publication-title: J. Mach. Learn. Res. – ident: ref12 doi: 10.1007/s11571-020-09630-5 – year: 2013 ident: ref30 article-title: Auto-encoding variational Bayes publication-title: arXiv:1312.6114 – ident: ref15 doi: 10.26599/TST.2021.9010081 – ident: ref17 doi: 10.1109/JBHI.2019.2946676 – ident: ref33 doi: 10.1109/JBHI.2023.3265364 – ident: ref2 doi: 10.1016/j.neulet.2014.06.043 – ident: ref4 doi: 10.1016/j.jneumeth.2020.108884 – ident: ref13 doi: 10.1002/SERIES1345 – ident: ref44 doi: 10.31083/j.jin2102057 – ident: ref41 doi: 10.1002/hbm.23240 – ident: ref23 doi: 10.1093/cercor/bhs270 – volume: 29 start-page: 1 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref28 article-title: Convolutional neural networks on graphs with fast localized spectral filtering – ident: ref10 doi: 10.1016/j.jneumeth.2017.03.006 – ident: ref16 doi: 10.1038/s41598-021-87411-8 – start-page: 7154 volume-title: Proc. 36th Int. Conf. Mach. Learn. ident: ref19 article-title: DAG-GNN: DAG structure learning with graph neural networks – year: 2016 ident: ref27 article-title: Semi-supervised classification with graph convolutional networks publication-title: arXiv:1609.02907 – ident: ref32 doi: 10.1038/s41598-021-93190-z – ident: ref1 doi: 10.1198/jasa.2009.0126 – year: 2014 ident: ref39 article-title: Striving for simplicity: The all convolutional net publication-title: arXiv:1412.6806 – ident: ref25 doi: 10.1016/j.actbio.2021.06.024 – ident: ref34 doi: 10.1609/aaai.v37i7.26031 – ident: ref36 doi: 10.1007/BF02985802 – ident: ref6 doi: 10.1016/j.nicl.2014.07.003 – ident: ref31 doi: 10.1016/j.neuroimage.2010.02.059 – ident: ref8 doi: 10.1016/j.neucom.2021.06.083
SSID	ssj0017657
Score	2.4545255
Snippet	Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection...
SourceID	doaj proquest pubmed crossref ieee
SourceType	Open Website Aggregation Database Index Database Enrichment Source Publisher
StartPage	1
SubjectTerms	Alzheimer Disease - diagnostic imaging Alzheimer's disease Alzheimer’s Disease (AD) Autism Autism Spectrum Disorder (ASD) Autoencoder Biomarkers Brain Brain - diagnostic imaging Brain mapping Brain modeling Causal inference Causality Data exchange Data models Diseases fMRI Functional magnetic resonance imaging Humans Magnetic Resonance Imaging - methods Medical imaging Neurodegenerative diseases Neuroimaging Neuroimaging - methods Time series Time series analysis
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQxYEL4lEgUJCRgAtKm8SOHR-7pRUHtKrKCvVm-SkVwabaZPn9zNjJansALlxjO7HmYX8Te74h5F3jrI3GuRIWPl5yUzelkl6UouK-sV0Q3icS1y9yueyur9XlXqkvvBOW6YGz4E5UkMKb4LxlKW3bgJHFaFUVecW5Sal7lVRzMDWdH0jRyjlFplInq-XXq_NjrBR-zBgHkN_e2YYSW_9UXuXPSDPtOBePyMMJKtLTPMXH5F5YPyHv92mB6SpzAtAP9OoO4_ZT8v1TCLf0zGyHBLTpN4iJ51Gn27FH_kofNnSZr4FTwK40J-2mxCcKuJBe9iM-gBGLm_4n3uPZDLSPdIFlJejM2zkcktXF-erscznVVSgdF81Ydsp0rrIgJNVGFpToVONZZXzrwEOd6WSspQumAZkb6RHFuEZYyYSSFpaEZ-Rg3a_DC0KDjKITSC8AsArAAwRPXNkmIkuadS0rSD1LWbtJAlj64odOsUeldNKMRs3oSTMF-bgbc5sZN_7ae4HK2_VEtuz0AGxITzak_2VDBTlE1e99jgkBkWhBjmZb0JNrD7pRdYuUQBya3-6awSnxpMWsQ7_FPsh8IDrJC_I829Du5QCRapgDe_k_Zv6KPABp8PxP6IgcjJtteE3uu1_jzbB5k3ziN8wTDww priority: 102 providerName: Directory of Open Access Journals
Title	Deep causality variational autoencoder network for identifying the potential biomarkers of brain disorders
URI	https://ieeexplore.ieee.org/document/10366289 https://www.ncbi.nlm.nih.gov/pubmed/38113163 https://www.proquest.com/docview/2915734849 https://www.proquest.com/docview/2904156874 https://doaj.org/article/9e76daecdb354316a425ffb90f4044a9
Volume	32
WOSCitedRecordID	wos001144547700001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1558-0210 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017657 issn: 1534-4320 databaseCode: DOA dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-0210 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017657 issn: 1534-4320 databaseCode: RIE dateStart: 20010101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELZo1QMXWkqhoWVlJOCCsiS2Y8dHHq04oBUqe9hb5FekIpSsNkl_PzNONmoPReIW-RE_xmN_fsw3hLxjztraOJfCxCdSYXKWauVlKjPhmS2D9D6SuP5Qq1W52eifk7F6tIUJIcTHZ2GJn_Eu37duwKMy0HAuJewQDsiBUnI01pqvDJSMtJ6gwVAmZ9neQibTn9arXzdXS3QUvuRcAMYvHqxCkax_8q7yONCMC8718X9W9YQ8m5Al_TwOhefkSWhOyfv7LMJ0PVII0A_05gFB9wvy-1sIW-rM0EVcTu9gC73PZYa-RbpLH3a0GV-NU4C69Dba-EY7KQowkm7bHgMgB9r047OfXUfbmlr0QkH9RPPZnZH19dX66_d0csOQOiFZn5balC6z0MG6qHnQstTM88z4woFCO1OqOlcuGOYtN8oj6HFMWsWlVhZmkJfksGmbcE5oULUsJbIRAAoDrAF7LaEtq5FUzbqCJyTfS6VyUw-gp4w_VdyqZLqKkqxQktUkyYR8nPNsR4KOf6b-gsKeUyK5dgwA4VWTrlY6KOlNcNCeyBRgYF6ra6uzWmRCGJ2QMxT4veJGWSfkcj92qmkm6Cqm8wIZhAREv52jQYfxYsY0oR0wDRIlyFKJhLwax9z8c0BUOdSBv36k0AvyFBooxlOhS3LY74bwhhy5u_622y1AUTblIh40LKK6_AV7CxER
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Jb9QwFLagINELaymBAkYCLihDYjt2fGRpVcQwQmUOvUXeIhWhZDRJ-vt5z8lE7aFI3CIv8fL87M_L-x4hb5mztjbOpTDxiVSYnKVaeZnKTHhmyyC9jySuS7Valefn-udkrB5tYUII8fFZWOBnvMv3rRvwqAw0nEsJO4Tb5E4hBMtGc6350kDJSOwJOgylcpbtbGQy_XG9-nV2vEBX4QvOBaD84to6FOn6J_8qN0PNuOScPPjPyj4k9ydsST-Ng-ERuRWax-TdVR5huh5JBOh7enaNovsJ-f01hA11ZugiMqeXsIne5TJD3yLhpQ9b2ozvximAXXoRrXyjpRQFIEk3bY8BkAOt-vHhz7ajbU0t-qGgfiL67A7I-uR4_eU0nRwxpE5I1qelNqXLLHSwLmoetCw18zwzvnCg0s6Uqs6VC4Z5y43yCHsck1ZxqZWFOeQp2WvaJjwjNKhalhL5CACHAdqA3ZbQltVIq2ZdwROS76RSuakH0FfGnypuVjJdRUlWKMlqkmRCPsx5NiNFxz9Tf0ZhzymRXjsGgPCqSVsrHZT0JjhoT-QKMDCz1bXVWS0yIYxOyAEK_Epxo6wTcrQbO9U0F3QV03mBHEICot_M0aDFeDVjmtAOmAapEmSpREIOxzE3_xwwVQ514M9vKPQ1uXe6_rGslt9W31-QfWisGM-Ijshevx3CS3LXXfYX3fZVVJe_8P8Sew
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=Deep+Causality+Variational+Autoencoder+Network+for+Identifying+the+Potential+Biomarkers+of+Brain+Disorders&rft.jtitle=IEEE+transactions+on+neural+systems+and+rehabilitation+engineering&rft.au=Alfakih%2C+Amani&rft.au=Xia%2C+Zhengwang&rft.au=Ali%2C+Bahzar&rft.au=Mamoon%2C+Saqib&rft.date=2024-01-01&rft.issn=1558-0210&rft.eissn=1558-0210&rft.volume=32&rft.spage=112&rft_id=info:doi/10.1109%2FTNSRE.2023.3344995&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1534-4320&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1534-4320&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1534-4320&client=summon