Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification

If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on neural systems and rehabilitation engineering Ročník 29; s. 215 - 229
Hlavní autoři:	Zhang, Bingtao, Yan, Guanghui, Yang, Zhifei, Su, Yun, Wang, Jinfeng, Lei, Tao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	abnormal topological architecture Biomarkers Brain Brain architecture brain functional networks Classification Clustering Complex systems Conductors Correlation analysis Depression EEG Electrodes Electroencephalography Feature extraction Frequencies Functional magnetic resonance imaging Major depressive disorder Measurement Mental depression Phase lag phase lag index Statistical analysis Synchronism Synchronization
ISSN:	1534-4320, 1558-0210, 1558-0210
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.
AbstractList	If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken. If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.
Author	Wang, Jinfeng Zhang, Bingtao Lei, Tao Yan, Guanghui Su, Yun Yang, Zhifei
Author_xml	– sequence: 1 givenname: Bingtao orcidid: 0000-0003-3643-3580 surname: Zhang fullname: Zhang, Bingtao email: zhangbingtao321@163.com organization: School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China – sequence: 2 givenname: Guanghui surname: Yan fullname: Yan, Guanghui organization: School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China – sequence: 3 givenname: Zhifei surname: Yang fullname: Yang, Zhifei organization: School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China – sequence: 4 givenname: Yun surname: Su fullname: Su, Yun organization: School of Information Science and Engineering, Lanzhou University, Lanzhou, China – sequence: 5 givenname: Jinfeng surname: Wang fullname: Wang, Jinfeng organization: Sleep Medicine Center of Gansu Province, Gansu Provincial People's Hospital, Lanzhou, China – sequence: 6 givenname: Tao orcidid: 0000-0002-2104-9298 surname: Lei fullname: Lei, Tao organization: School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/33296307$$D View this record in MEDLINE/PubMed
BookMark	eNp9kUtvEzEUhS3Uij7gD4CELLFhM8HPGXvZJmmp1BapLeuR47mDHCZ2sD2g_ns8Teiii25s6_o79-qec4IOfPCA0AdKZpQS_fXh9v5uOWOEkRknggtWv0HHVEpVEUbJwfTmohKckSN0ktKaENrUsnmLjjhnuuakOUbpPBrn8cXobXbBmwHfQv4b4q-Ez02CDgeP7yBl539W99lkwMvlJV6YbHAfIr4x63IuYBshJfcH8MKlEDuI-Kz0ekwuYeM7PB9M-e6dNdOQd-iwN0OC9_v7FP24WD7Mv1XX3y-v5mfXleVa5qqRCuoVEwCGAZVKWAZCQyky3oAiVjdESNA9B6mZZqLnyojadBY6u-KMn6Ivu77bGH6PZYl245KFYTAewphaJmpNiiN1U9DPL9B1GGNZYaK0qhWhkhbq054aVxvo2m10GxMf2_92FoDtABtDShH6Z4SSdsqsfcqsnTJr95kVkXohsi4_GZVLNsPr0o87qQOA51maKaWE5P8A74CjaA
CODEN	ITNSB3
CitedBy_id	crossref_primary_10_1016_j_neucom_2025_131074 crossref_primary_10_3389_fnhum_2022_799881 crossref_primary_10_1109_ACCESS_2023_3270426 crossref_primary_10_1109_TCE_2024_3370310 crossref_primary_10_3389_fnhum_2021_799288 crossref_primary_10_3389_fpsyt_2022_864393 crossref_primary_10_1038_s41380_023_02395_3 crossref_primary_10_1109_TNSRE_2022_3154772 crossref_primary_10_2147_NDT_S456486 crossref_primary_10_1016_j_bspc_2024_107226 crossref_primary_10_1016_j_cjph_2024_09_011 crossref_primary_10_1109_TAFFC_2025_3527459 crossref_primary_10_1109_TNSRE_2022_3192533 crossref_primary_10_1007_s11517_024_03279_6 crossref_primary_10_1080_21642583_2024_2427028 crossref_primary_10_1007_s12539_023_00567_x crossref_primary_10_1016_j_jad_2024_09_040 crossref_primary_10_1016_j_heliyon_2024_e36991 crossref_primary_10_1109_TNSRE_2023_3341923 crossref_primary_10_1016_j_bspc_2024_107271 crossref_primary_10_1109_TNSRE_2024_3465269 crossref_primary_10_1016_j_compbiomed_2024_108951 crossref_primary_10_1109_TBME_2022_3204861 crossref_primary_10_7717_peerj_17721 crossref_primary_10_1109_ACCESS_2025_3554758 crossref_primary_10_1088_1741_2552_acb088 crossref_primary_10_1109_ACCESS_2021_3103047 crossref_primary_10_1007_s10489_023_04702_5 crossref_primary_10_1016_j_brainres_2024_148816 crossref_primary_10_1109_TAFFC_2022_3210958 crossref_primary_10_1016_j_clinph_2022_11_013 crossref_primary_10_1016_j_bspc_2024_106030 crossref_primary_10_1109_ACCESS_2024_3349552 crossref_primary_10_3389_fnhum_2023_1197613 crossref_primary_10_1109_TNSRE_2023_3320693 crossref_primary_10_1016_j_bja_2023_11_033 crossref_primary_10_1088_1741_2552_ad5048 crossref_primary_10_3389_fncom_2022_1046310 crossref_primary_10_1016_j_bspc_2023_104923 crossref_primary_10_1109_TCDS_2024_3523020 crossref_primary_10_1109_TNSRE_2024_3374651 crossref_primary_10_1016_j_bbii_2024_100064 crossref_primary_10_3389_fnins_2021_778488 crossref_primary_10_1109_TNSRE_2022_3223056 crossref_primary_10_1109_TCSS_2023_3291950 crossref_primary_10_1109_TNSRE_2023_3311458 crossref_primary_10_1186_s12916_023_02941_4 crossref_primary_10_3389_fnins_2022_976229 crossref_primary_10_1016_j_inffus_2024_102723 crossref_primary_10_3390_s25164989 crossref_primary_10_1016_j_neuroscience_2022_10_010 crossref_primary_10_3389_fphy_2021_822915 crossref_primary_10_1093_cercor_bhaf034 crossref_primary_10_1088_1741_2552_acbe20 crossref_primary_10_3390_s22072553 crossref_primary_10_1016_j_bspc_2024_106402 crossref_primary_10_1016_j_bspc_2025_107879 crossref_primary_10_1016_j_chaos_2025_116163 crossref_primary_10_1016_j_cmpb_2022_107113 crossref_primary_10_1016_j_jad_2023_01_126 crossref_primary_10_1007_s11571_025_10305_2 crossref_primary_10_1109_JSEN_2021_3108471 crossref_primary_10_1016_j_pscychresns_2023_111687 crossref_primary_10_1109_JBHI_2022_3165640 crossref_primary_10_1016_j_eswa_2023_122356 crossref_primary_10_1109_TNSRE_2024_3509776 crossref_primary_10_3390_brainsci13030384
Cites_doi	10.1007/s00422-004-0471-9 10.1016/j.clinph.2014.12.026 10.3389/fnhum.2017.00549 10.1016/j.biopsych.2011.05.018 10.1016/j.jad.2014.10.001 10.1109/TNSRE.2018.2828143 10.1016/j.neubiorev.2004.10.009 10.1016/S0924-9338(10)70289-3 10.1016/j.biopsych.2013.05.036 10.1016/j.clinph.2020.06.031 10.1109/TBME.2006.873748 10.1088/978-0-7503-3279-8ch1 10.1016/j.inffus.2020.01.008 10.1002/hbm.20662 10.1046/j.1525-1497.2001.016009606.x 10.1109/TNSRE.2019.2939655 10.1016/j.clinph.2018.01.017 10.1155/2018/5238028 10.1016/j.physa.2016.10.068 10.1371/journal.pone.0214406 10.3389/fnhum.2018.00341 10.1109/TFUZZ.2019.2903753 10.1038/s41593-020-0630-x 10.1007/978-3-319-47103-7_10 10.3389/fnins.2018.01037 10.1371/journal.pone.0032508 10.1001/archinte.166.10.1092 10.3389/fnins.2020.00192 10.1002/hbm.20517 10.1016/0021-9991(73)90003-X 10.1016/j.jad.2009.05.029 10.1093/brain/awn262 10.1360/972009-2150 10.1016/S1053-8119(09)71653-2 10.1109/TAFFC.2018.2801289 10.1002/hbm.20346 10.1109/JBHI.2013.2253614 10.1016/j.clinph.2016.06.023 10.1016/j.cmpb.2015.04.009 10.1016/j.biopsych.2018.11.011 10.1088/1741-2560/13/3/036015 10.1007/s11682-018-0003-1 10.1016/j.biopsych.2006.09.020 10.1002/hbm.20273 10.1002/hbm.20275 10.1109/ACCESS.2020.2994985 10.1155/2017/9514369 10.1007/978-3-319-47653-7_1 10.1109/TNSRE.2020.3013429 10.1097/WCO.0b013e32833aa567 10.1007/s11633-019-1171-1 10.3389/fnagi.2014.00011 10.1109/TNSRE.2019.2894423 10.1155/2018/6534041 10.1109/TNNLS.2016.2515080 10.1371/journal.pone.0108648 10.1109/JBHI.2019.2938247 10.1016/0165-1781(89)90047-4 10.1080/02699930903407948 10.1109/ACCESS.2019.2927121
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
DBID	97E ESBDL RIA RIE AAYXX CITATION 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
DOI	10.1109/TNSRE.2020.3043426
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library CrossRef 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
DatabaseTitle	CrossRef PubMed 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	PubMed Materials Research Database 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/IET Electronic Library 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	Occupational Therapy & Rehabilitation
EISSN	1558-0210
EndPage	229
ExternalDocumentID	33296307 10_1109_TNSRE_2020_3043426 9288845
Genre	orig-research Journal Article
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 61962034; 61941109; 61862058 funderid: 10.13039/501100001809 – fundername: Tianyou Youth Talent Lift Program of Lanzhou Jiaotong Univesity funderid: 10.13039/501100009014 – fundername: Scientific Research Innovation Project of School of Electronics and Information Engineering, Lanzhou Jiaotong University funderid: 10.13039/501100009014 – fundername: Opening Foundation of the Key Laboratory of Opto-Technology and Intelligent Control, Lanzhou Jiaotong University, Ministry of Education grantid: KFKT2020-13 funderid: 10.13039/501100009014
GroupedDBID	--- -~X 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAFWJ AAJGR AASAJ AAWTH ABAZT ABVLG ACGFO ACGFS ACIWK ACPRK AENEX AETIX AFPKN AFRAH AGSQL AIBXA ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD ESBDL F5P GROUPED_DOAJ HZ~ H~9 IFIPE IPLJI JAVBF LAI M43 O9- OCL OK1 P2P RIA RIE RNS AAYXX CITATION 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-c395t-758e6b24eea2e1584c2e49e8e6237e80c97045e9f3e592924f38a46adcedcb323
IEDL.DBID	RIE
ISICitedReferencesCount	82
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000626331500002&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 Jul 11 15:11:12 EDT 2025 Fri Jul 25 05:35:11 EDT 2025 Thu Apr 03 07:08:58 EDT 2025 Tue Nov 18 22:31:03 EST 2025 Sat Nov 29 01:47:11 EST 2025 Wed Aug 27 02:51:13 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-c395t-758e6b24eea2e1584c2e49e8e6237e80c97045e9f3e592924f38a46adcedcb323
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0003-3643-3580 0000-0002-2104-9298
OpenAccessLink	https://ieeexplore.ieee.org/document/9288845
PMID	33296307
PQID	2498680151
PQPubID	85423
PageCount	15
ParticipantIDs	pubmed_primary_33296307 proquest_journals_2498680151 crossref_primary_10_1109_TNSRE_2020_3043426 proquest_miscellaneous_2469076567 crossref_citationtrail_10_1109_TNSRE_2020_3043426 ieee_primary_9288845
PublicationCentury	2000
PublicationDate	20210000 2021-00-00 20210101
PublicationDateYYYYMMDD	2021-01-01
PublicationDate_xml	– year: 2021 text: 20210000
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	2021
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	ref57 ref13 ref56 ref12 ref59 ref15 ref58 ref14 ref52 ref55 ref11 ref54 ref10 cai (ref39) 0 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 si (ref45) 2018; 46 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref38 ref24 ref23 ref26 ref25 ref64 ref20 ref63 ref22 ref65 sayers (ref1) 2001; 79 ref21 ref28 ref27 ref29 dharmadhikari (ref53) 2018; 28 ref60 ref62 ref61
References_xml	– ident: ref61 doi: 10.1007/s00422-004-0471-9 – ident: ref63 doi: 10.1016/j.clinph.2014.12.026 – ident: ref30 doi: 10.3389/fnhum.2017.00549 – ident: ref65 doi: 10.1016/j.biopsych.2011.05.018 – ident: ref52 doi: 10.1016/j.jad.2014.10.001 – ident: ref26 doi: 10.1109/TNSRE.2018.2828143 – ident: ref13 doi: 10.1016/j.neubiorev.2004.10.009 – ident: ref7 doi: 10.1016/S0924-9338(10)70289-3 – ident: ref57 doi: 10.1016/j.biopsych.2013.05.036 – ident: ref29 doi: 10.1016/j.clinph.2020.06.031 – ident: ref12 doi: 10.1109/TBME.2006.873748 – ident: ref34 doi: 10.1088/978-0-7503-3279-8ch1 – ident: ref2 doi: 10.1016/j.inffus.2020.01.008 – ident: ref59 doi: 10.1002/hbm.20662 – ident: ref35 doi: 10.1046/j.1525-1497.2001.016009606.x – ident: ref18 doi: 10.1109/TNSRE.2019.2939655 – ident: ref64 doi: 10.1016/j.clinph.2018.01.017 – ident: ref37 doi: 10.1155/2018/5238028 – ident: ref32 doi: 10.1016/j.physa.2016.10.068 – ident: ref49 doi: 10.1371/journal.pone.0214406 – ident: ref24 doi: 10.3389/fnhum.2018.00341 – ident: ref11 doi: 10.1109/TFUZZ.2019.2903753 – ident: ref33 doi: 10.1038/s41593-020-0630-x – ident: ref54 doi: 10.1007/978-3-319-47103-7_10 – ident: ref27 doi: 10.3389/fnins.2018.01037 – ident: ref20 doi: 10.1371/journal.pone.0032508 – ident: ref36 doi: 10.1001/archinte.166.10.1092 – ident: ref28 doi: 10.3389/fnins.2020.00192 – ident: ref51 doi: 10.1371/journal.pone.0032508 – ident: ref17 doi: 10.1002/hbm.20517 – ident: ref44 doi: 10.1016/0021-9991(73)90003-X – volume: 28 start-page: 17 year: 2018 ident: ref53 article-title: Frontal theta asymmetry as a biomarker of depression publication-title: East Asian Arch Psychiatry – ident: ref56 doi: 10.1016/j.jad.2009.05.029 – ident: ref23 doi: 10.1093/brain/awn262 – ident: ref47 doi: 10.1360/972009-2150 – ident: ref62 doi: 10.1016/S1053-8119(09)71653-2 – ident: ref15 doi: 10.1109/TAFFC.2018.2801289 – ident: ref21 doi: 10.1002/hbm.20346 – year: 0 ident: ref39 publication-title: MODMA Dataset A Multi-Modal Open Dataset for Mental-Disorder Analysis – ident: ref42 doi: 10.1109/JBHI.2013.2253614 – ident: ref16 doi: 10.1016/j.clinph.2016.06.023 – ident: ref40 doi: 10.1016/j.cmpb.2015.04.009 – ident: ref10 doi: 10.1016/j.biopsych.2018.11.011 – ident: ref22 doi: 10.1088/1741-2560/13/3/036015 – ident: ref6 doi: 10.1007/s11682-018-0003-1 – ident: ref55 doi: 10.1016/j.biopsych.2006.09.020 – ident: ref48 doi: 10.1002/hbm.20273 – ident: ref50 doi: 10.1002/hbm.20275 – ident: ref5 doi: 10.1109/ACCESS.2020.2994985 – ident: ref46 doi: 10.1155/2017/9514369 – ident: ref14 doi: 10.1007/978-3-319-47653-7_1 – ident: ref19 doi: 10.1109/TNSRE.2020.3013429 – ident: ref9 doi: 10.1097/WCO.0b013e32833aa567 – ident: ref43 doi: 10.1007/s11633-019-1171-1 – ident: ref31 doi: 10.3389/fnagi.2014.00011 – ident: ref60 doi: 10.1109/TNSRE.2019.2894423 – ident: ref41 doi: 10.1155/2018/6534041 – ident: ref8 doi: 10.1109/TNNLS.2016.2515080 – ident: ref25 doi: 10.1371/journal.pone.0108648 – ident: ref4 doi: 10.1109/JBHI.2019.2938247 – volume: 46 start-page: 1742 year: 2018 ident: ref45 article-title: Research on brain functional network and lie detection based on phase lag index publication-title: Acta Electronica Sinica – volume: 79 start-page: 1085 year: 2001 ident: ref1 article-title: The world health report 2001-Mental health: New understanding, new hope publication-title: Bull World Health Org – ident: ref38 doi: 10.1016/0165-1781(89)90047-4 – ident: ref58 doi: 10.1080/02699930903407948 – ident: ref3 doi: 10.1109/ACCESS.2019.2927121
SSID	ssj0017657
Score	2.596566
Snippet	If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	215
SubjectTerms	abnormal topological architecture Biomarkers Brain Brain architecture brain functional networks Classification Clustering Complex systems Conductors Correlation analysis Depression EEG Electrodes Electroencephalography Feature extraction Frequencies Functional magnetic resonance imaging Major depressive disorder Measurement Mental depression Phase lag phase lag index Statistical analysis Synchronism Synchronization
Title	Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification
URI	https://ieeexplore.ieee.org/document/9288845 https://www.ncbi.nlm.nih.gov/pubmed/33296307 https://www.proquest.com/docview/2498680151 https://www.proquest.com/docview/2469076567
Volume	29
WOSCitedRecordID	wos000626331500002&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/IET Electronic Library 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/eLvHCXMwlV1Lb9QwEB61FUK98CrQQFkZCbiAWa_tPHykdBcOsEJlK-0tcuyJBKqyqNnt72fsPFQkisQlipJxbGXGmm_G8wB4RSCi8qI23DkluU6F5dZXhmdO1ugVgWAfE4W_5MtlsV6bb3vwbsyFQcQYfIbvw208y_cbtwuusqmRZK_pdB_28zzvcrXGE4M8i1U9aQNrrpUUQ4KMMNPV8vv5nExBSRaq0Ip00iHcVUqS7IUusjf0UWywcjvWjDpncf__VvsA7vXYkn3ohOEh7GHzCF7frCPMVl0RAfaGnf9RovsI2tPQLIItSM_1tMsuQrxlp6TpPNs0NKYNUdI8IlQ2n39iZ3ZrGeFe9tX-pOtZH1d7jWwo68mGsifMNp7FHpwhOinO-hguFvPVx8-878jAnTLplpNxgVklNaKVOCPs4iRqg_RQqhwL4UxOEBFNrTAl3CV1rQqrM-sdelcpqZ7AQbNp8BiYVV5Yb1yVKqPryhWiMJlLQzyhxGKmE5gNfCld_y9C14zLMpotwpSRrWVga9mzNYG345hfXbGOf1IfBaaNlD2_EjgZ2F_2-7ktyUgtMlLm6SyBl-Nr2onheMU2uNkFmuBpIHycJ_C0E5vx24O0Pfv7nM_hUIZYmejaOYGD7dUOX8Add7390V5NSNzXxSR6CyZR6H8Dzez7SQ
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VgqAXXqUQKGAk4AJpHdt5-EjpLkVsI1QWqbfIsScSCGVRs9vfz9h5qEiAxCWKkkkc5RtrvrHnAfCSSETteKNja6WIVcpNbFyt48yKBp0kEuxCovAiL8vi_Fx_3oK3Uy4MIobgMzzwp2Ev363sxi-VHWpB_ppKr8H1VCmR9Nla055BnoW6njSFVayk4GOKDNeHy_LL2YycQUE-KleSrNIO3JRSkPb5PrJXLFJosfJ3thmszvzO_33vXbg9sEv2rleHe7CF7X14dbWSMFv2ZQTYa3b2W5HuXeiOfLsINidLN8iWfYx4x47I1jm2aumZzsdJx4GjstnsAzs2a8OI-bJT852Ox0Nk7SWysbAnGwufMNM6Frpw-vikMOoD-DqfLd-fxENPhthKna5jci8wq4VCNAITYi9WoNJIF4XMseBW50QSUTcSU2JeQjWyMCozzqKztRRyD7bbVYuPgBnpuHHa1qnUqqltwQud2dRHFAosEhVBMuJS2eFf-L4ZP6rguHBdBVgrD2s1wBrBm-mZn325jn9K73rQJskBrwj2R_irYUZ3FbmpRUbmPE0ieDHdprnoN1hMi6uNl_FrDcSQ8wge9mozvXvUtsd_HvM53DpZni6qxcfy0xPYET5yJiz07MP2-mKDT-GGvVx_6y6eBaX_BS8H_LM
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=Brain+Functional+Networks+Based+on+Resting-State+EEG+Data+for+Major+Depressive+Disorder+Analysis+and+Classification&rft.jtitle=IEEE+transactions+on+neural+systems+and+rehabilitation+engineering&rft.au=Zhang%2C+Bingtao&rft.au=Yan%2C+Guanghui&rft.au=Yang%2C+Zhifei&rft.au=Su%2C+Yun&rft.date=2021&rft.eissn=1558-0210&rft.volume=29&rft.spage=215&rft_id=info:doi/10.1109%2FTNSRE.2020.3043426&rft_id=info%3Apmid%2F33296307&rft.externalDocID=33296307
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