Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability
•High inter-subject variability for brain fingerprinting and cognitive behavior predicting.•Conditional deep generative network for extracting shared information of inter-subject.•Embed the state information into the conditional deep generative network.•High accuracy based on a large number of subje...
Uloženo v:
| Vydáno v: | NeuroImage (Orlando, Fla.) Ročník 295; s. 120651 |
|---|---|
| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Elsevier Inc
15.07.2024
Elsevier Limited Elsevier |
| Témata: | |
| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •High inter-subject variability for brain fingerprinting and cognitive behavior predicting.•Conditional deep generative network for extracting shared information of inter-subject.•Embed the state information into the conditional deep generative network.•High accuracy based on a large number of subjects and numerous states.•Higher fingerprinting is useful for resulting in higher behavioral associations.
The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions. |
|---|---|
| AbstractList | The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions. The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions. •High inter-subject variability for brain fingerprinting and cognitive behavior predicting.•Conditional deep generative network for extracting shared information of inter-subject.•Embed the state information into the conditional deep generative network.•High accuracy based on a large number of subjects and numerous states.•Higher fingerprinting is useful for resulting in higher behavioral associations. The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions. |
| ArticleNumber | 120651 |
| Author | Xiang, Jie Yan, Tianyi Lu, Jiayu Li, Jiaxin Zhang, Xi Yang, Lan Li, Dandan Wang, Bin |
| Author_xml | – sequence: 1 givenname: Jiayu surname: Lu fullname: Lu, Jiayu organization: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China – sequence: 2 givenname: Tianyi surname: Yan fullname: Yan, Tianyi organization: School of Life Science, Beijing Institute of Technology, 100081, China – sequence: 3 givenname: Lan surname: Yang fullname: Yang, Lan organization: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China – sequence: 4 givenname: Xi surname: Zhang fullname: Zhang, Xi organization: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China – sequence: 5 givenname: Jiaxin surname: Li fullname: Li, Jiaxin organization: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China – sequence: 6 givenname: Dandan surname: Li fullname: Li, Dandan organization: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China – sequence: 7 givenname: Jie surname: Xiang fullname: Xiang, Jie organization: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China – sequence: 8 givenname: Bin orcidid: 0000-0001-7771-5360 surname: Wang fullname: Wang, Bin email: wangbin01@tyut.edu.cn organization: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38788914$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkkuP0zAURiM0iHnAX0CR2LBpsetnNghmxGOkkdjA2rpxrluH1C52Uqn_HqcdBqmr2TjXyblHjr97XV2EGLCqakqWlFD5oV8GnFL0W1jjckVWfElXRAr6orqipBGLRqjVxVwLttCUNpfVdc49IaShXL-qLplWWpf6qhpvE_hQOx_WmHbJh7FUNYSutnEd_Oj3WLe4gb2Pqd4l7Lw9ElOeVzeFso0BhoKHgHaMW6yjqzd-vamLDNMiT21fPtR7SB5aP_jx8Lp66WDI-ObxeVP9-vrl5933xcOPb_d3nx8WVigxzquS4LiDDi0XrgVoaIey1byVzkkBRGmiGyWUU0Iw5AyEkrYBKRuuFLup7k_eLkJvyt9tIR1MBG-OL2JaG0ijtwOaVjjgWnOt0HFA1jBQpGOSCcEd6XRxvT-5din-mTCPZuuzxWGAgHHKhhFJmGJEyIK-O0P7OKVySUdKMsq5ooV6-0hN7Ra7p-P9y6YA-gTYFHNO6J4QSsw8BqY3_8fAzGNgTmNQWj-etVo_wpzUWOIeniO4PQmwxLP3mEy2HoMt-acSZrk__xzJpzOJHXzwFobfeHie4i92Zew1 |
| CitedBy_id | crossref_primary_10_1007_s13534_025_00487_3 crossref_primary_10_1016_j_media_2025_103761 crossref_primary_10_3758_s13428_025_02635_0 crossref_primary_10_1038_s41598_024_71295_5 |
| Cites_doi | 10.1016/j.neuroimage.2021.118423 10.1126/sciadv.abq8566 10.1007/s10334-013-0420-5 10.1093/cercor/bhac396 10.1089/brain.2011.0007 10.1016/j.neuroimage.2017.03.064 10.1101/2022.02.04.479112 10.1016/j.neuroimage.2017.07.016 10.1073/pnas.0601417103 10.1016/j.tics.2018.08.009 10.1002/mrm.1910340409 10.1126/scitranslmed.3008601 10.1523/JNEUROSCI.4638-14.2015 10.1038/nrn.2016.167 10.1126/science.1127647 10.1038/nn.3470 10.1016/j.neuron.2011.09.006 10.1016/j.engappai.2023.105859 10.1109/LRA.2020.3043163 10.1109/TSP.2006.881199 10.1016/j.neuron.2016.10.046 10.1109/ACCESS.2022.3233110 10.1016/j.neuron.2016.10.050 10.1016/j.neuroimage.2013.04.127 10.1016/j.cam.2023.115532 10.3390/s19112528 10.1016/j.neuroimage.2013.05.039 10.1038/nn.4393 10.1016/j.neuroimage.2022.118970 10.1002/hbm.25379 10.1038/s41598-018-25089-1 10.1016/j.neuron.2018.03.035 10.1002/hbm.25394 10.1093/cercor/bhx170 10.1093/cercor/bhx230 10.1101/2023.02.03.23285441 10.1089/brain.2017.0561 10.1016/j.neuroimage.2013.05.041 10.1002/hbm.26423 10.1016/j.neuroimage.2018.10.006 10.3389/fnins.2022.813293 10.1002/hbm.24741 10.1002/hbm.460020107 10.1073/pnas.1902932116 10.1038/nn.4135 10.1016/j.neuroimage.2011.09.015 10.1007/s11548-018-1898-0 10.1073/pnas.0135058100 10.1038/s42003-022-03185-3 10.1002/hbm.21265 10.1098/rstb.2014.0310 10.1016/j.isci.2019.100801 10.1016/j.procs.2018.10.129 10.1016/j.neuroimage.2012.02.018 10.1016/j.neuroimage.2013.11.046 10.1016/j.neuroimage.2020.117181 10.1038/nature18933 10.1002/hbm.26561 |
| ContentType | Journal Article |
| Copyright | 2024 The Authors Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved. 2024. The Authors |
| Copyright_xml | – notice: 2024 The Authors – notice: Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved. – notice: 2024. The Authors |
| DBID | 6I. AAFTH AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7TK 7X7 7XB 88E 88G 8AO 8FD 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2M M7P P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ Q9U RC3 7X8 DOA |
| DOI | 10.1016/j.neuroimage.2024.120651 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Neurosciences Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Psychology Database (Alumni) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni Edition) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) Medical Database Psychology Database Biological Science Database Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology ProQuest Central Basic Genetics Abstracts MEDLINE - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest One Psychology ProQuest Central Student Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Genetics Abstracts Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Biological Science Collection ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Psychology Journals (Alumni) Biological Science Database ProQuest SciTech Collection Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest Psychology Journals ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | ProQuest One Psychology MEDLINE - Academic MEDLINE |
| 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: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1095-9572 |
| ExternalDocumentID | oai_doaj_org_article_b5fa488487ef4ae393a70d363554f0d8 38788914 10_1016_j_neuroimage_2024_120651 S1053811924001460 |
| Genre | Journal Article |
| GroupedDBID | --- --K --M .1- .FO .~1 0R~ 123 1B1 1RT 1~. 1~5 4.4 457 4G. 5RE 5VS 7-5 71M 7X7 88E 8AO 8FE 8FH 8FI 8FJ 8P~ 9JM AABNK AAEDT AAEDW AAFWJ AAIKJ AAKOC AALRI AAOAW AATTM AAXKI AAXLA AAXUO AAYWO ABBQC ABCQJ ABFNM ABFRF ABIVO ABJNI ABMAC ABMZM ABUWG ACDAQ ACGFO ACGFS ACIEU ACLOT ACPRK ACRLP ACVFH ADBBV ADCNI ADEZE ADFRT ADVLN AEBSH AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFKRA AFPKN AFPUW AFRHN AFTJW AFXIZ AGUBO AGWIK AGYEJ AHHHB AHMBA AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX APXCP AXJTR AZQEC BBNVY BENPR BHPHI BKOJK BLXMC BNPGV BPHCQ BVXVI CCPQU CS3 DM4 DU5 DWQXO EBS EFBJH EFKBS EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN FYUFA G-Q GBLVA GNUQQ GROUPED_DOAJ HCIFZ HMCUK IHE J1W KOM LG5 LK8 LX8 M1P M29 M2M M2V M41 M7P MO0 MOBAO N9A O-L O9- OAUVE OK1 OVD OZT P-8 P-9 P2P PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PSYQQ Q38 ROL RPZ SAE SCC SDF SDG SDP SES SSH SSN SSZ T5K TEORI UKHRP UV1 YK3 Z5R ZU3 ~G- ~HD 0SF 6I. AACTN AAFTH AAIAV AFKWA AJOXV ALIPV AMFUW C45 HMQ NCXOZ RIG SEW SNS ZA5 29N 53G 9DU AAQFI AAQXK AAYXX ABXDB ACRPL ADFGL ADMUD ADNMO ADXHL AFFHD AGHFR AGQPQ AKRLJ ASPBG AVWKF AZFZN CAG CITATION COF EFLBG EJD FEDTE FGOYB G-2 HDW HEI HMK HMO HVGLF HZ~ R2- WUQ XPP ZMT AGCQF AGRNS CGR CUY CVF ECM EIF NPM 3V. 7TK 7XB 8FD 8FK FR3 K9. P64 PKEHL PQEST PQUKI PRINS Q9U RC3 7X8 PUEGO |
| ID | FETCH-LOGICAL-c575t-c5776af4fadec45fbaa91de6b84b6ff65a078089757f7553e43a576c9a6694773 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001247179900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1053-8119 1095-9572 |
| IngestDate | Fri Oct 03 12:53:33 EDT 2025 Thu Oct 02 09:14:31 EDT 2025 Tue Oct 07 07:00:27 EDT 2025 Mon Jul 21 06:02:08 EDT 2025 Sat Nov 29 04:32:17 EST 2025 Tue Nov 18 21:55:30 EST 2025 Thu Jul 04 08:40:17 EDT 2024 Tue Oct 14 19:35:52 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Functional connectivity Cognitive behavior predicting Fingerprint Individual identification Conditional variational autoencoder network |
| Language | English |
| License | This is an open access article under the CC BY-NC license. Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c575t-c5776af4fadec45fbaa91de6b84b6ff65a078089757f7553e43a576c9a6694773 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-7771-5360 |
| OpenAccessLink | https://doaj.org/article/b5fa488487ef4ae393a70d363554f0d8 |
| PMID | 38788914 |
| PQID | 3066314471 |
| PQPubID | 2031077 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_b5fa488487ef4ae393a70d363554f0d8 proquest_miscellaneous_3060373056 proquest_journals_3066314471 pubmed_primary_38788914 crossref_primary_10_1016_j_neuroimage_2024_120651 crossref_citationtrail_10_1016_j_neuroimage_2024_120651 elsevier_sciencedirect_doi_10_1016_j_neuroimage_2024_120651 elsevier_clinicalkey_doi_10_1016_j_neuroimage_2024_120651 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-07-15 |
| PublicationDateYYYYMMDD | 2024-07-15 |
| PublicationDate_xml | – month: 07 year: 2024 text: 2024-07-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Amsterdam |
| PublicationTitle | NeuroImage (Orlando, Fla.) |
| PublicationTitleAlternate | Neuroimage |
| PublicationYear | 2024 |
| Publisher | Elsevier Inc Elsevier Limited Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier Limited – name: Elsevier |
| References | Da K. A method for stochastic optimization. arXiv preprint Yang, Zheng, Wu (bib0070) 2019; 19 Gratton, Laumann, Nielsen (bib0025) 2018; 98 Zhang, Yang, Mou (bib0072) 2023 Ren, Zhou, Zhang (bib0055) 2022; 16 Finn, Shen, Scheinost (bib0020) 2015; 18 Li, Wisner, Atluri (bib0042) 2021; 42 Poldrack, Baker, Durnez (bib0052) 2017; 18 2017, 565: 2. Sohn, Lee, Yan (bib0060) 2015; 28 Kingma, Welling (bib0036) 2014; 1050 Van Essen, Ugurbil, Auerbach (bib0065) 2012; 62 Cole, Reynolds, Power (bib0013) 2013; 16 Krizhevsky A., Hinton G. Convolutional deep belief networks on cifar-10. Unpublished manuscript, 2010, 40(7): 1–9. Mantwill, Gell, Krohn (bib0045) 2022; 5 Khalili-Mahani, Zoethout, Beckmann (bib0034) 2012; 33 Anderson, Ferguson, Lopez-Larson (bib0006) 2011; 1 Demeter, Engelhardt, Mallett (bib0018) 2020; 23 Stampacchia S., Asadi S., Tomczyk S., et al. Fingerprints of brain disease: connectome identifiability in cognitive decline and Alzheimer's disease. bioRxiv, 2022: 2022.02. 04.479112. Amunts, Ebell, Muller (bib0005) 2016; 92 Glasser, Coalson, Robinson (bib0024) 2016; 536 Ivanovic, Leung, Schmerling (bib0031) 2020; 6 Griffa, Amico, Liégeois (bib0027) 2022; 250 Cai, Zhang, Hu (bib0009) 2019; 40 Jenkinson, Beckmann, Behrens (bib0032) 2012; 62 Mars, Passingham, Jbabdi (bib0046) 2018; 22 Noble, Spann, Tokoglu, Shen, Constable, Scheinost (bib0048) 2017; 27 Wu-Minn H.C.P. 1200 subjects data release reference manual. URL Allen, Sudlow, Peakman (bib0003) 2014; 6 2014. Da Silva Castanheira J., Wiesman A.I., Hansen J.Y., et al. The neurophysiological brain-fingerprint of Parkinson's disease. medRxiv, 2023. Seitzman, Gratton, Laumann (bib0057) 2019; 116 Salimi-Khorshidi, Douaud, Beckmann (bib0056) 2014; 90 Amico, Goñi (bib0004) 2018; 8 Ji, Spronk, Kulkarni (bib0033) 2019; 185 Kim, Zhang, Han (bib0035) 2021; 241 Kingma, Mohamed, Rezende, Welling (bib0037) 2014 Waller, Walter, Kruschwitz (bib0067) 2017; 158 Friston (bib0022) 1994; 2 Uzunova, Schultz, Handels (bib0064) 2019; 14 Greicius, Krasnow, Reiss (bib0026) 2003; 100 Hutchison, Morton (bib0030) 2015; 35 Shojaee, Li, Atluri (bib0058) 2019 Okano, Miyawaki, Kasai (bib0049) 2015; 370 Ansari, Chandrasekar, Singh (bib0007) 2022; 11 Power, Cohen, Nelson (bib0054) 2011; 72 Hinton, Salakhutdinov (bib0029) 2006; 313 Miller, Alfaro-Almagro, Bangerter (bib0047) 2016; 19 Peng, Liu, Hubbard (bib0051) 2023; 9 Weber, Soreni, Noseworthy (bib0068) 2014; 27 Van Essen, Smith, Barch (bib0066) 2013; 80 Corriveau, Yoo, Kwon (bib0014) 2023; 33 Glasser, Sotiropoulos, Wilson (bib0023) 2013; 80 Chen, Hu (bib0012) 2018; 8 Smith, Beckmann, Andersson (bib0059) 2013; 80 Zemouri, Ibrahim, Tahan (bib0071) 2023; 120 Zhang, Jiang (bib0073) 2024; 438 Lori, Ramalhosa, Marques (bib0044) 2018; 141 Abbas, Amico, Svaldi (bib0001) 2020; 221 Biswal, Zerrin Yetkin, Haughton (bib0008) 1995; 34 Lee, Lee (bib0041) 2024; 45 Aharon, Elad, Bruckstein (bib0002) 2006; 54 Cai, Zhang, Zhang (bib0010) 2021; 42 Poo, Du, Ip (bib0053) 2016; 92 Tian, Yeo, Cropley (bib0063) 2021; 229 Damoiseaux, Rombouts, Barkhof (bib0017) 2006; 103 Finn, Scheinost, Finn (bib0021) 2017; 160 Hannum, Lopez, Blanco (bib0028) 2023; 44 Peña-Gómez, Avena-Koenigsberger, Sepulcre (bib0050) 2018; 28 Peña-Gómez (10.1016/j.neuroimage.2024.120651_bib0050) 2018; 28 Khalili-Mahani (10.1016/j.neuroimage.2024.120651_bib0034) 2012; 33 10.1016/j.neuroimage.2024.120651_bib0069 Abbas (10.1016/j.neuroimage.2024.120651_bib0001) 2020; 221 Shojaee (10.1016/j.neuroimage.2024.120651_bib0058) 2019 Corriveau (10.1016/j.neuroimage.2024.120651_bib0014) 2023; 33 Ansari (10.1016/j.neuroimage.2024.120651_bib0007) 2022; 11 Kingma (10.1016/j.neuroimage.2024.120651_bib0037) 2014 Glasser (10.1016/j.neuroimage.2024.120651_bib0024) 2016; 536 Cole (10.1016/j.neuroimage.2024.120651_bib0013) 2013; 16 Poo (10.1016/j.neuroimage.2024.120651_bib0053) 2016; 92 Mantwill (10.1016/j.neuroimage.2024.120651_bib0045) 2022; 5 Cai (10.1016/j.neuroimage.2024.120651_bib0009) 2019; 40 Griffa (10.1016/j.neuroimage.2024.120651_bib0027) 2022; 250 Gratton (10.1016/j.neuroimage.2024.120651_bib0025) 2018; 98 Demeter (10.1016/j.neuroimage.2024.120651_bib0018) 2020; 23 Hinton (10.1016/j.neuroimage.2024.120651_bib0029) 2006; 313 Ivanovic (10.1016/j.neuroimage.2024.120651_bib0031) 2020; 6 Finn (10.1016/j.neuroimage.2024.120651_bib0020) 2015; 18 Zemouri (10.1016/j.neuroimage.2024.120651_bib0071) 2023; 120 Lee (10.1016/j.neuroimage.2024.120651_bib0041) 2024; 45 Seitzman (10.1016/j.neuroimage.2024.120651_bib0057) 2019; 116 Poldrack (10.1016/j.neuroimage.2024.120651_bib0052) 2017; 18 Uzunova (10.1016/j.neuroimage.2024.120651_bib0064) 2019; 14 Friston (10.1016/j.neuroimage.2024.120651_bib0022) 1994; 2 Tian (10.1016/j.neuroimage.2024.120651_bib0063) 2021; 229 Amunts (10.1016/j.neuroimage.2024.120651_bib0005) 2016; 92 Peng (10.1016/j.neuroimage.2024.120651_bib0051) 2023; 9 10.1016/j.neuroimage.2024.120651_bib0040 Weber (10.1016/j.neuroimage.2024.120651_bib0068) 2014; 27 Damoiseaux (10.1016/j.neuroimage.2024.120651_bib0017) 2006; 103 Hannum (10.1016/j.neuroimage.2024.120651_bib0028) 2023; 44 Power (10.1016/j.neuroimage.2024.120651_bib0054) 2011; 72 Van Essen (10.1016/j.neuroimage.2024.120651_bib0066) 2013; 80 Zhang (10.1016/j.neuroimage.2024.120651_bib0073) 2024; 438 Finn (10.1016/j.neuroimage.2024.120651_bib0021) 2017; 160 Yang (10.1016/j.neuroimage.2024.120651_bib0070) 2019; 19 Sohn (10.1016/j.neuroimage.2024.120651_bib0060) 2015; 28 Aharon (10.1016/j.neuroimage.2024.120651_bib0002) 2006; 54 Glasser (10.1016/j.neuroimage.2024.120651_bib0023) 2013; 80 Van Essen (10.1016/j.neuroimage.2024.120651_bib0065) 2012; 62 Waller (10.1016/j.neuroimage.2024.120651_bib0067) 2017; 158 Okano (10.1016/j.neuroimage.2024.120651_bib0049) 2015; 370 Jenkinson (10.1016/j.neuroimage.2024.120651_bib0032) 2012; 62 Kim (10.1016/j.neuroimage.2024.120651_bib0035) 2021; 241 10.1016/j.neuroimage.2024.120651_bib0016 Noble (10.1016/j.neuroimage.2024.120651_bib0048) 2017; 27 Cai (10.1016/j.neuroimage.2024.120651_bib0010) 2021; 42 Chen (10.1016/j.neuroimage.2024.120651_bib0012) 2018; 8 10.1016/j.neuroimage.2024.120651_bib0015 Salimi-Khorshidi (10.1016/j.neuroimage.2024.120651_bib0056) 2014; 90 Allen (10.1016/j.neuroimage.2024.120651_bib0003) 2014; 6 Kingma (10.1016/j.neuroimage.2024.120651_bib0036) 2014; 1050 Ji (10.1016/j.neuroimage.2024.120651_bib0033) 2019; 185 Hutchison (10.1016/j.neuroimage.2024.120651_bib0030) 2015; 35 Li (10.1016/j.neuroimage.2024.120651_bib0042) 2021; 42 Amico (10.1016/j.neuroimage.2024.120651_bib0004) 2018; 8 Zhang (10.1016/j.neuroimage.2024.120651_bib0072) 2023 Lori (10.1016/j.neuroimage.2024.120651_bib0044) 2018; 141 Biswal (10.1016/j.neuroimage.2024.120651_bib0008) 1995; 34 Smith (10.1016/j.neuroimage.2024.120651_bib0059) 2013; 80 Miller (10.1016/j.neuroimage.2024.120651_bib0047) 2016; 19 Mars (10.1016/j.neuroimage.2024.120651_bib0046) 2018; 22 Ren (10.1016/j.neuroimage.2024.120651_bib0055) 2022; 16 10.1016/j.neuroimage.2024.120651_bib0062 Greicius (10.1016/j.neuroimage.2024.120651_bib0026) 2003; 100 Anderson (10.1016/j.neuroimage.2024.120651_bib0006) 2011; 1 |
| References_xml | – volume: 40 start-page: 4843 year: 2019 end-page: 4858 ident: bib0009 article-title: Refined measure of functional connectomes for improved identifiability and prediction publication-title: Hum. Brain Mapp. – volume: 100 start-page: 253 year: 2003 end-page: 258 ident: bib0026 article-title: Functional connectivity in the resting brain: a network analysis of the default mode hypothesis publication-title: Proc. Natl. Acad. Sci. – volume: 8 start-page: 8254 year: 2018 ident: bib0004 article-title: The quest for identifiability in human functional connectomes publication-title: Sci. Rep. – year: 2023 ident: bib0072 article-title: An overview of brain fingerprint identification based on various neuroimaging technologies publication-title: IEEE Transactions on Cognitive and Developmental Systems – volume: 35 start-page: 6849 year: 2015 end-page: 6859 ident: bib0030 article-title: Tracking the brain's functional coupling dynamics over development publication-title: J. Neurosci. – volume: 158 start-page: 371 year: 2017 end-page: 377 ident: bib0067 article-title: Evaluating the replicability, specificity, and generalizability of connectome fingerprints publication-title: Neuroimage – volume: 98 start-page: 439 year: 2018 end-page: 452 ident: bib0025 article-title: Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation publication-title: Neuron – volume: 8 start-page: 197 year: 2018 end-page: 204 ident: bib0012 article-title: Individual identification using the functional brain fingerprint detected by the recurrent neural network publication-title: Brain Connect – volume: 2 start-page: 56 year: 1994 end-page: 78 ident: bib0022 article-title: Functional and effective connectivity in neuroimaging: a synthesis publication-title: Hum. Brain Mapp. – volume: 27 start-page: 291 year: 2014 end-page: 301 ident: bib0068 article-title: A preliminary study on the effects of acute ethanol ingestion on default mode network and temporal fractal properties of the brain publication-title: Magn. Reson. Mater. Phys., Biol. Med. – volume: 120 year: 2023 ident: bib0071 article-title: Hydrogenerator early fault detection: sparse dictionary learning jointly with the variational autoencoder publication-title: Eng. Appl. Artif. Intell. – volume: 185 start-page: 35 year: 2019 end-page: 57 ident: bib0033 article-title: Mapping the human brain's cortical-subcortical functional network organization publication-title: Neuroimage – start-page: 3581 year: 2014 end-page: 3589 ident: bib0037 article-title: Semi-supervised learning with deep generative models publication-title: Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, QC, Canada, 8–13 December – volume: 42 start-page: 3717 year: 2021 end-page: 3732 ident: bib0042 article-title: Feature selection framework for functional connectome fingerprinting publication-title: Hum. Brain Mapp. – volume: 116 start-page: 22851 year: 2019 end-page: 22861 ident: bib0057 article-title: Trait-like variants in human functional brain networks publication-title: Proc. Natl. Acad. Sci. – volume: 92 start-page: 591 year: 2016 end-page: 596 ident: bib0053 article-title: China brain project: basic neuroscience, brain diseases, and brain-inspired computing publication-title: Neuron – volume: 28 start-page: 2922 year: 2018 end-page: 2934 ident: bib0050 article-title: Spatiotemporal network markers of individual variability in the human functional connectome publication-title: Cereb. Cortex – volume: 33 start-page: 1003 year: 2012 end-page: 1018 ident: bib0034 article-title: Effects of morphine and alcohol on functional brain connectivity during “resting state”: a placebo-controlled crossover study in healthy young men publication-title: Hum. Brain Mapp. – volume: 42 start-page: 2691 year: 2021 end-page: 2705 ident: bib0010 article-title: Functional connectome fingerprinting: identifying individuals and predicting cognitive functions via autoencoder publication-title: Hum. Brain Mapp. – volume: 9 start-page: eabq8566 year: 2023 ident: bib0051 article-title: Robust dynamic brain coactivation states estimated in individuals publication-title: Sci. Adv. – volume: 141 start-page: 539 year: 2018 end-page: 544 ident: bib0044 article-title: Deep learning based pipeline for fingerprinting using brain functional MRI connectivity data publication-title: Procedia Comput. Sci. – volume: 16 start-page: 1348 year: 2013 end-page: 1355 ident: bib0013 article-title: Multi-task connectivity reveals flexible hubs for adaptive task control publication-title: Nat. Neurosci. – volume: 92 start-page: 574 year: 2016 end-page: 581 ident: bib0005 article-title: The human brain project: creating a European research infrastructure to decode the human brain publication-title: Neuron – reference: , 2017, 565: 2. – volume: 241 year: 2021 ident: bib0035 article-title: Representation learning of resting state fMRI with variational autoencoder publication-title: Neuroimage – volume: 18 start-page: 1664 year: 2015 end-page: 1671 ident: bib0020 article-title: Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity publication-title: Nat. Neurosci. – volume: 45 start-page: e26561 year: 2024 ident: bib0041 article-title: Discovering individual fingerprints in resting-state functional connectivity using deep neural networks publication-title: Hum. Brain Mapp. – volume: 14 start-page: 451 year: 2019 end-page: 461 ident: bib0064 article-title: Unsupervised pathology detection in medical images using conditional variational autoencoders publication-title: Int. J. Comput. Assist. Radiol. Surg. – volume: 11 start-page: 9890 year: 2022 end-page: 9906 ident: bib0007 article-title: Re-routing drugs to blood brain barrier: a comprehensive analysis of machine learning approaches with fingerprint amalgamation and data balancing publication-title: IEEE Access – volume: 19 start-page: 1523 year: 2016 end-page: 1536 ident: bib0047 article-title: Multimodal population brain imaging in the UK Biobank prospective epidemiological study publication-title: Nat. Neurosci. – volume: 250 year: 2022 ident: bib0027 article-title: Brain structure-function coupling provides signatures for task decoding and individual fingerprinting publication-title: Neuroimage – volume: 80 start-page: 62 year: 2013 end-page: 79 ident: bib0066 article-title: The WU-Minn human connectome project: an overview publication-title: Neuroimage – volume: 438 year: 2024 ident: bib0073 article-title: Conditional variational autoencoder with Gaussian process regression recognition for parametric models publication-title: J. Comput. Appl. Math. – volume: 16 year: 2022 ident: bib0055 article-title: Identifying individuals by fNIRS-based brain functional network fingerprints publication-title: Front. Neurosci. – volume: 1 start-page: 147 year: 2011 end-page: 157 ident: bib0006 article-title: Connectivity gradients between the default mode and attention control networks publication-title: Brain Connect – volume: 22 start-page: 1026 year: 2018 end-page: 1037 ident: bib0046 article-title: Connectivity fingerprints: from areal descriptions to abstract spaces publication-title: Trends Cogn. Sci. – volume: 19 start-page: 2528 year: 2019 ident: bib0070 article-title: Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network publication-title: Sensors – volume: 6 start-page: 224ed4 year: 2014 ident: bib0003 article-title: UK biobank data: come and get it publication-title: Sci. Transl. Med. – volume: 62 start-page: 2222 year: 2012 end-page: 2231 ident: bib0065 article-title: The human connectome project: a data acquisition perspective publication-title: Neuroimage – volume: 536 start-page: 171 year: 2016 end-page: 178 ident: bib0024 article-title: A multi-modal parcellation of human cerebral cortex publication-title: Nature – volume: 34 start-page: 537 year: 1995 end-page: 541 ident: bib0008 article-title: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI publication-title: Magn. Reson. Med. – volume: 44 start-page: 5294 year: 2023 end-page: 5308 ident: bib0028 article-title: High-accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding publication-title: Hum. Brain Mapp. – reference: , 2014. – volume: 80 start-page: 105 year: 2013 end-page: 124 ident: bib0023 article-title: The minimal preprocessing pipelines for the human connectome project publication-title: Neuroimage – volume: 72 start-page: 665 year: 2011 end-page: 678 ident: bib0054 article-title: Functional network organization of the human brain publication-title: Neuron – reference: Stampacchia S., Asadi S., Tomczyk S., et al. Fingerprints of brain disease: connectome identifiability in cognitive decline and Alzheimer's disease. bioRxiv, 2022: 2022.02. 04.479112. – volume: 62 start-page: 782 year: 2012 end-page: 790 ident: bib0032 article-title: Fsl publication-title: Neuroimage – reference: Da K. A method for stochastic optimization. arXiv preprint – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: bib0029 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 27 start-page: 5415 year: 2017 end-page: 5429 ident: bib0048 article-title: Influences on the test–retest reliability of functional connectivity MRI and its relationship with behavioral utility publication-title: Cereb. Cortex – volume: 80 start-page: 144 year: 2013 end-page: 168 ident: bib0059 article-title: Resting-state fMRI in the human connectome project publication-title: Neuroimage – volume: 1050 start-page: 1 year: 2014 ident: bib0036 article-title: Auto-Encoding Variational Bayes[J] publication-title: Statistics – volume: 5 start-page: 261 year: 2022 ident: bib0045 article-title: Brain connectivity fingerprinting and behavioural prediction rest on distinct functional systems of the human connectome publication-title: Commun. Biol. – volume: 103 start-page: 13848 year: 2006 end-page: 13853 ident: bib0017 article-title: Consistent resting-state networks across healthy subjects publication-title: Proc. Natl. Acad. Sci. – volume: 54 start-page: 4311 year: 2006 end-page: 4322 ident: bib0002 article-title: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation publication-title: IEEE Trans. Signal Process. – start-page: 83 year: 2019 end-page: 94 ident: bib0058 article-title: A machine learning framework for accurate functional connectome fingerprinting and an application of a siamese network publication-title: International Workshop on Connectomics in Neuroimaging – reference: Krizhevsky A., Hinton G. Convolutional deep belief networks on cifar-10. Unpublished manuscript, 2010, 40(7): 1–9. – volume: 160 start-page: 140 year: 2017 end-page: 151 ident: bib0021 article-title: Can brain state be manipulated to emphasize individual differences in functional connectivity? publication-title: Neuroimage – volume: 221 year: 2020 ident: bib0001 article-title: GEFF: graph embedding for functional fingerprinting publication-title: Neuroimage – volume: 28 start-page: 3483 year: 2015 end-page: 3491 ident: bib0060 article-title: Learning structured output representation using deep conditional generative models publication-title: Adv. Neural Inf. Process. Syst. – volume: 23 year: 2020 ident: bib0018 article-title: Functional connectivity fingerprints at rest are similar across youths and adults and vary with genetic similarity publication-title: iScience – reference: Wu-Minn H.C.P. 1200 subjects data release reference manual. URL – volume: 18 start-page: 115 year: 2017 end-page: 126 ident: bib0052 article-title: Scanning the horizon: towards transparent and reproducible neuroimaging research publication-title: Nat. Rev. Neurosc. – volume: 90 start-page: 449 year: 2014 end-page: 468 ident: bib0056 article-title: Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers publication-title: Neuroimage – volume: 229 year: 2021 ident: bib0063 article-title: High-resolution connectomic fingerprints: mapping neural identity and behavior publication-title: Neuroimage – reference: Da Silva Castanheira J., Wiesman A.I., Hansen J.Y., et al. The neurophysiological brain-fingerprint of Parkinson's disease. medRxiv, 2023. – volume: 6 start-page: 295 year: 2020 end-page: 302 ident: bib0031 article-title: Multimodal deep generative models for trajectory prediction: a conditional variational autoencoder approach publication-title: IEEE Robot. Autom. Lett. – volume: 370 year: 2015 ident: bib0049 article-title: Brain/MINDS: brain-mapping project in Japan publication-title: Philos. Trans. R. Soc. B – volume: 33 start-page: 5025 year: 2023 end-page: 5041 ident: bib0014 article-title: Functional connectome stability and optimality are markers of cognitive performance publication-title: Cereb. Cortex – volume: 241 year: 2021 ident: 10.1016/j.neuroimage.2024.120651_bib0035 article-title: Representation learning of resting state fMRI with variational autoencoder publication-title: Neuroimage doi: 10.1016/j.neuroimage.2021.118423 – volume: 9 start-page: eabq8566 issue: 3 year: 2023 ident: 10.1016/j.neuroimage.2024.120651_bib0051 article-title: Robust dynamic brain coactivation states estimated in individuals publication-title: Sci. Adv. doi: 10.1126/sciadv.abq8566 – volume: 27 start-page: 291 year: 2014 ident: 10.1016/j.neuroimage.2024.120651_bib0068 article-title: A preliminary study on the effects of acute ethanol ingestion on default mode network and temporal fractal properties of the brain publication-title: Magn. Reson. Mater. Phys., Biol. Med. doi: 10.1007/s10334-013-0420-5 – volume: 33 start-page: 5025 issue: 8 year: 2023 ident: 10.1016/j.neuroimage.2024.120651_bib0014 article-title: Functional connectome stability and optimality are markers of cognitive performance publication-title: Cereb. Cortex doi: 10.1093/cercor/bhac396 – volume: 1 start-page: 147 issue: 2 year: 2011 ident: 10.1016/j.neuroimage.2024.120651_bib0006 article-title: Connectivity gradients between the default mode and attention control networks publication-title: Brain Connect doi: 10.1089/brain.2011.0007 – volume: 160 start-page: 140 year: 2017 ident: 10.1016/j.neuroimage.2024.120651_bib0021 article-title: Can brain state be manipulated to emphasize individual differences in functional connectivity? publication-title: Neuroimage doi: 10.1016/j.neuroimage.2017.03.064 – ident: 10.1016/j.neuroimage.2024.120651_bib0062 doi: 10.1101/2022.02.04.479112 – volume: 158 start-page: 371 year: 2017 ident: 10.1016/j.neuroimage.2024.120651_bib0067 article-title: Evaluating the replicability, specificity, and generalizability of connectome fingerprints publication-title: Neuroimage doi: 10.1016/j.neuroimage.2017.07.016 – volume: 103 start-page: 13848 issue: 37 year: 2006 ident: 10.1016/j.neuroimage.2024.120651_bib0017 article-title: Consistent resting-state networks across healthy subjects publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0601417103 – ident: 10.1016/j.neuroimage.2024.120651_bib0015 – volume: 22 start-page: 1026 issue: 11 year: 2018 ident: 10.1016/j.neuroimage.2024.120651_bib0046 article-title: Connectivity fingerprints: from areal descriptions to abstract spaces publication-title: Trends Cogn. Sci. doi: 10.1016/j.tics.2018.08.009 – volume: 34 start-page: 537 issue: 4 year: 1995 ident: 10.1016/j.neuroimage.2024.120651_bib0008 article-title: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI publication-title: Magn. Reson. Med. doi: 10.1002/mrm.1910340409 – volume: 6 start-page: 224ed4 issue: 224 year: 2014 ident: 10.1016/j.neuroimage.2024.120651_bib0003 article-title: UK biobank data: come and get it publication-title: Sci. Transl. Med. doi: 10.1126/scitranslmed.3008601 – volume: 35 start-page: 6849 issue: 17 year: 2015 ident: 10.1016/j.neuroimage.2024.120651_bib0030 article-title: Tracking the brain's functional coupling dynamics over development publication-title: J. Neurosci. doi: 10.1523/JNEUROSCI.4638-14.2015 – volume: 18 start-page: 115 issue: 2 year: 2017 ident: 10.1016/j.neuroimage.2024.120651_bib0052 article-title: Scanning the horizon: towards transparent and reproducible neuroimaging research publication-title: Nat. Rev. Neurosc. doi: 10.1038/nrn.2016.167 – volume: 313 start-page: 504 issue: 5786 year: 2006 ident: 10.1016/j.neuroimage.2024.120651_bib0029 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 16 start-page: 1348 issue: 9 year: 2013 ident: 10.1016/j.neuroimage.2024.120651_bib0013 article-title: Multi-task connectivity reveals flexible hubs for adaptive task control publication-title: Nat. Neurosci. doi: 10.1038/nn.3470 – volume: 72 start-page: 665 issue: 4 year: 2011 ident: 10.1016/j.neuroimage.2024.120651_bib0054 article-title: Functional network organization of the human brain publication-title: Neuron doi: 10.1016/j.neuron.2011.09.006 – volume: 120 year: 2023 ident: 10.1016/j.neuroimage.2024.120651_bib0071 article-title: Hydrogenerator early fault detection: sparse dictionary learning jointly with the variational autoencoder publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2023.105859 – ident: 10.1016/j.neuroimage.2024.120651_bib0040 – volume: 6 start-page: 295 issue: 2 year: 2020 ident: 10.1016/j.neuroimage.2024.120651_bib0031 article-title: Multimodal deep generative models for trajectory prediction: a conditional variational autoencoder approach publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2020.3043163 – volume: 54 start-page: 4311 issue: 11 year: 2006 ident: 10.1016/j.neuroimage.2024.120651_bib0002 article-title: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2006.881199 – start-page: 83 year: 2019 ident: 10.1016/j.neuroimage.2024.120651_bib0058 article-title: A machine learning framework for accurate functional connectome fingerprinting and an application of a siamese network – volume: 92 start-page: 574 issue: 3 year: 2016 ident: 10.1016/j.neuroimage.2024.120651_bib0005 article-title: The human brain project: creating a European research infrastructure to decode the human brain publication-title: Neuron doi: 10.1016/j.neuron.2016.10.046 – year: 2023 ident: 10.1016/j.neuroimage.2024.120651_bib0072 article-title: An overview of brain fingerprint identification based on various neuroimaging technologies – volume: 11 start-page: 9890 year: 2022 ident: 10.1016/j.neuroimage.2024.120651_bib0007 article-title: Re-routing drugs to blood brain barrier: a comprehensive analysis of machine learning approaches with fingerprint amalgamation and data balancing publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3233110 – volume: 92 start-page: 591 issue: 3 year: 2016 ident: 10.1016/j.neuroimage.2024.120651_bib0053 article-title: China brain project: basic neuroscience, brain diseases, and brain-inspired computing publication-title: Neuron doi: 10.1016/j.neuron.2016.10.050 – volume: 80 start-page: 105 year: 2013 ident: 10.1016/j.neuroimage.2024.120651_bib0023 article-title: The minimal preprocessing pipelines for the human connectome project publication-title: Neuroimage doi: 10.1016/j.neuroimage.2013.04.127 – start-page: 3581 year: 2014 ident: 10.1016/j.neuroimage.2024.120651_bib0037 article-title: Semi-supervised learning with deep generative models – volume: 438 year: 2024 ident: 10.1016/j.neuroimage.2024.120651_bib0073 article-title: Conditional variational autoencoder with Gaussian process regression recognition for parametric models publication-title: J. Comput. Appl. Math. doi: 10.1016/j.cam.2023.115532 – volume: 19 start-page: 2528 issue: 11 year: 2019 ident: 10.1016/j.neuroimage.2024.120651_bib0070 article-title: Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network publication-title: Sensors doi: 10.3390/s19112528 – volume: 80 start-page: 144 year: 2013 ident: 10.1016/j.neuroimage.2024.120651_bib0059 article-title: Resting-state fMRI in the human connectome project publication-title: Neuroimage doi: 10.1016/j.neuroimage.2013.05.039 – volume: 19 start-page: 1523 issue: 11 year: 2016 ident: 10.1016/j.neuroimage.2024.120651_bib0047 article-title: Multimodal population brain imaging in the UK Biobank prospective epidemiological study publication-title: Nat. Neurosci. doi: 10.1038/nn.4393 – volume: 250 year: 2022 ident: 10.1016/j.neuroimage.2024.120651_bib0027 article-title: Brain structure-function coupling provides signatures for task decoding and individual fingerprinting publication-title: Neuroimage doi: 10.1016/j.neuroimage.2022.118970 – volume: 42 start-page: 3717 issue: 12 year: 2021 ident: 10.1016/j.neuroimage.2024.120651_bib0042 article-title: Feature selection framework for functional connectome fingerprinting publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.25379 – volume: 8 start-page: 8254 issue: 1 year: 2018 ident: 10.1016/j.neuroimage.2024.120651_bib0004 article-title: The quest for identifiability in human functional connectomes publication-title: Sci. Rep. doi: 10.1038/s41598-018-25089-1 – volume: 28 start-page: 3483 year: 2015 ident: 10.1016/j.neuroimage.2024.120651_bib0060 article-title: Learning structured output representation using deep conditional generative models publication-title: Adv. Neural Inf. Process. Syst. – volume: 98 start-page: 439 issue: 2 year: 2018 ident: 10.1016/j.neuroimage.2024.120651_bib0025 article-title: Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation publication-title: Neuron doi: 10.1016/j.neuron.2018.03.035 – volume: 42 start-page: 2691 issue: 9 year: 2021 ident: 10.1016/j.neuroimage.2024.120651_bib0010 article-title: Functional connectome fingerprinting: identifying individuals and predicting cognitive functions via autoencoder publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.25394 – volume: 1050 start-page: 1 year: 2014 ident: 10.1016/j.neuroimage.2024.120651_bib0036 article-title: Auto-Encoding Variational Bayes[J] publication-title: Statistics – volume: 28 start-page: 2922 issue: 8 year: 2018 ident: 10.1016/j.neuroimage.2024.120651_bib0050 article-title: Spatiotemporal network markers of individual variability in the human functional connectome publication-title: Cereb. Cortex doi: 10.1093/cercor/bhx170 – volume: 229 year: 2021 ident: 10.1016/j.neuroimage.2024.120651_bib0063 article-title: High-resolution connectomic fingerprints: mapping neural identity and behavior publication-title: Neuroimage – volume: 27 start-page: 5415 issue: 11 year: 2017 ident: 10.1016/j.neuroimage.2024.120651_bib0048 article-title: Influences on the test–retest reliability of functional connectivity MRI and its relationship with behavioral utility publication-title: Cereb. Cortex doi: 10.1093/cercor/bhx230 – ident: 10.1016/j.neuroimage.2024.120651_bib0016 doi: 10.1101/2023.02.03.23285441 – volume: 8 start-page: 197 issue: 4 year: 2018 ident: 10.1016/j.neuroimage.2024.120651_bib0012 article-title: Individual identification using the functional brain fingerprint detected by the recurrent neural network publication-title: Brain Connect doi: 10.1089/brain.2017.0561 – ident: 10.1016/j.neuroimage.2024.120651_bib0069 – volume: 80 start-page: 62 year: 2013 ident: 10.1016/j.neuroimage.2024.120651_bib0066 article-title: The WU-Minn human connectome project: an overview publication-title: Neuroimage doi: 10.1016/j.neuroimage.2013.05.041 – volume: 44 start-page: 5294 issue: 16 year: 2023 ident: 10.1016/j.neuroimage.2024.120651_bib0028 article-title: High-accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.26423 – volume: 185 start-page: 35 year: 2019 ident: 10.1016/j.neuroimage.2024.120651_bib0033 article-title: Mapping the human brain's cortical-subcortical functional network organization publication-title: Neuroimage doi: 10.1016/j.neuroimage.2018.10.006 – volume: 16 year: 2022 ident: 10.1016/j.neuroimage.2024.120651_bib0055 article-title: Identifying individuals by fNIRS-based brain functional network fingerprints publication-title: Front. Neurosci. doi: 10.3389/fnins.2022.813293 – volume: 40 start-page: 4843 issue: 16 year: 2019 ident: 10.1016/j.neuroimage.2024.120651_bib0009 article-title: Refined measure of functional connectomes for improved identifiability and prediction publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.24741 – volume: 2 start-page: 56 issue: 1–2 year: 1994 ident: 10.1016/j.neuroimage.2024.120651_bib0022 article-title: Functional and effective connectivity in neuroimaging: a synthesis publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.460020107 – volume: 116 start-page: 22851 issue: 45 year: 2019 ident: 10.1016/j.neuroimage.2024.120651_bib0057 article-title: Trait-like variants in human functional brain networks publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.1902932116 – volume: 18 start-page: 1664 issue: 11 year: 2015 ident: 10.1016/j.neuroimage.2024.120651_bib0020 article-title: Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity publication-title: Nat. Neurosci. doi: 10.1038/nn.4135 – volume: 62 start-page: 782 issue: 2 year: 2012 ident: 10.1016/j.neuroimage.2024.120651_bib0032 article-title: Fsl publication-title: Neuroimage doi: 10.1016/j.neuroimage.2011.09.015 – volume: 14 start-page: 451 year: 2019 ident: 10.1016/j.neuroimage.2024.120651_bib0064 article-title: Unsupervised pathology detection in medical images using conditional variational autoencoders publication-title: Int. J. Comput. Assist. Radiol. Surg. doi: 10.1007/s11548-018-1898-0 – volume: 100 start-page: 253 issue: 1 year: 2003 ident: 10.1016/j.neuroimage.2024.120651_bib0026 article-title: Functional connectivity in the resting brain: a network analysis of the default mode hypothesis publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0135058100 – volume: 5 start-page: 261 issue: 1 year: 2022 ident: 10.1016/j.neuroimage.2024.120651_bib0045 article-title: Brain connectivity fingerprinting and behavioural prediction rest on distinct functional systems of the human connectome publication-title: Commun. Biol. doi: 10.1038/s42003-022-03185-3 – volume: 33 start-page: 1003 issue: 5 year: 2012 ident: 10.1016/j.neuroimage.2024.120651_bib0034 article-title: Effects of morphine and alcohol on functional brain connectivity during “resting state”: a placebo-controlled crossover study in healthy young men publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.21265 – volume: 370 issue: 1668 year: 2015 ident: 10.1016/j.neuroimage.2024.120651_bib0049 article-title: Brain/MINDS: brain-mapping project in Japan publication-title: Philos. Trans. R. Soc. B doi: 10.1098/rstb.2014.0310 – volume: 23 issue: 1 year: 2020 ident: 10.1016/j.neuroimage.2024.120651_bib0018 article-title: Functional connectivity fingerprints at rest are similar across youths and adults and vary with genetic similarity publication-title: iScience doi: 10.1016/j.isci.2019.100801 – volume: 141 start-page: 539 year: 2018 ident: 10.1016/j.neuroimage.2024.120651_bib0044 article-title: Deep learning based pipeline for fingerprinting using brain functional MRI connectivity data publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2018.10.129 – volume: 62 start-page: 2222 issue: 4 year: 2012 ident: 10.1016/j.neuroimage.2024.120651_bib0065 article-title: The human connectome project: a data acquisition perspective publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.02.018 – volume: 90 start-page: 449 year: 2014 ident: 10.1016/j.neuroimage.2024.120651_bib0056 article-title: Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers publication-title: Neuroimage doi: 10.1016/j.neuroimage.2013.11.046 – volume: 221 year: 2020 ident: 10.1016/j.neuroimage.2024.120651_bib0001 article-title: GEFF: graph embedding for functional fingerprinting publication-title: Neuroimage doi: 10.1016/j.neuroimage.2020.117181 – volume: 536 start-page: 171 issue: 7615 year: 2016 ident: 10.1016/j.neuroimage.2024.120651_bib0024 article-title: A multi-modal parcellation of human cerebral cortex publication-title: Nature doi: 10.1038/nature18933 – volume: 45 start-page: e26561 issue: 1 year: 2024 ident: 10.1016/j.neuroimage.2024.120651_bib0041 article-title: Discovering individual fingerprints in resting-state functional connectivity using deep neural networks publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.26561 |
| SSID | ssj0009148 |
| Score | 2.4837744 |
| Snippet | •High inter-subject variability for brain fingerprinting and cognitive behavior predicting.•Conditional deep generative network for extracting shared... The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of... |
| SourceID | doaj proquest pubmed crossref elsevier |
| SourceType | Open Website Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 120651 |
| SubjectTerms | Accuracy Adult Biomarkers Brain Brain - diagnostic imaging Brain - physiology Cognition & reasoning Cognition - physiology Cognitive behavior predicting Conditional variational autoencoder network Connectome - methods Datasets Embedding Female Fingerprint Fingerprinting Functional connectivity Functional magnetic resonance imaging Humans Individual identification Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Nerve Net - diagnostic imaging Nerve Net - physiology Neural networks Neuroimaging |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwELYqhBCXipZHt0DlSr2mTdaOH-JUEKiXoh5aiZtlxzbaqmTRbhap_54ZOwlwQOyBSw6xJ7I8M55vJuMZQr4AZo86alfAaFNwF0WhXAWKpzyfRqWsZjY1m5CXl-rqSv961OoLc8JyeeC8cd9cHS0IGeDqELkNDGhl6Vmyk7H06ZovoJ7BmRrK7QLK7_N2cjZXqg45uwEdBZ9wyr9WU7C91RNjlGr2P7FJz2HOZHsudsjbHjTS73mx78ib0L4nWz_73-K7pDvFRg80phAdRuowl5na1tMxO4gO9_Hp7QLp0gxMer-maNpyRBCmw7HbdPObQOeRYiljivUkFsVy5TBgQ-_Atc6Vvf_vkT8X57_PfhR9O4WiAUzW4VMKG3m0PjS8js5aXfkgnOJOxChqC3ChVFrWMsq6ZoEzC95Io60QmkvJ9slGO2_DB0LB7lmw694J7wBPcR301Hnu66CEsqKcEDnsq2n6WuPY8uKfGZLK_poHjhjkiMkcmZBqpLzN9TbWoDlF1o3zsWJ2egFyZHo5Mi_J0YTogfFmuJQKxyh8aLbGAk5G2h64ZECyJvXRIGemP0CWhiEUBGdXwvDncRhUH__n2DbMV2lOyST6gBNykOVz3AOmpFKgBR9fY28OyTauF0PaVX1ENrrFKhyTzeaumy0Xn5Lm3QOggTYd priority: 102 providerName: Directory of Open Access Journals |
| Title | Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1053811924001460 https://dx.doi.org/10.1016/j.neuroimage.2024.120651 https://www.ncbi.nlm.nih.gov/pubmed/38788914 https://www.proquest.com/docview/3066314471 https://www.proquest.com/docview/3060373056 https://doaj.org/article/b5fa488487ef4ae393a70d363554f0d8 |
| Volume | 295 |
| WOSCitedRecordID | wos001247179900001&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: 1095-9572 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1095-9572 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: AIEXJ dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1095-9572 dateEnd: 20251009 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: M7P dateStart: 19980501 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1095-9572 dateEnd: 20251009 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: 7X7 dateStart: 20020801 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1095-9572 dateEnd: 20251009 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: BENPR dateStart: 19980501 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Psychology Database customDbUrl: eissn: 1095-9572 dateEnd: 20251009 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: M2M dateStart: 20020801 isFulltext: true titleUrlDefault: https://www.proquest.com/psychology providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwELfYhtBe-IYVRmUkXgNN7MS2eEAUbeKlVYVA6ltkx_bUiSUlbSfx33PnOKn2AKrEix8SX5TE9_G78_mOkHeA2b3yyiRwt0q48UUiTQqCJy3PvJRaMR2aTYj5XC6XahEDbpuYVtnrxKCobVNhjPwDQ9sI6F-kn9a_EuwahbursYXGETnBKgkspO4t9kV3U94dhctZItNUxUyeLr8r1Itc3YDUgpeY8fdpBtY4vWOeQhX_O1bqbyg0WKPLR__7HY_Jw4hD6eeOcZ6Qe65-Sh7M4k77M7KdYu8I6kPUD4N_mB5NdW3pkHBE-yP-dN0iXZiBefRXFK1lF2SE6aDJq21z42jjKVZHpliiok02O4MxIHoL3npXLPz3c_Lj8uL7l69J7NCQVADztjiKQnvutXUVz73RWqXWFUZyU3hf5BoQyEQqkQsv8pw5zjQ4OJXSRaG4EOwFOa6b2p0RCqZUA1SwprAGIBpXTmXGcps7WUhdTEZE9AtTVrF8OXbR-Fn2eWrX5X5JS1zSslvSEUkHynVXwuMAmimu_TAfi3CHC017VUaZLk3uNeg_cPmc59oxYGsxsSxAOD-xckRUzzllf84VNDM8aHXAC3wcaCMW6jDOgdTnPfOVUSdtyj3njcjb4TZoE9wi0rVrdmHOhAl0K0fkZcfgwz9gUkgJIvXq3w9_TU7xTTD-nebn5Hjb7twbcr-63a427ZgciaUIoxyTk-nFfPFtHOIgMM6y2TgI8B9KIEtu |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKQcCF9yNQwEhwDGwSJ7aFEKJA1ardFYci9ebasV0topslmy3qn-I3MuM8Vj2A9tIDlz1sPFY2-83MN5PxDCGvgLN76aWJ4WoZM-OLWJgEFE9YlnohtMx0GDbBJxNxdCS_bpDf_VkYLKvsbWIw1LYqMUf-NkPfCOyfJx_mP2OcGoVvV_sRGi0s9t35LwjZFu_3PsP_-zpNd74cftqNu6kCcQnUpMFPXmjPvLauZLk3WsvEusIIZgrvi1yD1xwJyXPueZ5njmUaSHkpdVFIxnkG-14hVxlEQjgqYpyOV01-E9YevcuzWCSJ7CqH2nqy0J9yegpWAqLSlL1JUvD-yQV3GKYGXPCKf2O9wfvt3P7fntsdcqvj2fRjqxh3yYab3SPXx10lwX3SbONsDOpDVhOTm1j-TfXM0qGgivYtDOi8RrmwAs8JnFBkA20SFZaDpyqb6tTRylPs_kyxBUcdL5YGc1z0TIOOhxLk8wfk26X85odkc1bN3GNCgSpooELWFNYABWXSydRYZnMnCqGLUUR4DwRVdu3ZcUrID9XX4X1XKwgphJBqIRSRZJCcty1K1pDZRqwN67HJePiiqk9UZ7OUyb0G-w4hrfNMuwzUlo9sFiiqH1kREdkjVfXneMHzwEbTNW7g3SDbcb2Ww60pvdWDXXU2d6FWSI_Iy-EyWEt8BaZnrlqGNaOMY9gckUetQg3PIBNcCFDhJ__e_AW5sXs4PlAHe5P9p-Qm3hXm-pN8i2w29dI9I9fKs2a6qJ8H40DJ8WVr1R9ViKNL |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKQRUX3o-FAkaCY2geTmwLIUQpK6rCag8g9Wbs2K4W0c2SzRb1r_HrmHEeqx5Ae-mBSw6Jx8pm5_F5_HmGkBeA2b300kTwtIyY8UUkTAKGJyxLvRBaZjo0m-CTiTg-ltMt8rs_C4O0yt4nBkdtqxJz5HsZxkZA_zzZ8x0tYnowfrv4GWEHKdxp7dtptCpy5M5_wfJt-ebwAP7rl2k6_vDl_ceo6zAQlQBTGrzyQnvmtXUly73RWibWFUYwU3hf5BoiaCwkz7nneZ45lmkA6KXURSEZ5xnMe4Vc5Vi0PNAGp-uCvwlrj-HlWSSSRHYsopZbFmpVzk7BY8AKNWWvkhSQQHIhNIYOAhci5N8QcIiE45v_8ze8RW50-Ju-aw3mNtly8ztk53PHMLhLmn3smUF9yHZi0hNp4VTPLR2IVrQvbUAXNcqFEXh-4IQiSmiTqzAcIljZVKeOVp5iVWiKpTnqaLkymPuiZxpsP1CTz--Rr5fym--T7Xk1dw8JBQihASJZU1gD0JRJJ1Njmc2dKIQu4hHhvVKosivbjt1Dfqien_ddrdVJoTqpVp1GJBkkF23pkg1k9lHvhvFYfDzcqOoT1fkyZXKvwe_DUtd5pl0G5sxjmwXo6mMrRkT2Wqv6870QkWCi2QYv8HqQ7TBgi-02lN7tFV91vnip1lo_Is-Hx-BFcWtMz121CmPijONyekQetMY1fINMcCHAnB_9e_JnZAeMSX06nBw9JtfxpXALIMl3yXZTr9wTcq08a2bL-mnwE5R8u2yj-gOCUKwc |
| 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+fingerprinting+and+cognitive+behavior+predicting+using+functional+connectome+of+high+inter-subject+variability&rft.jtitle=NeuroImage+%28Orlando%2C+Fla.%29&rft.au=Lu%2C+Jiayu&rft.au=Yan%2C+Tianyi&rft.au=Yang%2C+Lan&rft.au=Zhang%2C+Xi&rft.date=2024-07-15&rft.pub=Elsevier+Limited&rft.issn=1053-8119&rft.eissn=1095-9572&rft.volume=295&rft_id=info:doi/10.1016%2Fj.neuroimage.2024.120651&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-8119&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-8119&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-8119&client=summon |