Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging
Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derive...
Uložené v:
| Vydané v: | Communications biology Ročník 7; číslo 1; s. 414 - 14 |
|---|---|
| Hlavní autori: | , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
Nature Publishing Group UK
05.04.2024
Nature Publishing Group Nature Portfolio |
| Predmet: | |
| ISSN: | 2399-3642, 2399-3642 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants’ T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.
A study utilizing unsupervised deep learning to generate interpretable brain imaging phenotypes from brain T1 and T2-FLAIR MRI identified 97 genetic loci enhancing understanding of brain structure genetics. |
|---|---|
| AbstractList | Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants’ T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.A study utilizing unsupervised deep learning to generate interpretable brain imaging phenotypes from brain T1 and T2-FLAIR MRI identified 97 genetic loci enhancing understanding of brain structure genetics. Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants’ T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes. Abstract Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants’ T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes. Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes. Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants’ T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes. A study utilizing unsupervised deep learning to generate interpretable brain imaging phenotypes from brain T1 and T2-FLAIR MRI identified 97 genetic loci enhancing understanding of brain structure genetics. |
| ArticleNumber | 414 |
| Author | Islam, Sheikh Muhammad Saiful Knaack, Alexander Xie, Ziqian Fletcher, Evan Xie, Yaochen Gottlieb, Assaf Fornage, Myriam Yuan, Hao Zhang, Wanheng Chen, Han Giancardo, Luca Ji, Shuiwang He, Wei Zhi, Degui Patel, Khush |
| Author_xml | – sequence: 1 givenname: Khush orcidid: 0000-0002-7451-3103 surname: Patel fullname: Patel, Khush organization: McWilliams School of Biomedical Informatics, University of Texas Health Science Center – sequence: 2 givenname: Ziqian surname: Xie fullname: Xie, Ziqian organization: McWilliams School of Biomedical Informatics, University of Texas Health Science Center – sequence: 3 givenname: Hao surname: Yuan fullname: Yuan, Hao organization: Department of Computer Science and Engineering, Texas A&M University – sequence: 4 givenname: Sheikh Muhammad Saiful surname: Islam fullname: Islam, Sheikh Muhammad Saiful organization: McWilliams School of Biomedical Informatics, University of Texas Health Science Center – sequence: 5 givenname: Yaochen orcidid: 0000-0003-0320-6728 surname: Xie fullname: Xie, Yaochen organization: Department of Computer Science and Engineering, Texas A&M University – sequence: 6 givenname: Wei surname: He fullname: He, Wei organization: McWilliams School of Biomedical Informatics, University of Texas Health Science Center – sequence: 7 givenname: Wanheng surname: Zhang fullname: Zhang, Wanheng organization: School of Public Health, University of Texas Health Science Center – sequence: 8 givenname: Assaf orcidid: 0000-0003-4904-631X surname: Gottlieb fullname: Gottlieb, Assaf organization: McWilliams School of Biomedical Informatics, University of Texas Health Science Center – sequence: 9 givenname: Han surname: Chen fullname: Chen, Han organization: McWilliams School of Biomedical Informatics, University of Texas Health Science Center, School of Public Health, University of Texas Health Science Center – sequence: 10 givenname: Luca orcidid: 0000-0002-4862-2277 surname: Giancardo fullname: Giancardo, Luca organization: McWilliams School of Biomedical Informatics, University of Texas Health Science Center – sequence: 11 givenname: Alexander orcidid: 0000-0002-5231-3637 surname: Knaack fullname: Knaack, Alexander organization: Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis – sequence: 12 givenname: Evan surname: Fletcher fullname: Fletcher, Evan organization: Department of Neurology and Imaging of Dementia and Aging (IDeA) Laboratory, University of California at Davis – sequence: 13 givenname: Myriam orcidid: 0000-0003-0677-8158 surname: Fornage fullname: Fornage, Myriam organization: School of Public Health, University of Texas Health Science Center, McGovern Medical School, University of Texas Health Science Center – sequence: 14 givenname: Shuiwang orcidid: 0000-0002-4205-4563 surname: Ji fullname: Ji, Shuiwang organization: Department of Computer Science and Engineering, Texas A&M University – sequence: 15 givenname: Degui orcidid: 0000-0001-7754-1890 surname: Zhi fullname: Zhi, Degui email: Degui.Zhi@uth.tmc.edu organization: McWilliams School of Biomedical Informatics, University of Texas Health Science Center |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38580839$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kk9v3CAQxa0qVZOm-QI9VEi99OJ2DBjDsYr6J1KkXpozwnhwWXnBBXul_fZl46StcsgJhH7vzQzzXldnIQasqrcNfGyAyU-ZUwBWA-U1CFCi7l5UF5QpVTPB6dl_9_PqKucdADRKKcH4q-qcyVaCZOqiOtyFvM6YDj7jQAbEmSScE2YMi1l8DGRCk4IPI8Fg-gkzmX9hiMtxRjL4bOMB05G4mMiIARdvick5Wr-J87IOvmiiI30yPhC_N2Mxe1O9dGbKePVwXlZ3X7_8vP5e3_74dnP9-ba2XLZLzcC1OFhoOaW2485w0zHVt8ZKBZ0ajOgUR6WoBONk3ynrXMMGZY0znZGCXVY3m-8QzU7PqZRPRx2N1_cPMY3apNL0hLovtSQwYWiLHG0jeWMVACL0VnABxevD5jWn-HvFvOh9mR-nyQSMa9YMGKecKdoU9P0TdBfXFMqkJ4p1glHRFerdA7X2exz-tve4nQLIDbAp5pzQaeu3rSzlMyfdgD5lQW9Z0CUL-j4L-uRNn0gf3Z8VsU2UCxxGTP_afkb1B-Hdx2E |
| CitedBy_id | crossref_primary_10_1016_j_autcon_2025_106423 crossref_primary_10_1038_s41380_025_03232_5 crossref_primary_10_1093_bib_bbaf037 crossref_primary_10_1371_journal_pgen_1011332 crossref_primary_10_20517_ais_2024_103 crossref_primary_10_1097_CORR_0000000000003679 crossref_primary_10_1002_qub2_93 crossref_primary_10_1007_s12021_025_09722_9 crossref_primary_10_1016_j_scib_2025_04_058 |
| Cites_doi | 10.1038/s41593-021-00826-4 10.1038/s41588-019-0516-6 10.1038/s41588-019-0512-x 10.1016/j.neuroimage.2021.118603 10.1038/s41562-019-0653-z 10.1016/j.schres.2005.11.020 10.1038/s41588-018-0108-x 10.1038/ng.3406 10.1016/j.neuroimage.2012.01.021 10.1006/nimg.2002.1132 10.1103/PhysRevE.102.042119 10.1073/pnas.1523888113 10.3389/fnins.2021.652987 10.1038/s41588-021-00954-4 10.1038/s41398-020-00902-6 10.1038/s41588-019-0450-7 10.1176/appi.ajp.160.4.636 10.1038/d41586-022-03252-z 10.1038/ng.3211 10.1038/s41467-019-09480-8 10.1161/STROKEAHA.109.569194 10.1038/s41467-017-01261-5 10.1073/pnas.1706100115 10.1038/s41588-019-0530-8 10.1007/s11682-013-9269-5 10.1126/science.1127647 10.1038/nn.4398 10.1038/nature14101 10.1016/j.media.2020.101871 10.1038/s41588-019-0511-y 10.1038/nn.4393 10.3389/fncom.2021.654315 10.1038/s41586-018-0579-z 10.1371/journal.pmed.1001779 10.1038/s41467-018-04362-x 10.1038/s41593-022-01042-4 10.1161/CIRCGENETICS.108.829747 10.1088/1361-6560/abcd1a 10.1038/s41380-019-0569-z 10.1109/JBHI.2019.2914970 10.1093/bioinformatics/btq340 10.1093/brain/awab140 10.1038/s41593-020-0643-5 10.1155/2015/450341 10.1038/ncomms13624 10.3389/fnhum.2018.00399 10.1038/s41467-019-13163-9 10.1016/j.neuroimage.2022.118871 10.1111/add.15511 10.1038/s41467-020-17368-1 10.1016/j.biopsych.2020.01.026 10.1126/science.aay6690 10.3389/fnins.2016.00503 10.1016/j.neuroimage.2012.12.068 10.1002/ajmg.b.32349 10.1038/s41588-018-0307-5 10.1016/j.media.2016.10.004 10.6084/m9.figshare.25203224.v2 10.1038/s41586-018-0571-7 10.1038/s41598-017-02584-5 10.1109/acpr.2015.7486599 10.1007/978-3-319-46723-8_49 10.6084/m9.figshare.25203230.v2 10.21105/joss.00861 10.6084/m9.figshare.25203233.v2 10.1016/j.artmed.2018.08.008 10.1016/j.neuroimage.2011.09.015 10.1007/978-3-319-55524-9_14 10.1038/s41380-017-0001-5 10.1109/IEMBS.2011.6091212 10.6084/m9.figshare.25148744.v2 10.1038/s41467-021-23175-z |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2024 – notice: 2024. The Author(s). – notice: The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7XB 88I 8FE 8FH 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M2P M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 DOA |
| DOI | 10.1038/s42003-024-06096-7 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Biological Science Collection Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) 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 Central Basic MEDLINE - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Central (New) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Biological Science Database ProQuest SciTech Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database CrossRef MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2399-3642 |
| EndPage | 14 |
| ExternalDocumentID | oai_doaj_org_article_b0f58036a25e4ec1841c900ee0bc6460 38580839 10_1038_s42003_024_06096_7 |
| Genre | Journal Article Research Support, N.I.H., Extramural |
| GeographicLocations | United Kingdom--UK |
| GeographicLocations_xml | – name: United Kingdom--UK |
| GrantInformation_xml | – fundername: U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) grantid: U01 AG070112-01A1 funderid: https://doi.org/10.13039/100000049 – fundername: NIA NIH HHS grantid: U01 AG070112 – fundername: NCATS NIH HHS grantid: UL1 TR003167 – fundername: NINDS NIH HHS grantid: R01 NS121154 – fundername: NEI NIH HHS grantid: R01 EY032768 |
| GroupedDBID | 0R~ 53G 88I AAJSJ ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV AFKRA AJTQC ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BBNVY BCNDV BENPR BHPHI C6C CCPQU DWQXO EBLON EBS GNUQQ GROUPED_DOAJ HCIFZ HYE M2P M7P M~E NAO O9- OK1 PGMZT PIMPY RNT RPM SNYQT AASML AAYXX AFFHD CITATION PHGZM PHGZT PQGLB CGR CUY CVF ECM EIF NPM 3V. 7XB 8FE 8FH 8FK AARCD LK8 PKEHL PQEST PQQKQ PQUKI PRINS Q9U 7X8 PUEGO |
| ID | FETCH-LOGICAL-c485t-30f5edc05422c74fa4a739b5ac89079da6794e99280af8b79cff13d9cafa7a863 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001197447800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2399-3642 |
| IngestDate | Tue Oct 14 18:50:31 EDT 2025 Thu Oct 02 07:52:30 EDT 2025 Wed Aug 13 10:58:03 EDT 2025 Thu Jan 02 22:23:22 EST 2025 Tue Nov 18 22:20:41 EST 2025 Sat Nov 29 02:09:05 EST 2025 Fri Feb 21 02:38:40 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | 2024. The Author(s). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c485t-30f5edc05422c74fa4a739b5ac89079da6794e99280af8b79cff13d9cafa7a863 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-7451-3103 0000-0002-4862-2277 0000-0001-7754-1890 0000-0002-4205-4563 0000-0002-5231-3637 0000-0003-0677-8158 0000-0003-0320-6728 0000-0003-4904-631X |
| OpenAccessLink | https://doaj.org/article/b0f58036a25e4ec1841c900ee0bc6460 |
| PMID | 38580839 |
| PQID | 3033763267 |
| PQPubID | 4669726 |
| PageCount | 14 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_b0f58036a25e4ec1841c900ee0bc6460 proquest_miscellaneous_3034243921 proquest_journals_3033763267 pubmed_primary_38580839 crossref_citationtrail_10_1038_s42003_024_06096_7 crossref_primary_10_1038_s42003_024_06096_7 springer_journals_10_1038_s42003_024_06096_7 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-04-05 |
| PublicationDateYYYYMMDD | 2024-04-05 |
| PublicationDate_xml | – month: 04 year: 2024 text: 2024-04-05 day: 05 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Communications biology |
| PublicationTitleAbbrev | Commun Biol |
| PublicationTitleAlternate | Commun Biol |
| PublicationYear | 2024 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | Thompson (CR2) 2014; 8 Joel (CR34) 2018; 12 Adams (CR9) 2016; 19 Makris (CR74) 2006; 83 Jiang, Zheng, Fang, Yang (CR77) 2021; 53 Hibar (CR6) 2015; 520 CR39 Kranzler (CR51) 2019; 10 CR38 Tudorascu (CR18) 2016; 10 Zhao (CR60) 2021; 26 Chekroud, Ward, Rosenberg, Holmes (CR33) 2016; 113 Goes (CR50) 2015; 168 CR31 Hinton, Salakhutdinov (CR26) 2006; 313 CR73 Ulyanov, Tarasevich, Eserkepov, Grigorieva (CR67) 2020; 102 Jenkinson, Bannister, Brady, Smith (CR69) 2002; 17 Brouwer (CR63) 2022; 25 Carlson, Henn, Al-Hindi, Ramachandran (CR68) 2022; 610 Martinez-Murcia, Ortiz, Gorriz, Ramirez, Castillo-Barnes (CR70) 2020; 24 Konstantinos (CR29) 2017; 36 Jonsson (CR36) 2019; 10 Hibar (CR7) 2017; 8 Miller (CR5) 2016; 19 Satizabal (CR8) 2019; 51 Smith (CR15) 2021; 24 Peng, Gong, Beckmann, Vedaldi, Smith (CR35) 2021; 68 CR46 Fischl (CR12) 2012; 62 Gottesman, Gould (CR61) 2003; 160 Pardiñas (CR47) 2019; 51 CR42 CR41 CR40 CR80 Zhao (CR17) 2019; 51 Evans (CR44) 2018; 50 Jiang (CR76) 2019; 51 Despotović, Goossens, Philips (CR20) 2015; 2015 Han (CR21) 2019; 11383 Sudlow (CR1) 2015; 12 Smeland (CR59) 2021; 89 Weng (CR71) 2020; 9 van der Meer (CR24) 2020; 11 CR19 Zhou (CR53) 2020; 23 Almuqhim, Saeed (CR72) 2021; 15 Debette (CR4) 2010; 41 CR14 Hashimoto (CR66) 2021; 66 CR13 CR57 Liu (CR54) 2019; 51 CR56 CR11 Wu (CR48) 2020; 10 Watanabe, Taskesen, van Bochoven (CR79) 2017; 8 Davies (CR58) 2018; 9 Feis, Brodersen, von Cramon, Luders, Tittgemeyer (CR32) 2013; 70 Lam (CR49) 2019; 51 Lorenzi (CR62) 2018; 115 Willer, Li, Abecasis (CR45) 2010; 26 Jolly, Hampshire (CR23) 2021; 144 CR28 Grasby (CR10) 2020; 367 CR27 CR25 Bulik-Sullivan (CR78) 2015; 47 Yamaguchi (CR30) 2021; 15 CR22 Wood (CR37) 2022; 249 Dao (CR52) 2021; 116 CR65 Bycroft (CR75) 2018; 562 CR64 Psaty (CR3) 2009; 2 Shadrin (CR16) 2021; 244 Evangelou (CR55) 2019; 3 Bulik-Sullivan (CR43) 2015; 47 E Evangelou (6096_CR55) 2019; 3 OB Smeland (6096_CR59) 2021; 89 J Carlson (6096_CR68) 2022; 610 M Lam (6096_CR49) 2019; 51 LM Evans (6096_CR44) 2018; 50 B Fischl (6096_CR12) 2012; 62 D-L Feis (6096_CR32) 2013; 70 KL Grasby (6096_CR10) 2020; 367 KL Miller (6096_CR5) 2016; 19 C Bycroft (6096_CR75) 2018; 562 M Lorenzi (6096_CR62) 2018; 115 M Jenkinson (6096_CR69) 2002; 17 X Han (6096_CR21) 2019; 11383 6096_CR39 6096_CR38 6096_CR73 B Zhao (6096_CR17) 2019; 51 6096_CR31 CL Satizabal (6096_CR8) 2019; 51 FJ Martinez-Murcia (6096_CR70) 2020; 24 DP Hibar (6096_CR7) 2017; 8 AE Jolly (6096_CR23) 2021; 144 DP Hibar (6096_CR6) 2015; 520 PM Thompson (6096_CR2) 2014; 8 AF Pardiñas (6096_CR47) 2019; 51 B Zhao (6096_CR60) 2021; 26 I Despotović (6096_CR20) 2015; 2015 6096_CR25 F Almuqhim (6096_CR72) 2021; 15 K Watanabe (6096_CR79) 2017; 8 DL Tudorascu (6096_CR18) 2016; 10 BA Jonsson (6096_CR36) 2019; 10 6096_CR28 S Debette (6096_CR4) 2010; 41 6096_CR27 HR Kranzler (6096_CR51) 2019; 10 F Hashimoto (6096_CR66) 2021; 66 MV Ulyanov (6096_CR67) 2020; 102 K Konstantinos (6096_CR29) 2017; 36 6096_CR22 6096_CR65 6096_CR64 H Peng (6096_CR35) 2021; 68 DA Wood (6096_CR37) 2022; 249 C Dao (6096_CR52) 2021; 116 AA Shadrin (6096_CR16) 2021; 244 D Joel (6096_CR34) 2018; 12 B Bulik-Sullivan (6096_CR78) 2015; 47 G Davies (6096_CR58) 2018; 9 BK Bulik-Sullivan (6096_CR43) 2015; 47 C Sudlow (6096_CR1) 2015; 12 6096_CR14 6096_CR13 6096_CR57 Y Wu (6096_CR48) 2020; 10 6096_CR56 6096_CR19 GE Hinton (6096_CR26) 2006; 313 L Jiang (6096_CR76) 2019; 51 SM Smith (6096_CR15) 2021; 24 M Liu (6096_CR54) 2019; 51 L Jiang (6096_CR77) 2021; 53 6096_CR11 N Makris (6096_CR74) 2006; 83 D van der Meer (6096_CR24) 2020; 11 H Yamaguchi (6096_CR30) 2021; 15 6096_CR80 AM Chekroud (6096_CR33) 2016; 113 II Gottesman (6096_CR61) 2003; 160 HHH Adams (6096_CR9) 2016; 19 RM Brouwer (6096_CR63) 2022; 25 FS Goes (6096_CR50) 2015; 168 CJ Willer (6096_CR45) 2010; 26 BM Psaty (6096_CR3) 2009; 2 J-C Weng (6096_CR71) 2020; 9 6096_CR46 H Zhou (6096_CR53) 2020; 23 6096_CR40 6096_CR42 6096_CR41 |
| References_xml | – ident: CR22 – ident: CR39 – volume: 24 start-page: 737 year: 2021 end-page: 745 ident: CR15 article-title: An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank publication-title: Nat. Neurosci. doi: 10.1038/s41593-021-00826-4 – volume: 51 start-page: 1637 year: 2019 end-page: 1644 ident: CR17 article-title: Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits publication-title: Nat. Genet. doi: 10.1038/s41588-019-0516-6 – volume: 51 start-page: 1670 year: 2019 end-page: 1678 ident: CR49 article-title: Comparative genetic architectures of schizophrenia in East Asian and European populations publication-title: Nat. Genet. doi: 10.1038/s41588-019-0512-x – volume: 244 year: 2021 ident: CR16 article-title: Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology publication-title: Neuroimage doi: 10.1016/j.neuroimage.2021.118603 – ident: CR80 – volume: 3 start-page: 950 year: 2019 end-page: 961 ident: CR55 article-title: New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders publication-title: Nat. Hum. Behav. doi: 10.1038/s41562-019-0653-z – volume: 83 start-page: 155 year: 2006 end-page: 171 ident: CR74 article-title: Decreased volume of left and total anterior insular lobule in schizophrenia publication-title: Schizophr. Res. doi: 10.1016/j.schres.2005.11.020 – ident: CR25 – volume: 50 start-page: 737 year: 2018 end-page: 745 ident: CR44 article-title: Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits publication-title: Nat. Genet. doi: 10.1038/s41588-018-0108-x – volume: 47 start-page: 1236 year: 2015 end-page: 1241 ident: CR78 article-title: An atlas of genetic correlations across human diseases and traits publication-title: Nat. Genet. doi: 10.1038/ng.3406 – ident: CR42 – volume: 62 start-page: 774 year: 2012 end-page: 781 ident: CR12 article-title: FreeSurfer publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.01.021 – volume: 17 start-page: 825 year: 2002 end-page: 841 ident: CR69 article-title: Improved optimization for the robust and accurate linear registration and motion correction of brain images publication-title: Neuroimage doi: 10.1006/nimg.2002.1132 – volume: 102 start-page: 042119 year: 2020 ident: CR67 article-title: Characterization of domain formation during random sequential adsorption of stiff linear k-mers onto a square lattice publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.102.042119 – volume: 113 start-page: E1968 year: 2016 ident: CR33 article-title: Patterns in the human brain mosaic discriminate males from females publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1523888113 – ident: CR46 – ident: CR19 – volume: 15 start-page: 652987 year: 2021 ident: CR30 article-title: Three-dimensional convolutional autoencoder extracts features of structural brain images with a ‘diagnostic label-free’ approach: application to schizophrenia datasets publication-title: Front. Neurosci. doi: 10.3389/fnins.2021.652987 – volume: 53 start-page: 1616 year: 2021 end-page: 1621 ident: CR77 article-title: A generalized linear mixed model association tool for biobank-scale data publication-title: Nat. Genet. doi: 10.1038/s41588-021-00954-4 – volume: 10 year: 2020 ident: CR48 article-title: Multi-trait analysis for genome-wide association study of five psychiatric disorders publication-title: Transl. Psychiatry doi: 10.1038/s41398-020-00902-6 – volume: 51 start-page: 1193 year: 2019 ident: CR47 article-title: Publisher Correction: Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection publication-title: Nat. Genet. doi: 10.1038/s41588-019-0450-7 – volume: 160 start-page: 636 year: 2003 end-page: 645 ident: CR61 article-title: The endophenotype concept in psychiatry: etymology and strategic intentions publication-title: Am. J. Psychiatry doi: 10.1176/appi.ajp.160.4.636 – ident: CR11 – ident: CR57 – volume: 610 start-page: 444 year: 2022 end-page: 447 ident: CR68 article-title: Counter the weaponization of genetics research by extremists publication-title: Nature doi: 10.1038/d41586-022-03252-z – volume: 47 start-page: 291 year: 2015 end-page: 295 ident: CR43 article-title: LD Score regression distinguishes confounding from polygenicity in genome-wide association studies publication-title: Nat. Genet. doi: 10.1038/ng.3211 – ident: CR64 – volume: 10 year: 2019 ident: CR51 article-title: Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations publication-title: Nat. Commun. doi: 10.1038/s41467-019-09480-8 – volume: 41 start-page: 210 year: 2010 end-page: 217 ident: CR4 article-title: Genome-wide association studies of MRI-defined brain infarcts: meta-analysis from the CHARGE Consortium publication-title: Stroke doi: 10.1161/STROKEAHA.109.569194 – volume: 8 start-page: 1 year: 2017 end-page: 11 ident: CR79 article-title: Functional mapping and annotation of genetic associations with FUMA publication-title: Nat. Commun. doi: 10.1038/s41467-017-01261-5 – volume: 115 start-page: 3162 year: 2018 end-page: 3167 ident: CR62 article-title: Susceptibility of brain atrophy in Alzheimer’s disease, evidence from functional prioritization in imaging genetics publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1706100115 – volume: 51 start-page: 1749 year: 2019 end-page: 1755 ident: CR76 article-title: A resource-efficient tool for mixed model association analysis of large-scale data publication-title: Nat. Genet. doi: 10.1038/s41588-019-0530-8 – volume: 8 start-page: 153 year: 2014 end-page: 182 ident: CR2 article-title: The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data publication-title: Brain Imaging Behav. doi: 10.1007/s11682-013-9269-5 – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: CR26 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 19 start-page: 1569 year: 2016 end-page: 1582 ident: CR9 article-title: Novel genetic loci underlying human intracranial volume identified through genome-wide association publication-title: Nat. Neurosci. doi: 10.1038/nn.4398 – ident: CR14 – volume: 520 start-page: 224 year: 2015 end-page: 229 ident: CR6 article-title: Common genetic variants influence human subcortical brain structures publication-title: Nature doi: 10.1038/nature14101 – volume: 68 start-page: 101871 year: 2021 ident: CR35 article-title: Accurate brain age prediction with lightweight deep neural networks publication-title: Med. Image Anal. doi: 10.1016/j.media.2020.101871 – volume: 51 start-page: 1624 year: 2019 end-page: 1636 ident: CR8 article-title: Genetic architecture of subcortical brain structures in 38,851 individuals publication-title: Nat. Genet. doi: 10.1038/s41588-019-0511-y – volume: 19 start-page: 1523 year: 2016 end-page: 1536 ident: CR5 article-title: Multimodal population brain imaging in the UK Biobank prospective epidemiological study publication-title: Nat. Neurosci. doi: 10.1038/nn.4393 – volume: 15 start-page: 654315 year: 2021 ident: CR72 article-title: ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data publication-title: Front. Comput. Neurosci. doi: 10.3389/fncom.2021.654315 – ident: CR56 – ident: CR40 – volume: 562 start-page: 203 year: 2018 end-page: 209 ident: CR75 article-title: The UK Biobank resource with deep phenotyping and genomic data publication-title: Nature doi: 10.1038/s41586-018-0579-z – volume: 12 start-page: e1001779 year: 2015 ident: CR1 article-title: UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age publication-title: PLoS Med. doi: 10.1371/journal.pmed.1001779 – volume: 9 year: 2018 ident: CR58 article-title: Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function publication-title: Nat. Commun. doi: 10.1038/s41467-018-04362-x – volume: 25 start-page: 421 year: 2022 end-page: 432 ident: CR63 article-title: Genetic variants associated with longitudinal changes in brain structure across the lifespan publication-title: Nat. Neurosci. doi: 10.1038/s41593-022-01042-4 – volume: 2 start-page: 73 year: 2009 end-page: 80 ident: CR3 article-title: Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts publication-title: Circ. Cardiovasc. Genet. doi: 10.1161/CIRCGENETICS.108.829747 – ident: CR27 – volume: 66 start-page: 015006 year: 2021 ident: CR66 article-title: 4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/abcd1a – volume: 9 start-page: 658 year: 2020 ident: CR71 article-title: An autoencoder and machine learning model to predict suicidal ideation with brain structural imaging publication-title: J. Clin. Med. Res. – volume: 26 start-page: 3943 year: 2021 end-page: 3955 ident: CR60 article-title: Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706) publication-title: Mol. Psychiatry doi: 10.1038/s41380-019-0569-z – volume: 24 start-page: 17 year: 2020 end-page: 26 ident: CR70 article-title: Studying the manifold structure of alzheimer’s disease: a deep learning approach using convolutional autoencoders publication-title: IEEE J. Biomed. Health Inf. doi: 10.1109/JBHI.2019.2914970 – volume: 26 start-page: 2190 year: 2010 end-page: 2191 ident: CR45 article-title: METAL: fast and efficient meta-analysis of genomewide association scans publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq340 – volume: 144 start-page: 1038 year: 2021 end-page: 1040 ident: CR23 article-title: A robust brain signature region approach for episodic memory performance in older adults publication-title: Brain J. Neurol. doi: 10.1093/brain/awab140 – ident: CR73 – ident: CR65 – volume: 23 start-page: 809 year: 2020 end-page: 818 ident: CR53 article-title: Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits publication-title: Nat. Neurosci. doi: 10.1038/s41593-020-0643-5 – volume: 2015 start-page: 450341 year: 2015 ident: CR20 article-title: MRI segmentation of the human brain: challenges, methods, and applications publication-title: Comput. Math. Methods Med. doi: 10.1155/2015/450341 – volume: 11383 start-page: 105 year: 2019 end-page: 114 ident: CR21 article-title: Patient-specific registration of pre-operative and post-recurrence brain tumor MRI scans publication-title: Brainlesion – ident: CR38 – volume: 8 year: 2017 ident: CR7 article-title: Novel genetic loci associated with hippocampal volume publication-title: Nat. Commun. doi: 10.1038/ncomms13624 – volume: 12 start-page: 399 year: 2018 ident: CR34 article-title: Analysis of human brain structure reveals that the brain ‘types’ typical of males are also typical of females, and vice versa publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2018.00399 – volume: 10 year: 2019 ident: CR36 article-title: Brain age prediction using deep learning uncovers associated sequence variants publication-title: Nat. Commun. doi: 10.1038/s41467-019-13163-9 – ident: CR31 – volume: 249 year: 2022 ident: CR37 article-title: Accurate brain-age models for routine clinical MRI examinations publication-title: Neuroimage doi: 10.1016/j.neuroimage.2022.118871 – ident: CR13 – volume: 116 start-page: 3044 year: 2021 end-page: 3054 ident: CR52 article-title: The impact of removing former drinkers from genome-wide association studies of AUDIT-C publication-title: Addiction doi: 10.1111/add.15511 – volume: 11 year: 2020 ident: CR24 article-title: Understanding the genetic determinants of the brain with MOSTest publication-title: Nat. Commun. doi: 10.1038/s41467-020-17368-1 – volume: 89 start-page: 227 year: 2021 end-page: 235 ident: CR59 article-title: Genome-wide association analysis of parkinson’s disease and schizophrenia reveals shared genetic architecture and identifies novel risk loci publication-title: Biol. Psychiatry doi: 10.1016/j.biopsych.2020.01.026 – volume: 367 start-page: eaay6690 year: 2020 ident: CR10 article-title: The genetic architecture of the human cerebral cortex publication-title: Science doi: 10.1126/science.aay6690 – volume: 10 start-page: 503 year: 2016 ident: CR18 article-title: Reproducibility and bias in healthy brain segmentation: comparison of two popular neuroimaging platforms publication-title: Front. Neurosci. doi: 10.3389/fnins.2016.00503 – ident: CR28 – volume: 70 start-page: 250 year: 2013 end-page: 257 ident: CR32 article-title: Decoding gender dimorphism of the human brain using multimodal anatomical and diffusion MRI data publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.12.068 – ident: CR41 – volume: 168 start-page: 649 year: 2015 end-page: 659 ident: CR50 article-title: Genome-wide association study of schizophrenia in Ashkenazi Jews publication-title: Am. J. Med. Genet. B Neuropsychiatr. Genet. doi: 10.1002/ajmg.b.32349 – volume: 51 start-page: 237 year: 2019 end-page: 244 ident: CR54 article-title: Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use publication-title: Nat. Genet. doi: 10.1038/s41588-018-0307-5 – volume: 36 start-page: 61 year: 2017 end-page: 78 ident: CR29 article-title: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation publication-title: Med. Image Anal. doi: 10.1016/j.media.2016.10.004 – ident: 6096_CR42 doi: 10.6084/m9.figshare.25203224.v2 – ident: 6096_CR14 doi: 10.1038/s41586-018-0571-7 – volume: 520 start-page: 224 year: 2015 ident: 6096_CR6 publication-title: Nature doi: 10.1038/nature14101 – ident: 6096_CR39 – volume: 50 start-page: 737 year: 2018 ident: 6096_CR44 publication-title: Nat. Genet. doi: 10.1038/s41588-018-0108-x – volume: 562 start-page: 203 year: 2018 ident: 6096_CR75 publication-title: Nature doi: 10.1038/s41586-018-0579-z – volume: 19 start-page: 1569 year: 2016 ident: 6096_CR9 publication-title: Nat. Neurosci. doi: 10.1038/nn.4398 – ident: 6096_CR19 doi: 10.1038/s41598-017-02584-5 – volume: 24 start-page: 17 year: 2020 ident: 6096_CR70 publication-title: IEEE J. Biomed. Health Inf. doi: 10.1109/JBHI.2019.2914970 – volume: 313 start-page: 504 year: 2006 ident: 6096_CR26 publication-title: Science doi: 10.1126/science.1127647 – volume: 2 start-page: 73 year: 2009 ident: 6096_CR3 publication-title: Circ. Cardiovasc. Genet. doi: 10.1161/CIRCGENETICS.108.829747 – volume: 168 start-page: 649 year: 2015 ident: 6096_CR50 publication-title: Am. J. Med. Genet. B Neuropsychiatr. Genet. doi: 10.1002/ajmg.b.32349 – volume: 11 year: 2020 ident: 6096_CR24 publication-title: Nat. Commun. doi: 10.1038/s41467-020-17368-1 – volume: 249 year: 2022 ident: 6096_CR37 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2022.118871 – volume: 244 year: 2021 ident: 6096_CR16 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2021.118603 – ident: 6096_CR31 doi: 10.1109/acpr.2015.7486599 – ident: 6096_CR27 doi: 10.1007/978-3-319-46723-8_49 – volume: 19 start-page: 1523 year: 2016 ident: 6096_CR5 publication-title: Nat. Neurosci. doi: 10.1038/nn.4393 – volume: 15 start-page: 652987 year: 2021 ident: 6096_CR30 publication-title: Front. Neurosci. doi: 10.3389/fnins.2021.652987 – volume: 47 start-page: 1236 year: 2015 ident: 6096_CR78 publication-title: Nat. Genet. doi: 10.1038/ng.3406 – volume: 53 start-page: 1616 year: 2021 ident: 6096_CR77 publication-title: Nat. Genet. doi: 10.1038/s41588-021-00954-4 – ident: 6096_CR40 doi: 10.6084/m9.figshare.25203230.v2 – volume: 89 start-page: 227 year: 2021 ident: 6096_CR59 publication-title: Biol. Psychiatry doi: 10.1016/j.biopsych.2020.01.026 – ident: 6096_CR73 – volume: 24 start-page: 737 year: 2021 ident: 6096_CR15 publication-title: Nat. Neurosci. doi: 10.1038/s41593-021-00826-4 – ident: 6096_CR65 – volume: 115 start-page: 3162 year: 2018 ident: 6096_CR62 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1706100115 – volume: 116 start-page: 3044 year: 2021 ident: 6096_CR52 publication-title: Addiction doi: 10.1111/add.15511 – ident: 6096_CR13 – volume: 51 start-page: 1637 year: 2019 ident: 6096_CR17 publication-title: Nat. Genet. doi: 10.1038/s41588-019-0516-6 – volume: 51 start-page: 1749 year: 2019 ident: 6096_CR76 publication-title: Nat. Genet. doi: 10.1038/s41588-019-0530-8 – volume: 3 start-page: 950 year: 2019 ident: 6096_CR55 publication-title: Nat. Hum. Behav. doi: 10.1038/s41562-019-0653-z – volume: 12 start-page: 399 year: 2018 ident: 6096_CR34 publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2018.00399 – volume: 8 start-page: 153 year: 2014 ident: 6096_CR2 publication-title: Brain Imaging Behav. doi: 10.1007/s11682-013-9269-5 – volume: 10 year: 2020 ident: 6096_CR48 publication-title: Transl. Psychiatry doi: 10.1038/s41398-020-00902-6 – volume: 51 start-page: 1670 year: 2019 ident: 6096_CR49 publication-title: Nat. Genet. doi: 10.1038/s41588-019-0512-x – ident: 6096_CR38 doi: 10.21105/joss.00861 – volume: 47 start-page: 291 year: 2015 ident: 6096_CR43 publication-title: Nat. Genet. doi: 10.1038/ng.3211 – ident: 6096_CR41 doi: 10.6084/m9.figshare.25203233.v2 – volume: 26 start-page: 2190 year: 2010 ident: 6096_CR45 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq340 – volume: 70 start-page: 250 year: 2013 ident: 6096_CR32 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.12.068 – ident: 6096_CR25 doi: 10.1016/j.artmed.2018.08.008 – volume: 10 start-page: 503 year: 2016 ident: 6096_CR18 publication-title: Front. Neurosci. doi: 10.3389/fnins.2016.00503 – volume: 2015 start-page: 450341 year: 2015 ident: 6096_CR20 publication-title: Comput. Math. Methods Med. doi: 10.1155/2015/450341 – volume: 51 start-page: 1193 year: 2019 ident: 6096_CR47 publication-title: Nat. Genet. doi: 10.1038/s41588-019-0450-7 – ident: 6096_CR11 doi: 10.1016/j.neuroimage.2011.09.015 – volume: 68 start-page: 101871 year: 2021 ident: 6096_CR35 publication-title: Med. Image Anal. doi: 10.1016/j.media.2020.101871 – volume: 17 start-page: 825 year: 2002 ident: 6096_CR69 publication-title: Neuroimage doi: 10.1006/nimg.2002.1132 – volume: 610 start-page: 444 year: 2022 ident: 6096_CR68 publication-title: Nature doi: 10.1038/d41586-022-03252-z – volume: 113 start-page: E1968 year: 2016 ident: 6096_CR33 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1523888113 – ident: 6096_CR28 doi: 10.1007/978-3-319-55524-9_14 – ident: 6096_CR57 doi: 10.1038/s41380-017-0001-5 – volume: 11383 start-page: 105 year: 2019 ident: 6096_CR21 publication-title: Brainlesion – volume: 10 year: 2019 ident: 6096_CR36 publication-title: Nat. Commun. doi: 10.1038/s41467-019-13163-9 – ident: 6096_CR56 – volume: 367 start-page: eaay6690 year: 2020 ident: 6096_CR10 publication-title: Science doi: 10.1126/science.aay6690 – ident: 6096_CR22 doi: 10.1109/IEMBS.2011.6091212 – volume: 8 year: 2017 ident: 6096_CR7 publication-title: Nat. Commun. doi: 10.1038/ncomms13624 – volume: 23 start-page: 809 year: 2020 ident: 6096_CR53 publication-title: Nat. Neurosci. doi: 10.1038/s41593-020-0643-5 – volume: 8 start-page: 1 year: 2017 ident: 6096_CR79 publication-title: Nat. Commun. doi: 10.1038/s41467-017-01261-5 – ident: 6096_CR80 doi: 10.6084/m9.figshare.25148744.v2 – volume: 62 start-page: 774 year: 2012 ident: 6096_CR12 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.01.021 – volume: 41 start-page: 210 year: 2010 ident: 6096_CR4 publication-title: Stroke doi: 10.1161/STROKEAHA.109.569194 – volume: 102 start-page: 042119 year: 2020 ident: 6096_CR67 publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.102.042119 – volume: 83 start-page: 155 year: 2006 ident: 6096_CR74 publication-title: Schizophr. Res. doi: 10.1016/j.schres.2005.11.020 – volume: 9 start-page: 658 year: 2020 ident: 6096_CR71 publication-title: J. Clin. Med. Res. – volume: 10 year: 2019 ident: 6096_CR51 publication-title: Nat. Commun. doi: 10.1038/s41467-019-09480-8 – ident: 6096_CR46 doi: 10.1038/s41467-021-23175-z – volume: 25 start-page: 421 year: 2022 ident: 6096_CR63 publication-title: Nat. Neurosci. doi: 10.1038/s41593-022-01042-4 – volume: 51 start-page: 1624 year: 2019 ident: 6096_CR8 publication-title: Nat. Genet. doi: 10.1038/s41588-019-0511-y – volume: 36 start-page: 61 year: 2017 ident: 6096_CR29 publication-title: Med. Image Anal. doi: 10.1016/j.media.2016.10.004 – volume: 51 start-page: 237 year: 2019 ident: 6096_CR54 publication-title: Nat. Genet. doi: 10.1038/s41588-018-0307-5 – volume: 26 start-page: 3943 year: 2021 ident: 6096_CR60 publication-title: Mol. Psychiatry doi: 10.1038/s41380-019-0569-z – volume: 160 start-page: 636 year: 2003 ident: 6096_CR61 publication-title: Am. J. Psychiatry doi: 10.1176/appi.ajp.160.4.636 – volume: 15 start-page: 654315 year: 2021 ident: 6096_CR72 publication-title: Front. Comput. Neurosci. doi: 10.3389/fncom.2021.654315 – volume: 66 start-page: 015006 year: 2021 ident: 6096_CR66 publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/abcd1a – volume: 9 year: 2018 ident: 6096_CR58 publication-title: Nat. Commun. doi: 10.1038/s41467-018-04362-x – ident: 6096_CR64 – volume: 144 start-page: 1038 year: 2021 ident: 6096_CR23 publication-title: Brain J. Neurol. doi: 10.1093/brain/awab140 – volume: 12 start-page: e1001779 year: 2015 ident: 6096_CR1 publication-title: PLoS Med. doi: 10.1371/journal.pmed.1001779 |
| SSID | ssj0001999634 |
| Score | 2.3327925 |
| Snippet | Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain... Abstract Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of... |
| SourceID | doaj proquest pubmed crossref springer |
| SourceType | Open Website Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 414 |
| SubjectTerms | 45/43 59/57 631/114 631/208/205 Biobanks Biomedical and Life Sciences Brain - diagnostic imaging Brain architecture Deep learning Gene loci Genetic Loci Genome-wide association studies Genome-Wide Association Study - methods Genomes Humans Life Sciences Medical imaging Neuroimaging Phenotype Phenotypes Single-nucleotide polymorphism |
| SummonAdditionalLinks | – databaseName: Science Database dbid: M2P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Li9UwFD7oqODG96M6SgR3GiZN0yZZiYqDG4dZODC7kOYxDIzt9fbegfn3nqS59yLqbIRCoU1Lyjkn52vO4wN4y71oHTpmir6op0JHTS3rGUVf5TWX3nViJpuQR0fq9FQflw23qaRVbtbEvFD70aU98gNcapMt8E5-WPykiTUqRVcLhcZNuIXIpk4pXd_48W6PJaH5RpRaGdaog0nkZCx0TJR1iN6p_M0f5bb9f8Oaf8RJs_s5vP-_E38A9wrwJB9nTXkIN8LwCO7MVJRXj-HyZJjWi7RwTMETH8KC5H6Xm9qkgRR-iTMScrnVRFJ22Ji2cEkq7U2poFcEITBBlUyVkcTuJE-mOV2RjJH0iZSCnP_I9EhP4OTwy_fPX2nhZKBOqHZFGxbb4B0CPc6dFNEKKxvdt9Yp_M3W3nZo4EFrrpiNqpfaxVg3XjsbrbSqa57C3jAO4TkQvOxt46XtlRAyctW2PLWzw3Ovom0qqDeSMa40LE-8GRcmB84bZWZpGpSmydI0soJ322cWc7uOa0d_SgLfjkyttvOFcXlmiuWaHr9YoZ-3vA0iOPwjrp1mLATWoyJ3rIL9jdxNsf_J7IRewZvtbbTcFI6xQxjXeYzgiAd5XcGzWc22M0nhWgTHuoL3G73bvfzfH_Ti-rm8hLs8qz4e7T7srZbr8Apuu8vV-bR8nW3nFyKSH94 priority: 102 providerName: ProQuest |
| Title | Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging |
| URI | https://link.springer.com/article/10.1038/s42003-024-06096-7 https://www.ncbi.nlm.nih.gov/pubmed/38580839 https://www.proquest.com/docview/3033763267 https://www.proquest.com/docview/3034243921 https://doaj.org/article/b0f58036a25e4ec1841c900ee0bc6460 |
| Volume | 7 |
| WOSCitedRecordID | wos001197447800004&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: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: DOA dateStart: 20180101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: M~E dateStart: 20180101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: M7P dateStart: 20220101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: BENPR dateStart: 20220101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: PIMPY dateStart: 20220101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: M2P dateStart: 20220101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrR1daxQxcNBWwRfx2631iOCbLs1ls5vk0UqLPvRYxML5FLL5kELdO7p3hf77TpK9a8WvF2HJQjYbksxMZiaZD4C3zPHaImMukRd1JVdBlYZ2tERe5RQTzjY8J5sQs5mcz1V7K9VXtAnL4YHzwh10NNQSt1nDas-9RYVkahWl3tMO-2mStk6FuqVMpdOVKMdXfPSSoZU8GHgyw0KWVNIG5fZS_MSJUsD-30mZv9yQJsZz_AgejhIj-ZBH-hju-P4J3M85JK-ewuVpP6yXkeIH74jzfklSoMqNU1FPxsQQ34lPflIDiWZdi3j2SqJPbrThvCIouxLEpejSSMwNyMiQ7QzJIpAuZpMgZz9SXqNncHp89PXjp3JMplBaLutVWeE6emdRQmPMCh4MN6JSXW2sRP1YOdMgZXqlmKQmyE4oG8K0csqaYISRTfUcdvpF718CwWpnKidMJzkXgcm6ZjEOHb47GUxVwHSzsNqOkcZjwotznW68K6kzMDQCQydgaFHAu-0_yxxn46-tDyO8ti1jjOxUgZijR8zR_8KcAvY30NYj4Q4aOXrccnE-BbzZfkaSi_copveLdWrDGQpybFrAi4wl25HEe1aUalUB7zdoc9P5nye09z8m9AoesITf-NT7sLO6WPvXcM9ers6GiwncFXM5gd3Do1n7ZZLIBcsT1sZSYLnbfj5pv10D6iYXhg |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VAoILb0qggJHgBFG9jhPbB4R4Va1aVj20Um_GsZ2qEk2WzW7R_il-I2Mn2RUCeusBKVKkrBPFm29e9sx8AC-Z47lFw5yiLSpTriqVGlrSFG2VU0w4W_CObEKMx_L4WB2swc-hFiakVQ46MSpq19iwRr6FqjbIAivEu8n3NLBGhd3VgUKjg8WeX_zAkK19u_sJv-8rxrY_H37cSXtWgdRymc_SjFa5dxZdFcas4JXhRmSqzI2VGCgqZwqEqFeKSWoqWQplq2qUOWVNZYSRRYbPvQJXeegsFlIF2cFqTSdEDxnva3NoJrdaHpO_0BCmtMBoIRW_2b9IE_A33_aPfdlo7rZv_29_1B241TvW5H0nCXdhzdf34HpHtbm4D-dHdTufBMXYekec9xMS-3kOtVc16fkzToiP5WQtCdlvTViiJqF0OaS6Lgi6-ARFLlR-ErNCNmm7dEzSVKQMpBvk9CzSPz2Ao0uZ9ENYr5vaPwKCl53JnDCl5FxUTOY5C-368FzKymQJjAYkaNs3ZA-8IN90TAzIpO7QoxE9OqJHiwReL--ZdO1ILhz9IQBsOTK0Eo8XmumJ7jWTLnHGEv0Yw3LPvcWIf2QVpd7TEgW1oAlsDjjTvX5r9QpkCbxY_oyaKWw3mdo38ziGM_R32SiBjQ7WyzcJ29Ho_KsE3gw4Xz383xN6fPG7PIcbO4df9vX-7njvCdxkUezwyDdhfTad-6dwzZ7PTtvpsyi3BL5eNv5_AUQVff4 |
| 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=Unsupervised+deep+representation+learning+enables+phenotype+discovery+for+genetic+association+studies+of+brain+imaging&rft.jtitle=Communications+biology&rft.au=Khush+Patel&rft.au=Ziqian+Xie&rft.au=Hao+Yuan&rft.au=Sheikh+Muhammad+Saiful+Islam&rft.date=2024-04-05&rft.pub=Nature+Portfolio&rft.eissn=2399-3642&rft.volume=7&rft.issue=1&rft.spage=1&rft.epage=14&rft_id=info:doi/10.1038%2Fs42003-024-06096-7&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_b0f58036a25e4ec1841c900ee0bc6460 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2399-3642&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2399-3642&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2399-3642&client=summon |