s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer’s disease solely from structural MRI
Objective To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. Methods A total of 3377 participants’ sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dime...
Uloženo v:
| Vydáno v: | Magma (New York, N.Y.) Ročník 37; číslo 5; s. 845 - 857 |
|---|---|
| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Cham
Springer International Publishing
01.10.2024
|
| Témata: | |
| ISSN: | 1352-8661, 1352-8661 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Objective
To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD.
Methods
A total of 3377 participants’ sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s
2
MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy.
Results
The s
2
MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (
p
< 0.05). Significant associations (
p
< 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation.
Conclusion
The s
2
MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization. |
|---|---|
| AbstractList | Objective
To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD.
Methods
A total of 3377 participants’ sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s
2
MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy.
Results
The s
2
MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (
p
< 0.05). Significant associations (
p
< 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation.
Conclusion
The s
2
MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization. To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD.OBJECTIVETo establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD.A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy.METHODSA total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy.The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation.RESULTSThe s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation.The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.CONCLUSIONThe s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization. |
| Author | Wang, Yi Ouyang, Minhui Li, Honglun Hu, Fang Zhu, Chuanzhen Zheng, Qiang Jiang, Minbo Song, Limei Song, Zhiwei Zhang, Yiyu |
| Author_xml | – sequence: 1 givenname: Zhiwei surname: Song fullname: Song, Zhiwei organization: School of Computer and Control Engineering, Yantai University – sequence: 2 givenname: Honglun surname: Li fullname: Li, Honglun organization: Department of Radiology, Yantai Yuhuangding Hospital Affiliated with Qingdao University Medical College – sequence: 3 givenname: Yiyu surname: Zhang fullname: Zhang, Yiyu organization: School of Computer and Control Engineering, Yantai University – sequence: 4 givenname: Chuanzhen surname: Zhu fullname: Zhu, Chuanzhen organization: School of Computer and Control Engineering, Yantai University – sequence: 5 givenname: Minbo surname: Jiang fullname: Jiang, Minbo organization: School of Computer and Control Engineering, Yantai University – sequence: 6 givenname: Limei surname: Song fullname: Song, Limei organization: School of Medical Imaging, Weifang Medical University – sequence: 7 givenname: Yi surname: Wang fullname: Wang, Yi organization: School of Computer and Control Engineering, Yantai University, Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University – sequence: 8 givenname: Minhui surname: Ouyang fullname: Ouyang, Minhui organization: Department of Radiology, Children’s Hospital of Philadelphia, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 9 givenname: Fang surname: Hu fullname: Hu, Fang organization: Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University – sequence: 10 givenname: Qiang orcidid: 0000-0002-7853-8033 surname: Zheng fullname: Zheng, Qiang email: zhengqiang@ytu.edu.cn organization: School of Computer and Control Engineering, Yantai University |
| BookMark | eNp9kb1uFTEQhS0UJJLAC1C5pFnwz_7c0F2FAJECSAhqy9c7vnHw2suMVyhUvAQFr8eT4JulQBQpLFsz5zua8TlhRyknYOypFM-lEMMLkkLrthGqHimHTaMfsGOpO9Vs-l4e_fN-xE6IboRQshP6mP0k9e7jZbN99R7KS24TD6kAzgjF7iLwEWDmESymkPbco53gW8Yvd6o92nKoXiwuhhFsamplvuYIFSdIpbZzIp4938bv1xAmwN8_fhEfA4El4JQjxNvqmidOBRdXFrSR13kes4feRoInf-9T9vn1xafzt83VhzeX59urxqmuLU3bS-c7qftOOVC98oPwHsQoPHg7KjF43e5UP551nYTeqt1obeu921kvRl27p-zZ6jtj_roAFTMFchCjTZAXMlr0w5nU7UZX6WaVOsxECN64sG5Y0IZopDCHJMyahKlJmLskzAFV_6Ezhsni7f2QXiGq4rQHNDd5wVR_4z7qD-dqopY |
| CitedBy_id | crossref_primary_10_1016_j_bspc_2024_107067 |
| Cites_doi | 10.1371/journal.pone.0068910 10.1212/WNL.52.9.1861 10.1162/jocn.2007.19.9.1498 10.1016/j.neuroimage.2019.116459 10.1093/brain/awaa137 10.3233/JAD-150988 10.1109/TPAMI.2022.3152247 10.1073/pnas.1902376116 10.1016/j.bspc.2022.103565 10.1007/s00330-023-09519-x 10.1148/radiol.2015150220 10.1038/35084005 10.1148/radiol.2021200643 10.1002/jmri.21049 10.1148/radiol.2018180958 10.1148/radiol.2020192541 10.1038/nature06976 10.1016/j.neuroimage.2016.03.067 10.1056/NEJMra0909142 10.3233/JAD-2011-0007 10.1002/advs.202104538 10.1109/TMI.2021.3051604 10.1145/3633518 10.1148/radiol.2015142766 10.1016/j.neubiorev.2016.11.023 10.1016/j.media.2023.102932 10.1002/advs.202000675 10.1016/j.scib.2020.04.003 10.1038/nrn3465 10.1148/radiol.212400 10.1016/j.compbiomed.2022.106043 10.1016/j.jneumeth.2021.109376 10.1016/j.media.2022.102413 10.1016/j.neurobiolaging.2010.04.007 10.1109/MLCCIM55934.2022.00067 10.1109/CVPR.2016.90 10.3389/fpsyg.2018.00386 10.1007/978-3-319-60801-3_27 10.1109/IVCNZ51579.2020.9290616 10.1109/CVPR52688.2022.01186 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 2024. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB). |
| Copyright_xml | – notice: The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: 2024. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB). |
| DBID | AAYXX CITATION 7X8 |
| DOI | 10.1007/s10334-024-01178-3 |
| DatabaseName | CrossRef MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1352-8661 |
| EndPage | 857 |
| ExternalDocumentID | 10_1007_s10334_024_01178_3 |
| GrantInformation_xml | – fundername: Open Project of Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University grantid: YXZN2022002 – fundername: Yantai City Science and Technology Innovation Development Plan grantid: 2023XDRH006 – fundername: National Natural Science Foundation of China grantid: 61802330, ; 61802331 funderid: http://dx.doi.org/10.13039/501100001809 |
| GroupedDBID | --- --K -53 -5E -5G -BR -EM -Y2 -~C .86 .VR 06C 06D 0R~ 0VY 1B1 1N0 1SB 203 28- 29M 29~ 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3SX 3V. 4.4 406 408 409 40D 40E 53G 5GY 5QI 5VS 67Z 6NX 7X7 88E 88I 8FE 8FG 8FH 8FI 8FJ 8FW 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAEDT AAHNG AAIAL AAJBT AAJKR AALRI AANXM AANZL AAQFI AAQXK AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAXUO AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABPLI ABQBU ABQSL ABSXP ABTEG ABTKH ABTMW ABULA ABUWG ABWNU ABWVN ABXPI ACAOD ACBXY ACDTI ACGFS ACGOD ACHSB ACHXU ACIUM ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACRPL ACSNA ACUDM ACZOJ ADBBV ADHHG ADHIR ADINQ ADKNI ADKPE ADMUD ADNMO ADPHR ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEUYN AEVLU AEXYK AFBBN AFEXP AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKMHD AKRWK ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BHPHI BKSAR BPHCQ BSONS BVXVI CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD EMOBN EN4 EPAXT ESBYG F5P FDB FEDTE FERAY FFXSO FGOYB FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA G-Y G-Z GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GRRUI GXS H13 HCIFZ HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW KPH LAK LK5 LLZTM M1P M2P M41 M4Y M7R MA- N2Q NB0 NDZJH NPVJJ NQ- NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9S PCBAR PF0 PQQKQ PROAC PSQYO PT4 PT5 Q2X QOK QOR QOS R2- R4E R89 R9I RHV RIG RNI RNS ROL RPX RPZ RRX RSV RZK S16 S1Z S26 S27 S28 S37 S3B SAP SCLPG SDE SDH SEW SHX SISQX SJYHP SMD SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD SSZ STPWE SV3 SZ9 SZN T13 T16 TSG TSK TSV TT1 TUC U2A U9L UG4 UHS UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WJK WK6 WK8 YLTOR Z45 Z7X Z82 Z83 Z88 Z8R Z8V Z8W ZMTXR ZOVNA ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION 7X8 |
| ID | FETCH-LOGICAL-c254t-461cf513652ce262f70ffe0d0fefad207f34b26d9551e6a2bdaa4ffcbaf0d37f3 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001247228100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1352-8661 |
| IngestDate | Sun Nov 09 13:48:42 EST 2025 Sat Nov 29 03:00:50 EST 2025 Tue Nov 18 22:27:05 EST 2025 Fri Feb 21 02:40:25 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | Deep learning Structural MRI Multi-dimensional representations Alzheimer’s disease Interpretability |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c254t-461cf513652ce262f70ffe0d0fefad207f34b26d9551e6a2bdaa4ffcbaf0d37f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-7853-8033 |
| PQID | 3067913483 |
| PQPubID | 23479 |
| PageCount | 13 |
| ParticipantIDs | proquest_miscellaneous_3067913483 crossref_citationtrail_10_1007_s10334_024_01178_3 crossref_primary_10_1007_s10334_024_01178_3 springer_journals_10_1007_s10334_024_01178_3 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-10-01 |
| PublicationDateYYYYMMDD | 2024-10-01 |
| PublicationDate_xml | – month: 10 year: 2024 text: 2024-10-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Cham |
| PublicationPlace_xml | – name: Cham |
| PublicationSubtitle | Official Journal of the European Society for Magnetic Resonance in Medicine and Biology |
| PublicationTitle | Magma (New York, N.Y.) |
| PublicationTitleAbbrev | Magn Reson Mater Phy |
| PublicationYear | 2024 |
| Publisher | Springer International Publishing |
| Publisher_xml | – name: Springer International Publishing |
| References | Rowe, Ellis, Rimajova, Bourgeat, Pike, Jones, Fripp, Tochon-Danguy, Morandeau, O’Keefe (CR46) 2010; 31 Zhao, Yan, Luo, Zhi, Fu, Du, Yu, Jiang, Calhoun, Sui (CR23) 2022; 78 CR35 CR34 Wang, Wang, Xia, Liao, Evans, He (CR27) 2015; 9 CR33 CR32 Möller, Pijnenburg, van der Flier, Versteeg, Tijms, de Munck, Hafkemeijer, Rombouts, van der Grond, van Swieten (CR44) 2016; 279 Jack, Bernstein, Fox, Thompson, Alexander, Harvey, Borowski, BritsonWhitwell, Ward (CR45) 2008; 27 Zhao, Ding, Han, Fan, Alexander-Bloch, Han, Jin, Liu, Lu, Song (CR7) 2020; 65 CR30 Mehta, Carpenter, Mehta, Haut, Ranjan, Najib, Lockman, Wang, D’haese, Rezai (CR49) 2021; 298 Caso, Agosta, Mattavelli, Migliaccio, Canu, Magnani, Marcone, Copetti, Falautano, Comi (CR43) 2015; 277 Oishi, Mielke, Albert, Lyketsos, Mori (CR3) 2011; 26 Marcus, Wang, Parker, Csernansky, Morris, Buckner (CR48) 2007; 19 Khoshraftar, An (CR22) 2024; 15 Jin, Zhou, Han, Ren, Han, Liu, Lu, Song, Wang, Wang (CR29) 2020; 7 Yao, Sui, Yang, Yap, Shen, Liu (CR14) 2020 Ding, Sohn, Kawczynski, Trivedi, Harnish, Jenkins, Lituiev, Copeland, Aboian, Mari Aparici (CR42) 2019; 290 Brueggen, Grothe, Dyrba, Fellgiebel, Fischer, Filippi, Agosta, Nestor, Meisenzahl, Blautzik (CR47) 2017; 144 Choi, Moon, Kim, Yim, Lee, Moon (CR50) 2022; 304 Zhang, Gordon, Goldberg (CR4) 2017; 72 Bäckman, Andersson, Nyberg, Winblad, Nordberg, Almkvist (CR40) 1999; 52 Zhang, Wang, Phillips, Yang, Yuan (CR41) 2016; 50 Querfurth, Laferla (CR1) 2010; 362 CR19 CR18 CR17 Damulina, Pirpamer, Soellradl, Sackl, Tinauer, Hofer, Enzinger, Gesierich, Duering, Ropele (CR51) 2020; 296 Zhao, Zheng, Dyrba, Rittman, Li, Che, Chen, Sun, Kang, Li (CR13) 2022; 9 CR10 Xia, Wang, He (CR26) 2013; 8 Kong, Zhang, Zhu, Yi, Wang, Zhang (CR36) 2022; 75 Yao, Sui, Wang, Yang, Jiaerken, Luo, Yap, Liu, Shen (CR15) 2021; 40 Wang, Knol, Tiulpin, Dubost, de Bruijne, Vernooij, Adams, Ikram, Niessen, Roshchupkin (CR16) 2019; 116 Logothetis (CR2) 2008; 453 Alexander-Bloch, Giedd, Bullmore (CR11) 2013; 14 Zhao, Zheng, Che, Dyrba, Li, Ding, Zheng, Liu, Li (CR12) 2021; 5 AI-Tam, AI-Hejri, Narangale, Samee, Mahmoud, AI-Masni, AI-Antari (CR31) 2022; 10 CR24 Logothetis, Pauls, Augath, Trinath, Oeltermann (CR6) 2001; 412 CR21 CR20 Rasheed, Qayyum, Ghaly, Al-Fuqaha, Razi, Qadir (CR38) 2022; 149 Liu, Li, Yan, Wang, Ma, Shen, Xu, AsDN (CR5) 2020; 208 Qiu, Joshi, Miller, Xue, Zhou, Karjadi, Chang, Joshi, Dwyer, Zhu (CR39) 2020; 143 Zhang, Li, Zheng (CR28) 2023; 33 Pan, Lei, Shen, Liu, Feng, Wang (CR37) 2021 Zhang, Teng, Liu, Liu, He (CR8) 2022; 365 Han, Wang, Chen, Chen, Guo, Liu, Tang, Xiao, Xu, Xu (CR9) 2022; 45 Zhang, Chen, Shen, Ren, Yu, Yang, Jiang, Shen, Zhou, Zhang (CR25) 2023; 90 CR Jack (1178_CR45) 2008; 27 K Rasheed (1178_CR38) 2022; 149 L Bäckman (1178_CR40) 1999; 52 J Wang (1178_CR27) 2015; 9 CC Rowe (1178_CR46) 2010; 31 S Zhang (1178_CR25) 2023; 90 N Zhang (1178_CR4) 2017; 72 S Khoshraftar (1178_CR22) 2024; 15 F Caso (1178_CR43) 2015; 277 1178_CR30 1178_CR35 1178_CR34 JD Choi (1178_CR50) 2022; 304 1178_CR33 1178_CR32 DS Marcus (1178_CR48) 2007; 19 D Jin (1178_CR29) 2020; 7 J Pan (1178_CR37) 2021 M Xia (1178_CR26) 2013; 8 D Yao (1178_CR14) 2020 Z Kong (1178_CR36) 2022; 75 A Alexander-Bloch (1178_CR11) 2013; 14 Y Zhang (1178_CR28) 2023; 33 1178_CR17 Y Zhang (1178_CR41) 2016; 50 C Möller (1178_CR44) 2016; 279 RM AI-Tam (1178_CR31) 2022; 10 1178_CR19 1178_CR18 K Zhao (1178_CR7) 2020; 65 A Damulina (1178_CR51) 2020; 296 Y Zhang (1178_CR8) 2022; 365 K Oishi (1178_CR3) 2011; 26 S Qiu (1178_CR39) 2020; 143 K Han (1178_CR9) 2022; 45 K Zhao (1178_CR12) 2021; 5 Y Ding (1178_CR42) 2019; 290 NK Logothetis (1178_CR6) 2001; 412 M Liu (1178_CR5) 2020; 208 RI Mehta (1178_CR49) 2021; 298 D Yao (1178_CR15) 2021; 40 1178_CR10 K Brueggen (1178_CR47) 2017; 144 K Zhao (1178_CR13) 2022; 9 NK Logothetis (1178_CR2) 2008; 453 M Zhao (1178_CR23) 2022; 78 HW Querfurth (1178_CR1) 2010; 362 1178_CR20 J Wang (1178_CR16) 2019; 116 1178_CR24 1178_CR21 |
| References_xml | – volume: 8 start-page: e68910 issue: 7 year: 2013 ident: CR26 article-title: BrainNet Viewer: a network visualization tool for human brain connectomics publication-title: PLoS ONE doi: 10.1371/journal.pone.0068910 – volume: 52 start-page: 1861 issue: 9 year: 1999 end-page: 1861 ident: CR40 article-title: Brain regions associated with episodic retrieval in normal aging and Alzheimer’s disease publication-title: Neurology doi: 10.1212/WNL.52.9.1861 – volume: 19 start-page: 1498 issue: 9 year: 2007 end-page: 1507 ident: CR48 article-title: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults publication-title: J Cogn Neurosci doi: 10.1162/jocn.2007.19.9.1498 – volume: 208 year: 2020 ident: CR5 article-title: A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.116459 – volume: 143 start-page: 1920 issue: 6 year: 2020 end-page: 1933 ident: CR39 article-title: Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification publication-title: Brain doi: 10.1093/brain/awaa137 – volume: 10 start-page: 1971 issue: 11 year: 2022 ident: CR31 article-title: A hybrid workflow of residual convolutional transformer encoder for breast cancer classificationi using digital X-ray mammograms publication-title: Biomedicines – ident: CR35 – volume: 50 start-page: 1163 issue: 4 year: 2016 end-page: 1179 ident: CR41 article-title: Three-dimensional eigenbrain for the detection of subjects and brain regions related with Alzheimer’s disease publication-title: J Alzheimers Dis doi: 10.3233/JAD-150988 – volume: 45 start-page: 87 issue: 1 year: 2022 end-page: 110 ident: CR9 article-title: A survey on vision transformer publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2022.3152247 – volume: 116 start-page: 21213 issue: 42 year: 2019 end-page: 21218 ident: CR16 article-title: Gray matter age prediction as a biomarker for risk of dementia publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.1902376116 – ident: CR21 – volume: 75 start-page: 103565 year: 2022 ident: CR36 article-title: Multi-modal data Alzheimer’s disease detection based on 3D convolution publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2022.103565 – ident: CR19 – volume: 33 start-page: 5385 issue: 8 year: 2023 end-page: 5397 ident: CR28 article-title: A comprehensive characterization of hippocampal feature ensemble serves as individualized brain signature for Alzheimer’s disease: deep learning analysis in 3238 participants worldwide publication-title: Eur Radiol doi: 10.1007/s00330-023-09519-x – volume: 279 start-page: 838 issue: 3 year: 2016 end-page: 848 ident: CR44 article-title: Alzheimer disease and behavioral variant frontotemporal dementia: automatic classification based on cortical atrophy for single-subject diagnosis publication-title: Radiology doi: 10.1148/radiol.2015150220 – volume: 412 start-page: 150 issue: 6843 year: 2001 end-page: 157 ident: CR6 article-title: Neurophysiological investigation of the basis of the fMRI signal publication-title: Nature doi: 10.1038/35084005 – volume: 298 start-page: 654 issue: 3 year: 2021 end-page: 662 ident: CR49 article-title: Blood-brain barrier opening with MRI-guided focused ultrasound elicits meningeal venous permeability in humans with early Alzheimer disease publication-title: Radiology doi: 10.1148/radiol.2021200643 – start-page: 1 year: 2020 end-page: 10 ident: CR14 article-title: Temporal-adaptive graph convolutional network for automated identification of major depressive disorder using resting-state fMRI publication-title: International workshop on machine learning in medical imaging – ident: CR32 – volume: 27 start-page: 685 issue: 4 year: 2008 end-page: 691 ident: CR45 article-title: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods publication-title: J Magn Reson Imaging doi: 10.1002/jmri.21049 – volume: 5 start-page: 783 issue: 3 year: 2021 end-page: 797 ident: CR12 article-title: Regional radiomics similarity networks (R2SNs) in the human brain: reproducibility, small-world properties and a biological basis publication-title: Netw Neurosci – volume: 290 start-page: 456 issue: 2 year: 2019 end-page: 464 ident: CR42 article-title: A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain publication-title: Radiology doi: 10.1148/radiol.2018180958 – volume: 296 start-page: 619 issue: 3 year: 2020 end-page: 626 ident: CR51 article-title: Cross-sectional and longitudinal assessment of brain iron level in Alzheimer disease using 3-T MRI publication-title: Radiology doi: 10.1148/radiol.2020192541 – volume: 453 start-page: 869 issue: 7197 year: 2008 end-page: 878 ident: CR2 article-title: What we can do and what we cannot do with fMRI publication-title: Nature doi: 10.1038/nature06976 – ident: CR18 – start-page: 467 year: 2021 end-page: 478 ident: CR37 article-title: Characterization multimodal connectivity of brain network by hypergraph GAN for Alzheimer’s disease analysis publication-title: Chinese conference on pattern recognition and computer vision (PRCV) – volume: 144 start-page: 305 year: 2017 end-page: 308 ident: CR47 article-title: The European DTI study on dementia—a multicenter DTI and MRI study on Alzheimer’s disease and mild cognitive impairment publication-title: Neuroimage doi: 10.1016/j.neuroimage.2016.03.067 – volume: 362 start-page: 329 issue: 4 year: 2010 ident: CR1 article-title: Alzheimer’s disease publication-title: N Engl J Med doi: 10.1056/NEJMra0909142 – volume: 26 start-page: 287 issue: s3 year: 2011 end-page: 296 ident: CR3 article-title: DTI analyses and clinical applications in Alzheimer’s disease publication-title: J Alzheimers Dis doi: 10.3233/JAD-2011-0007 – ident: CR30 – volume: 9 start-page: 2104538 issue: 12 year: 2022 ident: CR13 article-title: Regional radiomics similarity networks reveal distinct subtypes and abnormality patterns in mild cognitive impairment publication-title: Adv Sci doi: 10.1002/advs.202104538 – ident: CR10 – volume: 40 start-page: 1279 issue: 4 year: 2021 end-page: 1289 ident: CR15 article-title: A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2021.3051604 – ident: CR33 – volume: 15 start-page: 1 issue: 1 year: 2024 end-page: 55 ident: CR22 article-title: A survey on graph representation learning methods publication-title: ACM Trans Intell Syst Technol doi: 10.1145/3633518 – volume: 9 start-page: 386 year: 2015 ident: CR27 article-title: GRETNA: a graph theoretical network analysis toolbox for imaging connectomics publication-title: Front Hum Neurosci – volume: 277 start-page: 162 issue: 1 year: 2015 end-page: 172 ident: CR43 article-title: White matter degeneration in atypical Alzheimer disease publication-title: Radiology doi: 10.1148/radiol.2015142766 – volume: 72 start-page: 168 year: 2017 end-page: 175 ident: CR4 article-title: Cerebral blood flow measured by arterial spin labeling MRI at resting state in normal aging and Alzheimer’s disease publication-title: Neurosci Biobehav Rev doi: 10.1016/j.neubiorev.2016.11.023 – volume: 90 start-page: 102932 year: 2023 ident: CR25 article-title: A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders publication-title: Med Image Anal doi: 10.1016/j.media.2023.102932 – volume: 7 start-page: 2000675 issue: 14 year: 2020 ident: CR29 article-title: Generalizable, reproducible, and neuroscientifically interpretable imaging biomarkers for Alzheimer’s disease publication-title: Adv Sci doi: 10.1002/advs.202000675 – volume: 65 start-page: 1103 issue: 13 year: 2020 end-page: 1113 ident: CR7 article-title: Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease: diagnosis, longitudinal progress and biological basis publication-title: Sci Bull doi: 10.1016/j.scib.2020.04.003 – volume: 14 start-page: 322 issue: 5 year: 2013 end-page: 336 ident: CR11 article-title: Imaging structural co-variance between human brain regions publication-title: Nat Rev Neurosci doi: 10.1038/nrn3465 – ident: CR17 – volume: 304 start-page: 635 issue: 3 year: 2022 end-page: 645 ident: CR50 article-title: Choroid plexus volume and permeability at brain MRI within the Alzheimer disease clinical spectrum publication-title: Radiology doi: 10.1148/radiol.212400 – volume: 149 start-page: 106043 year: 2022 ident: CR38 article-title: Explainable, trustworthy, and ethical machine learning for healthcare: a survey publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2022.106043 – volume: 365 year: 2022 ident: CR8 article-title: Diagnosis of Alzheimer’s disease based on regional attention with sMRI gray matter slices publication-title: J Neurosci Method doi: 10.1016/j.jneumeth.2021.109376 – volume: 78 start-page: 102413 year: 2022 ident: CR23 article-title: An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data publication-title: Med Image Anal doi: 10.1016/j.media.2022.102413 – ident: CR34 – ident: CR24 – volume: 31 start-page: 1275 issue: 8 year: 2010 end-page: 1283 ident: CR46 article-title: Amyloid imaging results from the Australian imaging, biomarkers and lifestyle (AIBL) study of aging publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2010.04.007 – ident: CR20 – volume: 277 start-page: 162 issue: 1 year: 2015 ident: 1178_CR43 publication-title: Radiology doi: 10.1148/radiol.2015142766 – volume: 149 start-page: 106043 year: 2022 ident: 1178_CR38 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2022.106043 – ident: 1178_CR20 – volume: 14 start-page: 322 issue: 5 year: 2013 ident: 1178_CR11 publication-title: Nat Rev Neurosci doi: 10.1038/nrn3465 – volume: 412 start-page: 150 issue: 6843 year: 2001 ident: 1178_CR6 publication-title: Nature doi: 10.1038/35084005 – volume: 75 start-page: 103565 year: 2022 ident: 1178_CR36 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2022.103565 – volume: 45 start-page: 87 issue: 1 year: 2022 ident: 1178_CR9 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2022.3152247 – volume: 362 start-page: 329 issue: 4 year: 2010 ident: 1178_CR1 publication-title: N Engl J Med doi: 10.1056/NEJMra0909142 – volume: 116 start-page: 21213 issue: 42 year: 2019 ident: 1178_CR16 publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.1902376116 – ident: 1178_CR34 – volume: 7 start-page: 2000675 issue: 14 year: 2020 ident: 1178_CR29 publication-title: Adv Sci doi: 10.1002/advs.202000675 – ident: 1178_CR21 – volume: 78 start-page: 102413 year: 2022 ident: 1178_CR23 publication-title: Med Image Anal doi: 10.1016/j.media.2022.102413 – volume: 296 start-page: 619 issue: 3 year: 2020 ident: 1178_CR51 publication-title: Radiology doi: 10.1148/radiol.2020192541 – volume: 90 start-page: 102932 year: 2023 ident: 1178_CR25 publication-title: Med Image Anal doi: 10.1016/j.media.2023.102932 – volume: 27 start-page: 685 issue: 4 year: 2008 ident: 1178_CR45 publication-title: J Magn Reson Imaging doi: 10.1002/jmri.21049 – volume: 52 start-page: 1861 issue: 9 year: 1999 ident: 1178_CR40 publication-title: Neurology doi: 10.1212/WNL.52.9.1861 – volume: 33 start-page: 5385 issue: 8 year: 2023 ident: 1178_CR28 publication-title: Eur Radiol doi: 10.1007/s00330-023-09519-x – volume: 72 start-page: 168 year: 2017 ident: 1178_CR4 publication-title: Neurosci Biobehav Rev doi: 10.1016/j.neubiorev.2016.11.023 – volume: 8 start-page: e68910 issue: 7 year: 2013 ident: 1178_CR26 publication-title: PLoS ONE doi: 10.1371/journal.pone.0068910 – ident: 1178_CR18 – ident: 1178_CR35 – volume: 26 start-page: 287 issue: s3 year: 2011 ident: 1178_CR3 publication-title: J Alzheimers Dis doi: 10.3233/JAD-2011-0007 – ident: 1178_CR10 – start-page: 1 volume-title: International workshop on machine learning in medical imaging year: 2020 ident: 1178_CR14 – volume: 453 start-page: 869 issue: 7197 year: 2008 ident: 1178_CR2 publication-title: Nature doi: 10.1038/nature06976 – ident: 1178_CR24 doi: 10.1109/MLCCIM55934.2022.00067 – volume: 19 start-page: 1498 issue: 9 year: 2007 ident: 1178_CR48 publication-title: J Cogn Neurosci doi: 10.1162/jocn.2007.19.9.1498 – volume: 65 start-page: 1103 issue: 13 year: 2020 ident: 1178_CR7 publication-title: Sci Bull doi: 10.1016/j.scib.2020.04.003 – volume: 298 start-page: 654 issue: 3 year: 2021 ident: 1178_CR49 publication-title: Radiology doi: 10.1148/radiol.2021200643 – volume: 9 start-page: 2104538 issue: 12 year: 2022 ident: 1178_CR13 publication-title: Adv Sci doi: 10.1002/advs.202104538 – ident: 1178_CR17 doi: 10.1109/CVPR.2016.90 – volume: 144 start-page: 305 year: 2017 ident: 1178_CR47 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2016.03.067 – volume: 50 start-page: 1163 issue: 4 year: 2016 ident: 1178_CR41 publication-title: J Alzheimers Dis doi: 10.3233/JAD-150988 – volume: 10 start-page: 1971 issue: 11 year: 2022 ident: 1178_CR31 publication-title: Biomedicines – volume: 40 start-page: 1279 issue: 4 year: 2021 ident: 1178_CR15 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2021.3051604 – volume: 5 start-page: 783 issue: 3 year: 2021 ident: 1178_CR12 publication-title: Netw Neurosci – volume: 9 start-page: 386 year: 2015 ident: 1178_CR27 publication-title: Front Hum Neurosci doi: 10.3389/fpsyg.2018.00386 – volume: 279 start-page: 838 issue: 3 year: 2016 ident: 1178_CR44 publication-title: Radiology doi: 10.1148/radiol.2015150220 – ident: 1178_CR32 – volume: 15 start-page: 1 issue: 1 year: 2024 ident: 1178_CR22 publication-title: ACM Trans Intell Syst Technol doi: 10.1145/3633518 – volume: 31 start-page: 1275 issue: 8 year: 2010 ident: 1178_CR46 publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2010.04.007 – ident: 1178_CR33 doi: 10.1007/978-3-319-60801-3_27 – volume: 365 year: 2022 ident: 1178_CR8 publication-title: J Neurosci Method doi: 10.1016/j.jneumeth.2021.109376 – ident: 1178_CR30 doi: 10.1109/IVCNZ51579.2020.9290616 – volume: 208 year: 2020 ident: 1178_CR5 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.116459 – volume: 304 start-page: 635 issue: 3 year: 2022 ident: 1178_CR50 publication-title: Radiology doi: 10.1148/radiol.212400 – ident: 1178_CR19 doi: 10.1109/CVPR52688.2022.01186 – start-page: 467 volume-title: Chinese conference on pattern recognition and computer vision (PRCV) year: 2021 ident: 1178_CR37 – volume: 143 start-page: 1920 issue: 6 year: 2020 ident: 1178_CR39 publication-title: Brain doi: 10.1093/brain/awaa137 – volume: 290 start-page: 456 issue: 2 year: 2019 ident: 1178_CR42 publication-title: Radiology doi: 10.1148/radiol.2018180958 |
| SSID | ssj0021503 |
| Score | 2.392644 |
| Snippet | Objective
To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD.
Methods
A total of 3377 participants’ sMRI... To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD.OBJECTIVETo establish a multi-dimensional... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 845 |
| SubjectTerms | Basic Science - Reconstruction algorithms and artificial intelligence Biomedical Engineering and Bioengineering Computer Appl. in Life Sciences Health Informatics Imaging Medicine Medicine & Public Health Radiology Research Article Solid State Physics |
| Title | s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer’s disease solely from structural MRI |
| URI | https://link.springer.com/article/10.1007/s10334-024-01178-3 https://www.proquest.com/docview/3067913483 |
| Volume | 37 |
| WOSCitedRecordID | wos001247228100001&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: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1352-8661 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0021503 issn: 1352-8661 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LSwMxEA5aRbz4FuuLCN40sJvdbna9FW3RQ4v4orclm0y0sG7FbQU9-Sc8-Pf8JSbbbKsiBb3uTkLITObBzHyD0L4LEY9ExEjAfUqMhJCIuZRIJhJXJokOyUQxbIK122GnE53bprC8rHYvU5KFpv7S7OZ5PtE2hRgcs5B402hGm7vQDGy4uLwZhVnaxfFse8zv676boLFf-SMVWliY5uL_zraEFqxHietDEVhGU5CtoLmWzZmvorecti7OSP2kDf0jzDPcHdUZJilgCfCA7eyIW6zKWi1c4kiYr42BSLsSeEYKfGtcIGGWXUtZjnsK19OXO-jew-PH63uObdoHa8GG9BmbHhY8RKo1KB9Yn2cNXTcbV8enxA5jIELHkH3iB65QNVMURwXQgCrmKAWOdBQoLqnDlOcnNJCRdsEg4DSRnPtKiYQrR3r67zqqZL0MNhCugZvwSNUYr3k-Z6ZVlmnHByKtb7S6lVXklvyJhUUqNwMz0niMsWzuO9b3HRf3HXtVdDBa8zDE6ZhIvVeyPdbPyeRIeAa9QR6bCMoUI4Sa5rDkdWzfdT5hy82_kW-heVqIiykl3EYVzQLYQbPiqd_NH3fRNOuEu4VYfwL2RPWV |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8QwEA6-UC--xfUZwZsG2rTbbL0tuqLoLuILbyFNJrpQu2JXQU_-CQ_-PX-JSTddH4ig13YSQmYyD2bmG4Q2fIhFLGNGIhFSYiWExMynRDGZ-CpJTEgmi2ETrNWqXV7Gx64pLC-r3cuUZKGpPzW7BUFIjE0hFsesRoJBNBwai2UR809OL_phlnFxAtce8_O6rybow6_8lgotLMze5P_ONoUmnEeJ6z0RmEYDkM2g0abLmc-il5w2Tw5IfbcF3W0sMtzu1xkmKWAFcIvd7IgrrMtaLVziSNivjXuZthWIjBT41rhAwiy7lrIcdzSup0_X0L6Bu7fn1xy7tA82gg3pI7Y9LLiHVGtRPrA5zxw632uc7ewTN4yBSBNDdkkY-VJXbVEclUAjqpmnNXjK06CFoh7TQZjQSMXGBYNI0EQJEWotE6E9FZi_82go62SwgHAV_ETEuspENQgFs62yzDg-EBt9Y9StqiC_5A-XDqncDsxI-QfGsr1vbu6bF_fNgwra7K-57eF0_Eq9XrKdm-dkcyQig859zm0EZYsRaoZmq-Q1d-86_2XLxb-Rr6Gx_bPmET86aB0uoXFqRacoEVxGQ4YdsIJG5EO3nd-tFsL9Drel94o |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFA7eEF-8i_MawTcNtmnXrL4NdSi6Id7wLaTJiQ5qN9ZN0Cf_hA_-PX-JSdfOKSKIr20SQnKSnMP5vu8gtO1CKEIZMhIInxJrISRkLiWKychVUWRCMpkVm2CNRuX2NjwfYvFnaPciJdnnNFiVpqS711Z6b4j45nk-Me8LsZpmFeKNonHfAultvH55Mwi5jLvj5VSZn_t9fY4-fcxvadHstanN_H-es2g69zRxtW8ac2gEknk0Wc9z6QvoNaX1ixNSPWxAdx-LBDcH-MMoBqwA2jivKXGHdYHhwoW-hP161JNxU4FISKZ7jTOFzILNlKS4pXE1fr6H5gN03l_eUpyng7AxeIifsOW24L6CrVX_wGY-i-i6dnR1cEzyIg1EmtiyS_zAlbpswXJUAg2oZo7W4ChHgxaKOkx7fkQDFRrXDAJBIyWEr7WMhHaUZ_4uobGklcAywmVwIxHqMhNlzxfMUmiZcYggNPeQuYZVCbnFXnGZK5jbQhox_9RetuvNzXrzbL25V0I7gz7tvn7Hr623ChPg5pjZ3IlIoNVLuY2sLEihYtrsFvvO8_Oe_jLkyt-ab6LJ88MaPztpnK6iKWotJ0MOrqExsxuwjibkY7eZdjYyO_8AGgUAfQ |
| 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=s2MRI-ADNet%3A+an+interpretable+deep+learning+framework+integrating+Euclidean-graph+representations+of+Alzheimer%E2%80%99s+disease+solely+from+structural+MRI&rft.jtitle=Magma+%28New+York%2C+N.Y.%29&rft.au=Song%2C+Zhiwei&rft.au=Li%2C+Honglun&rft.au=Zhang%2C+Yiyu&rft.au=Zhu%2C+Chuanzhen&rft.date=2024-10-01&rft.issn=1352-8661&rft.eissn=1352-8661&rft.volume=37&rft.issue=5&rft.spage=845&rft.epage=857&rft_id=info:doi/10.1007%2Fs10334-024-01178-3&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10334_024_01178_3 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1352-8661&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1352-8661&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1352-8661&client=summon |