MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
•A multi-channel model based on recurrent variational autoencoders was proposed to capture spatial and temporal evolution of AD using multimodal data.•Proposed model was evaluated on synthetic and real datasets.•Model outperforms a set of baselines for missing data reconstruction across modalities....
Gespeichert in:
| Veröffentlicht in: | NeuroImage (Orlando, Fla.) Jg. 268; S. 119892 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
Elsevier Inc
01.03.2023
Elsevier Limited Elsevier |
| Schlagworte: | |
| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •A multi-channel model based on recurrent variational autoencoders was proposed to capture spatial and temporal evolution of AD using multimodal data.•Proposed model was evaluated on synthetic and real datasets.•Model outperforms a set of baselines for missing data reconstruction across modalities.
[Display omitted]
The progression of neurodegenerative diseases, such as Alzheimer’s Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N=897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores. |
|---|---|
| AbstractList | The progression of neurodegenerative diseases, such as Alzheimer’s Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N=897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores. •A multi-channel model based on recurrent variational autoencoders was proposed to capture spatial and temporal evolution of AD using multimodal data.•Proposed model was evaluated on synthetic and real datasets.•Model outperforms a set of baselines for missing data reconstruction across modalities. [Display omitted] The progression of neurodegenerative diseases, such as Alzheimer’s Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N=897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores. The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N=897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores.The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N=897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores. |
| ArticleNumber | 119892 |
| Author | Martí-Juan, Gerard Piella, Gemma Lorenzi, Marco |
| Author_xml | – sequence: 1 givenname: Gerard orcidid: 0000-0003-4729-7182 surname: Martí-Juan fullname: Martí-Juan, Gerard email: gerard.marti@upf.edu organization: BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain – sequence: 2 givenname: Marco surname: Lorenzi fullname: Lorenzi, Marco organization: Université Côte d’Azur, Inria Sophia Antipolis, Epione Research Project, France – sequence: 3 givenname: Gemma surname: Piella fullname: Piella, Gemma organization: BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36682509$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNks1u1DAUhSPUiv7AK6BIbNhk8E-cxCwQw6jQSq2QELC1buybqUMSFzupVFa8Bq_Hk-A0LUizGilKrPi759r3nJPkYHADJklKyYoSWrxuVwNO3tketrhihPEVpbKS7ElyTIkUmRQlO5jXgmdV3DpKTkJoCSGS5tXT5IgXRcUEkcdJuNpkn7-tz96kV1M32kxfwzBgl3rUk_c4jOkteAujdQN0KUyjw0E7gz5tnE_7uaZ3Jm6tu5_XaHv0f379DqmxASFgeuPd1mMIsTyNHHadHbbPksMGuoDPH76nydcPZ18259nlp48Xm_VlpkUpxqyW2BQEJcg8r8ucsIaWgA0HBlLmRtZGCFNwWgPhDSE1ZZHXOD8FLXTJT5OLRdc4aNWNj-Pyd8qBVfc_nN8q8KPVHSrMBeWEowDW5ACsrg2H2FggbQw2ddR6tWjFG_2YMIyqt0HH-8CAbgqKlUVV0fkV0Zc7aOsmH8c3UyUvCaEVidSLB2qqezT_jvdoTQTeLoD2LgSPjdJ2vDdi9GA7RYmas6Ba9T8Las6CWrIQBaodgccee5S-X0ox2nNr0augbTQejY3BGOP87D4i73ZEdHTfaui-491-En8B0pztpA |
| CitedBy_id | crossref_primary_10_1145_3659575 crossref_primary_10_1016_j_apsb_2025_07_030 crossref_primary_10_2196_59792 crossref_primary_10_3390_bioengineering10101120 crossref_primary_10_1016_j_neuroimage_2023_120485 crossref_primary_10_1016_j_cosrev_2024_100720 crossref_primary_10_1038_s41583_023_00779_6 crossref_primary_10_70401_ec_2025_0010 crossref_primary_10_1016_j_cmpb_2024_108259 crossref_primary_10_1016_j_eswa_2025_128580 crossref_primary_10_1002_hsr2_70802 crossref_primary_10_1016_j_neuroimage_2024_120737 crossref_primary_10_1016_j_eswa_2024_124780 crossref_primary_10_1109_JBHI_2024_3472462 crossref_primary_10_1016_j_knosys_2025_114197 crossref_primary_10_1109_TNSRE_2025_3549730 crossref_primary_10_1177_25424823241290694 crossref_primary_10_3348_kjr_2025_0073 |
| Cites_doi | 10.1097/WCO.0000000000000460 10.1093/brain/awu176 10.1016/j.neuroimage.2013.02.059 10.1016/j.patcog.2020.107247 10.1136/jnnp.71.4.441 10.1109/TNNLS.2014.2376974 10.1016/S1474-4422(09)70299-6 10.1002/hbm.24682 10.1016/j.neuroimage.2012.01.062 10.1038/s41598-019-49656-2 10.1016/j.neuroimage.2012.06.061 10.1109/TNNLS.2016.2520964 10.1038/s41467-018-05892-0 10.1016/j.neuroimage.2017.08.059 10.1093/brain/awx194 10.1016/j.neucom.2020.05.087 10.1177/0962280212445834 10.1016/j.neuroimage.2010.07.020 10.1162/0899766042321814 10.1016/j.neuroimage.2012.02.084 10.1002/ana.21610 10.1016/j.matcom.2009.01.023 10.1038/s41598-021-82098-3 10.1016/j.neuroimage.2010.07.002 10.1111/ene.13439 10.1371/journal.pone.0211558 10.1016/j.nic.2005.09.008 10.1038/s41598-018-27337-w 10.1016/j.media.2019.01.004 10.1162/neco.1997.9.8.1735 10.1016/j.neuroimage.2016.04.023 10.1016/j.cmpb.2020.105348 10.1136/jnnp-2014-309105 |
| ContentType | Journal Article |
| Copyright | 2023 Copyright © 2023. Published by Elsevier Inc. Copyright Elsevier Limited Mar 2023 |
| Copyright_xml | – notice: 2023 – notice: Copyright © 2023. Published by Elsevier Inc. – notice: Copyright Elsevier Limited Mar 2023 |
| CorporateAuthor | Alzheimer’s Disease Neuroimaging Initiative |
| CorporateAuthor_xml | – name: Alzheimer’s Disease Neuroimaging Initiative |
| DBID | 6I. AAFTH AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7TK 7X7 7XB 88E 88G 8AO 8FD 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2M M7P P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ Q9U RC3 7X8 DOA |
| DOI | 10.1016/j.neuroimage.2023.119892 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Neurosciences Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Psychology Database (Alumni) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest : Biological Science Collection journals [unlimited simultaneous users] ProQuest Central Natural Science Collection ProQuest One ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection Medical Database Psychology Database Biological Science Database Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology ProQuest Central Basic Genetics Abstracts MEDLINE - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest One Psychology ProQuest Central Student Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Genetics Abstracts Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Biological Science Collection ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Psychology Journals (Alumni) Biological Science Database ProQuest SciTech Collection Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest Psychology Journals ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE ProQuest One Psychology |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1095-9572 |
| ExternalDocumentID | oai_doaj_org_article_e451303e5a2f4aa2bbd3aa945e1fdefb 36682509 10_1016_j_neuroimage_2023_119892 S1053811923000411 |
| Genre | Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NIA NIH HHS grantid: U01 AG024904 |
| GroupedDBID | --- --K --M .1- .FO .~1 0R~ 123 1B1 1RT 1~. 1~5 29N 4.4 457 4G. 53G 5RE 5VS 7-5 71M 7X7 88E 8AO 8FE 8FH 8FI 8FJ 8P~ 9JM AABNK AAEDT AAEDW AAFWJ AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXLA AAXUO AAYWO ABBQC ABCQJ ABFNM ABFRF ABIVO ABJNI ABMAC ABMZM ABUWG ABXDB ACDAQ ACGFO ACGFS ACIEU ACLOT ACPRK ACRLP ACRPL ACVFH ADBBV ADCNI ADEZE ADFGL ADFRT ADMUD ADNMO ADVLN ADXHL AEBSH AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFKRA AFPKN AFPUW AFRHN AFTJW AFXIZ AGHFR AGQPQ AGUBO AGWIK AGYEJ AHHHB AHMBA AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRLJ AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX APXCP ASPBG AVWKF AXJTR AZFZN AZQEC BBNVY BENPR BHPHI BKOJK BLXMC BNPGV BPHCQ BVXVI CAG CCPQU COF CS3 DM4 DU5 DWQXO EBS EFBJH EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN FYUFA G-2 G-Q GBLVA GNUQQ GROUPED_DOAJ HCIFZ HDW HEI HMCUK HMK HMO HMQ HVGLF HZ~ IHE J1W KOM LG5 LK8 LX8 M1P M29 M2M M2V M41 M7P MO0 MOBAO N9A O-L O9- OAUVE OK1 OVD OZT P-8 P-9 P2P PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PSYQQ Q38 R2- ROL RPZ SAE SCC SDF SDG SDP SES SEW SNS SSH SSN SSZ T5K TEORI UKHRP UV1 WUQ XPP YK3 Z5R ZMT ZU3 ~G- ~HD 3V. 6I. AACTN AADPK AAFTH AAIAV ABLVK ABYKQ AFKWA AJBFU AJOXV AMFUW C45 LCYCR NCXOZ RIG ZA5 9DU AAYXX AFFHD CITATION AGCQF AGRNS ALIPV CGR CUY CVF ECM EIF NPM 7TK 7XB 8FD 8FK FR3 K9. P64 PKEHL PQEST PQUKI PRINS Q9U RC3 7X8 PUEGO |
| ID | FETCH-LOGICAL-c575t-b9ef60e9a944b7402f17aef3a2a994d9bd55d631ba03f00b12ef6ce6ce6616c73 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 21 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000934066200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1053-8119 1095-9572 |
| IngestDate | Fri Oct 03 12:39:03 EDT 2025 Thu Oct 02 10:14:35 EDT 2025 Tue Oct 07 07:25:22 EDT 2025 Mon Jul 21 06:01:19 EDT 2025 Tue Nov 18 20:40:26 EST 2025 Sat Nov 29 07:09:21 EST 2025 Fri Feb 23 02:38:38 EST 2024 Tue Oct 14 19:35:53 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Variational autoencoder Longitudinal Alzheimer’s disease Multimodal Recurrent neural network Disease progression modelling |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2023. Published by Elsevier Inc. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c575t-b9ef60e9a944b7402f17aef3a2a994d9bd55d631ba03f00b12ef6ce6ce6616c73 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-4729-7182 |
| OpenAccessLink | https://doaj.org/article/e451303e5a2f4aa2bbd3aa945e1fdefb |
| PMID | 36682509 |
| PQID | 2773700180 |
| PQPubID | 2031077 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_e451303e5a2f4aa2bbd3aa945e1fdefb proquest_miscellaneous_2768817688 proquest_journals_2773700180 pubmed_primary_36682509 crossref_citationtrail_10_1016_j_neuroimage_2023_119892 crossref_primary_10_1016_j_neuroimage_2023_119892 elsevier_sciencedirect_doi_10_1016_j_neuroimage_2023_119892 elsevier_clinicalkey_doi_10_1016_j_neuroimage_2023_119892 |
| PublicationCentury | 2000 |
| PublicationDate | March 2023 2023-03-00 20230301 2023-03-01 |
| PublicationDateYYYYMMDD | 2023-03-01 |
| PublicationDate_xml | – month: 03 year: 2023 text: March 2023 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Amsterdam |
| PublicationTitle | NeuroImage (Orlando, Fla.) |
| PublicationTitleAlternate | Neuroimage |
| PublicationYear | 2023 |
| Publisher | Elsevier Inc Elsevier Limited Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier Limited – name: Elsevier |
| References | Jack, Knopman, Jagust, Shaw, Aisen, Weiner, Petersen, Trojanowski (bib0018) 2010; 9 Cao, Ke, Sandstede, Luo (bib0005) 2019 Young, Oxtoby, Huang, Marinescu, Daga, Cash, Fox, Ourselin, Schott, Alexander (bib0052) 2015 Alzheimer’s Association., 2018. Alzheimer’s Dement.: Global Resources. Moore, Lyons, Gallacher (bib0033) 2019; 14 Hochreiter, Schmidhuber (bib0016) 1997; 9 Hardoon, D. R., Szedmak, S., Shawe-Taylor, J., 2004. Canonical correlation analysis: an overview with application to learning methods. 10.1162/0899766042321814 Vounou, Nichols, Montana (bib0046) 2010; 53 Fonteijn, Modat, Clarkson, Barnes, Lehmann, Hobbs, Scahill, Tabrizi, Ourselin, Fox, Alexander (bib0012) 2012; 60 El-Sappagh, Abuhmed, Riazul Islam, Kwak (bib0008) 2020; 412 Antelmi, Ayache, Robert, Lorenzi (bib0003) 2019 Shaw, Vanderstichele, Knapik-Czajka, Clark, Aisen, Petersen, Blennow, Soares, Simon, Lewczuk, Dean, Siemers, Potter, Lee, Trojanowski (bib0041) 2009; 65 Verbeke, Fieuws, Molenberghs, Davidian (bib0045) 2014; 23 Édith Le Floch, Guillemot, Frouin, Pinel, Lalanne, Trinchera, Tenenhaus, Moreno, Zilbovicius, Bourgeron, Dehaene, Thirion, Poline, Édouard Duchesnay (bib0023) 2012; 63 Martí-Juan, Sanroma-Guell, Piella (bib0029) 2020; 189 El-Sappagh, Alonso, Islam, Sultan, Kwak (bib0009) 2021; 11 Sobol, Kucherenko (bib0043) 2009; 79 Shi, Siddharth, Paige, Torr (bib0042) 2019 Young, Marinescu, Oxtoby, Bocchetta, Yong, Firth, Cash, Thomas, Dick, Cardoso, van Swieten, Borroni, Galimberti, Masellis, Tartaglia, Rowe, Graff, Tagliavini, Frisoni, Laforce, Finger, de Mendonça, Sorbi, Warren, Crutch, Fox, Ourselin, Schott, Rohrer, and (bib0050) 2018; 9 . Lorenzi, Ziegler, Alexander, Ourselin (bib0027) 2015; 24 Goldberg (bib0013) 2017; 10 Molamohammadi, Rezaei-Shoshtari, Quitoriano (bib0031) 2020; Vol. 2020 Reuter, Rosas, Fischl (bib0039) 2010; 53 Reuter, Schmansky, Rosas, Fischl (bib0040) 2012; 61 Hyun, Li, Huang, Styner, Lin, Zhu (bib0017) 2016; 134 Gregor, Danihelka, Graves, Rezende, Wierstra (bib0014) 2015 Bakkour, Morris, Wolk, Dickerson (bib0004) 2013; 76 Nguyen, Sun, Alexander, Feng, Thomas Yeo (bib0036) 2018 Jacobs, Hopkins, Mayrhofer, Bruner, van Leeuwen, Raaijmakers, Schmahmann (bib0019) 2017; 141 Ngiam, Khosla, Kim, Nam, Lee, Ng (bib0035) 2011 Marinescu, Eshaghi, Alexander, Golland (bib0028) 2019 Chung, Kastner, Dinh, Goel, Courville, Bengio (bib0006) 2015 Klami, Virtanen, Leppaaho, Kaski (bib0021) 2015; 26 Molchanov, Ashukha, Vetrov (bib0032) 2017; Vol. 5 Oxtoby, Alexander (bib0038) 2017; 30 Wang, Qiu, Yu (bib0047) 2018; 8 Fabius, van Amersfoort (bib0010) 2015 Lane, Hardy, Schott (bib0022) 2018; 25 Lee, Nho, Kang, Sohn, Kim, Weiner, Aisen, Petersen, Jack (bib0024) 2019; 9 Du, Schuff, Amend, Laakso, Hsu, Jagust, Yaffe, Kramer, Reed, Norman, Chui, Weiner (bib0007) 2001; 71 Fisher, Smith, Walsh, Simon, Edgar, Jack, Holtzman, Russell, Hill, Grosset, Wood, Vanderstichele, Morris, Blennow, Marek, Shaw, Albert, Weiner, Fox, Aisen, Cole, Petersen, Sherer, Kubick (bib0011) 2019; 9 Kingma, Ba (bib0020) 2015 Yi, Möller, Dieleman, Bouwman, Barkhof, Scheltens, van der Flier, Vrenken (bib0049) 2016; 87 Lorenzi, Filippone, Frisoni, Alexander, Ourselin (bib0026) 2019; 190 Young, Oxtoby, Daga, Cash, Fox, Ourselin, Schott, Alexander (bib0051) 2014; 137 Young, Vogel, Aksman, Wijeratne, Eshaghi, Oxtoby, Williams, Alexander (bib0053) 2021; 4 Lei, Yang, Yang, Zhou, Hou, Zou, Li, Wang, Xiao, Wang (bib0025) 2020; 102 Nie, Zhang, Meng, Song, Chang, Li (bib0037) 2017; 28 Wu, Goodman (bib0048) 2018 Mueller, Weiner, Thal, Petersen, Jack, Jagust, Trojanowski, Toga, Beckett (bib0034) 2005; 15 Aksman, Scelsi, Marquand, Alexander, Ourselin, Altmann (bib0001) 2019; 40 Mehdipour Ghazi, Nielsen, Pai, Cardoso, Modat, Ourselin, Sørensen (bib0030) 2019; 53 Tsai, Liang, Zadeh, Morency, Salakhutdinov (bib0044) 2019 Hochreiter (10.1016/j.neuroimage.2023.119892_bib0016) 1997; 9 Molamohammadi (10.1016/j.neuroimage.2023.119892_bib0031) 2020; Vol. 2020 Reuter (10.1016/j.neuroimage.2023.119892_bib0039) 2010; 53 Jack (10.1016/j.neuroimage.2023.119892_bib0018) 2010; 9 Fabius (10.1016/j.neuroimage.2023.119892_bib0010) 2015 Mueller (10.1016/j.neuroimage.2023.119892_bib0034) 2005; 15 Reuter (10.1016/j.neuroimage.2023.119892_bib0040) 2012; 61 Shi (10.1016/j.neuroimage.2023.119892_bib0042) 2019 Shaw (10.1016/j.neuroimage.2023.119892_bib0041) 2009; 65 Marinescu (10.1016/j.neuroimage.2023.119892_bib0028) 2019 Chung (10.1016/j.neuroimage.2023.119892_bib0006) 2015 Antelmi (10.1016/j.neuroimage.2023.119892_bib0003) 2019 Kingma (10.1016/j.neuroimage.2023.119892_bib0020) 2015 Du (10.1016/j.neuroimage.2023.119892_bib0007) 2001; 71 El-Sappagh (10.1016/j.neuroimage.2023.119892_bib0009) 2021; 11 Gregor (10.1016/j.neuroimage.2023.119892_bib0014) 2015 Molchanov (10.1016/j.neuroimage.2023.119892_bib0032) 2017; Vol. 5 Aksman (10.1016/j.neuroimage.2023.119892_bib0001) 2019; 40 Young (10.1016/j.neuroimage.2023.119892_bib0050) 2018; 9 Tsai (10.1016/j.neuroimage.2023.119892_bib0044) 2019 Vounou (10.1016/j.neuroimage.2023.119892_bib0046) 2010; 53 Fonteijn (10.1016/j.neuroimage.2023.119892_bib0012) 2012; 60 Lee (10.1016/j.neuroimage.2023.119892_bib0024) 2019; 9 10.1016/j.neuroimage.2023.119892_bib0015 Nie (10.1016/j.neuroimage.2023.119892_bib0037) 2017; 28 Klami (10.1016/j.neuroimage.2023.119892_bib0021) 2015; 26 Lorenzi (10.1016/j.neuroimage.2023.119892_bib0027) 2015; 24 Moore (10.1016/j.neuroimage.2023.119892_bib0033) 2019; 14 Hyun (10.1016/j.neuroimage.2023.119892_bib0017) 2016; 134 Goldberg (10.1016/j.neuroimage.2023.119892_bib0013) 2017; 10 Lorenzi (10.1016/j.neuroimage.2023.119892_bib0026) 2019; 190 Fisher (10.1016/j.neuroimage.2023.119892_bib0011) 2019; 9 Lei (10.1016/j.neuroimage.2023.119892_bib0025) 2020; 102 Sobol (10.1016/j.neuroimage.2023.119892_bib0043) 2009; 79 El-Sappagh (10.1016/j.neuroimage.2023.119892_bib0008) 2020; 412 10.1016/j.neuroimage.2023.119892_bib0002 Édith Le Floch (10.1016/j.neuroimage.2023.119892_bib0023) 2012; 63 Verbeke (10.1016/j.neuroimage.2023.119892_bib0045) 2014; 23 Bakkour (10.1016/j.neuroimage.2023.119892_bib0004) 2013; 76 Mehdipour Ghazi (10.1016/j.neuroimage.2023.119892_bib0030) 2019; 53 Ngiam (10.1016/j.neuroimage.2023.119892_bib0035) 2011 Cao (10.1016/j.neuroimage.2023.119892_bib0005) 2019 Nguyen (10.1016/j.neuroimage.2023.119892_bib0036) 2018 Young (10.1016/j.neuroimage.2023.119892_bib0052) 2015 Oxtoby (10.1016/j.neuroimage.2023.119892_bib0038) 2017; 30 Young (10.1016/j.neuroimage.2023.119892_bib0053) 2021; 4 Yi (10.1016/j.neuroimage.2023.119892_bib0049) 2016; 87 Wang (10.1016/j.neuroimage.2023.119892_bib0047) 2018; 8 Lane (10.1016/j.neuroimage.2023.119892_bib0022) 2018; 25 Wu (10.1016/j.neuroimage.2023.119892_bib0048) 2018 Martí-Juan (10.1016/j.neuroimage.2023.119892_bib0029) 2020; 189 Jacobs (10.1016/j.neuroimage.2023.119892_bib0019) 2017; 141 Young (10.1016/j.neuroimage.2023.119892_bib0051) 2014; 137 |
| References_xml | – volume: 65 start-page: 403 year: 2009 end-page: 413 ident: bib0041 article-title: Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects publication-title: Ann. Neurol. – volume: 79 start-page: 3009 year: 2009 end-page: 3017 ident: bib0043 article-title: Derivative based global sensitivity measures and their link with global sensitivity indices publication-title: Math. Comput. Simul. – volume: 24 start-page: 626 year: 2015 end-page: 637 ident: bib0027 article-title: Efficient Gaussian process-based modelling and prediction of image time series publication-title: Inf. Process. Med. Imaging – start-page: 453 year: 2019 end-page: 464 ident: bib0003 article-title: Sparse multi-channel variational autoencoder for the joint analysis of heterogeneous data publication-title: 36th Int. Conf. Mach. Learn., ICML 2019 – reference: Alzheimer’s Association., 2018. Alzheimer’s Dement.: Global Resources. – volume: 10 start-page: 1 year: 2017 end-page: 311 ident: bib0013 article-title: Neural network methods for natural language processing publication-title: Synth. Lect. Hum. Lang. Technol. – volume: 61 start-page: 1402 year: 2012 end-page: 1418 ident: bib0040 article-title: Within-subject template estimation for unbiased longitudinal image analysis publication-title: Neuroimage – volume: 87 start-page: 425 year: 2016 end-page: 432 ident: bib0049 article-title: Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to alzheimer’s disease publication-title: J. Neurol. Neurosurg. Psychiatry – start-page: 112 year: 2019 end-page: 120 ident: bib0028 article-title: BrainPainter: a software for the visualisation of brain structures, biomarkers and associated pathological processes publication-title: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) – start-page: 2980 year: 2015 end-page: 2988 ident: bib0006 article-title: A recurrent latent variable model for sequential data publication-title: Adv. Neural Inf. Process. Syst. – volume: 53 start-page: 1147 year: 2010 end-page: 1159 ident: bib0046 article-title: Discovering genetic associations with high-dimensional neuroimaging phenotypes: a sparse reduced-rank regression approach publication-title: Neuroimage – volume: 30 start-page: 371 year: 2017 end-page: 379 ident: bib0038 article-title: Imaging plus X: multimodal models of neurodegenerative disease publication-title: Curr. Opin. Neurol. – year: 2019 ident: bib0044 article-title: Learning factorized multimodal representations publication-title: Int. Conf. Learn. Represent. ICLR 2019 - Conf. Track Proc. – volume: 8 start-page: 9161 year: 2018 ident: bib0047 article-title: Predictive modeling of the progression of Alzheimer’s disease with recurrent neural networks publication-title: Sci. Rep. – volume: 71 start-page: 441 year: 2001 end-page: 447 ident: bib0007 article-title: Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and alzheimer’s disease publication-title: J. Neurol. Neurosurg. Psychiatry – volume: 26 start-page: 2136 year: 2015 end-page: 2147 ident: bib0021 article-title: Group factor analysis publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 60 start-page: 1880 year: 2012 end-page: 1889 ident: bib0012 article-title: An event-based model for disease progression and its application in familial Alzheimer’s disease and Huntington’s disease publication-title: Neuroimage – volume: Vol. 5 start-page: 3854 year: 2017 end-page: 3863 ident: bib0032 article-title: Variational dropout sparsifies deep neural networks publication-title: 34th Int. Conf. Mach. Learn., ICML 2017 – volume: 137 start-page: 2564 year: 2014 end-page: 2577 ident: bib0051 article-title: A data-driven model of biomarker changes in sporadic Alzheimer’s disease publication-title: Brain – year: 2018 ident: bib0048 article-title: Multimodal generative models for scalable weakly-supervised learning publication-title: NeurIPS – volume: 28 start-page: 1508 year: 2017 end-page: 1519 ident: bib0037 article-title: Modeling disease progression via multisource multitask learners: a case study with Alzheimer’s disease publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 190 start-page: 56 year: 2019 end-page: 68 ident: bib0026 article-title: Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer’s disease publication-title: Neuroimage – volume: 141 start-page: 37 year: 2017 end-page: 47 ident: bib0019 article-title: The cerebellum in Alzheimer’s disease: evaluating its role in cognitive decline publication-title: Brain – volume: 412 start-page: 197 year: 2020 end-page: 215 ident: bib0008 article-title: Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data publication-title: Neurocomputing – volume: 14 start-page: e0211558 year: 2019 ident: bib0033 article-title: Random forest prediction of Alzheimer’s disease using pairwise selection from time series data publication-title: PLoS ONE – volume: 76 start-page: 332 year: 2013 end-page: 344 ident: bib0004 article-title: The effects of aging and Alzheimer’s disease on cerebral cortical anatomy: specificity and differential relationships with cognition publication-title: Neuroimage – start-page: 689 year: 2011 end-page: 696 ident: bib0035 article-title: Multimodal deep learning publication-title: Proc. 28th Int. Conf. Mach. Learn., ICML 2011 – start-page: 711 year: 2015 end-page: 722 ident: bib0052 article-title: Multiple orderings of events in disease progression publication-title: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) – year: 2019 ident: bib0005 article-title: Time-dependent canonical correlation analysis for multilevel time series publication-title: bioRxiv – volume: 134 start-page: 550 year: 2016 end-page: 562 ident: bib0017 article-title: STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data publication-title: Neuroimage – volume: 15 start-page: 869 year: 2005 end-page: 877 ident: bib0034 article-title: The Alzheimer’s disease neuroimaging initiative publication-title: Neuroimaging Clin. N. Am. – volume: 53 start-page: 1181 year: 2010 end-page: 1196 ident: bib0039 article-title: Highly accurate inverse consistent registration: arobust approach publication-title: Neuroimage – year: 2015 ident: bib0020 article-title: Adam: a method for stochastic optimization publication-title: 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc. – year: 2019 ident: bib0042 article-title: Variational mixture-of-experts autoencoders for multi-modal deep generative models publication-title: NeurIPS – volume: 9 year: 2019 ident: bib0011 article-title: Machine learning for comprehensive forecasting of Alzheimer’s disease progression publication-title: Sci. Rep. – volume: 40 start-page: 3982 year: 2019 end-page: 4000 ident: bib0001 article-title: Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning publication-title: Hum. Brain Mapp. – volume: 11 start-page: 1 year: 2021 end-page: 26 ident: bib0009 article-title: A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease publication-title: Sci. Rep. – year: 2018 ident: bib0036 article-title: Modeling Alzheimer’s disease progression using deep recurrent neural networks publication-title: 2018 Int. Work. Pattern Recognit. Neuroimaging, PRNI 2018 – volume: 189 start-page: 105348 year: 2020 ident: bib0029 article-title: A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer’s disease publication-title: Comput. Methods Programs Biomed. – volume: 9 year: 2018 ident: bib0050 article-title: Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with subtype and stage inference publication-title: Nat. Commun. – volume: 63 start-page: 11 year: 2012 end-page: 24 ident: bib0023 article-title: Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse partial least squares publication-title: Neuroimage – volume: 23 start-page: 42 year: 2014 end-page: 49 ident: bib0045 article-title: The analysis of multivariate longitudinal data: areview publication-title: Stat. Methods Med. Res. – volume: 4 start-page: 1 year: 2021 end-page: 13 ident: bib0053 article-title: Ordinal SuStaIn: subtype and stage inference for clinical scores, visual ratings, and other ordinal data publication-title: Front. Artif. Intell. – volume: 9 start-page: 119 year: 2010 end-page: 128 ident: bib0018 article-title: Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade publication-title: Lancet Neurol. – reference: . – volume: 9 start-page: 1 year: 2019 end-page: 12 ident: bib0024 article-title: Predicting Alzheimer’s disease progression using multi-modal deep learning approach publication-title: Sci. Rep. – reference: Hardoon, D. R., Szedmak, S., Shawe-Taylor, J., 2004. Canonical correlation analysis: an overview with application to learning methods. 10.1162/0899766042321814 – year: 2015 ident: bib0010 article-title: Variational recurrent auto-encoders publication-title: 3rd Int. Conf. Learn. Represent. ICLR 2015 - Work. Track Proc. – volume: 25 start-page: 59 year: 2018 end-page: 70 ident: bib0022 article-title: Alzheimer’s disease publication-title: Eur. J. Neurol. – volume: 53 start-page: 39 year: 2019 end-page: 46 ident: bib0030 article-title: Training recurrent neural networks robust to incomplete data: application to Alzheimer’s disease progression modeling publication-title: Med. Image Anal. – volume: Vol. 2020 year: 2020 ident: bib0031 article-title: Jacobian of generative models for sensitivity analysis of photovoltaic device processes publication-title: Machine Learning for Engineering Workshop at NeurIPS – volume: 102 year: 2020 ident: bib0025 article-title: Deep and joint learning of longitudinal data for Alzheimer’s disease prediction publication-title: Pattern Recognit. – start-page: 1462 year: 2015 end-page: 1471 ident: bib0014 article-title: DRAW: a recurrent neural network for image generation publication-title: 32nd Int. Conf. Mach. Learn. ICML 2015 – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: bib0016 article-title: Long short-term memory publication-title: Neural Comput. – volume: 30 start-page: 371 issue: 4 year: 2017 ident: 10.1016/j.neuroimage.2023.119892_bib0038 article-title: Imaging plus X: multimodal models of neurodegenerative disease publication-title: Curr. Opin. Neurol. doi: 10.1097/WCO.0000000000000460 – volume: 137 start-page: 2564 issue: Pt 9 year: 2014 ident: 10.1016/j.neuroimage.2023.119892_bib0051 article-title: A data-driven model of biomarker changes in sporadic Alzheimer’s disease publication-title: Brain doi: 10.1093/brain/awu176 – start-page: 112 year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0028 article-title: BrainPainter: a software for the visualisation of brain structures, biomarkers and associated pathological processes – volume: 76 start-page: 332 year: 2013 ident: 10.1016/j.neuroimage.2023.119892_bib0004 article-title: The effects of aging and Alzheimer’s disease on cerebral cortical anatomy: specificity and differential relationships with cognition publication-title: Neuroimage doi: 10.1016/j.neuroimage.2013.02.059 – year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0005 article-title: Time-dependent canonical correlation analysis for multilevel time series publication-title: bioRxiv – start-page: 1462 year: 2015 ident: 10.1016/j.neuroimage.2023.119892_bib0014 article-title: DRAW: a recurrent neural network for image generation – volume: 102 year: 2020 ident: 10.1016/j.neuroimage.2023.119892_bib0025 article-title: Deep and joint learning of longitudinal data for Alzheimer’s disease prediction publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2020.107247 – year: 2018 ident: 10.1016/j.neuroimage.2023.119892_bib0036 article-title: Modeling Alzheimer’s disease progression using deep recurrent neural networks – volume: 71 start-page: 441 issue: 4 year: 2001 ident: 10.1016/j.neuroimage.2023.119892_bib0007 article-title: Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and alzheimer’s disease publication-title: J. Neurol. Neurosurg. Psychiatry doi: 10.1136/jnnp.71.4.441 – volume: 26 start-page: 2136 issue: 9 year: 2015 ident: 10.1016/j.neuroimage.2023.119892_bib0021 article-title: Group factor analysis publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2014.2376974 – volume: 4 start-page: 1 issue: August year: 2021 ident: 10.1016/j.neuroimage.2023.119892_bib0053 article-title: Ordinal SuStaIn: subtype and stage inference for clinical scores, visual ratings, and other ordinal data publication-title: Front. Artif. Intell. – volume: 9 start-page: 119 issue: 1 year: 2010 ident: 10.1016/j.neuroimage.2023.119892_bib0018 article-title: Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade publication-title: Lancet Neurol. doi: 10.1016/S1474-4422(09)70299-6 – volume: 40 start-page: 3982 issue: 13 year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0001 article-title: Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.24682 – volume: 10 start-page: 1 issue: 1 year: 2017 ident: 10.1016/j.neuroimage.2023.119892_bib0013 article-title: Neural network methods for natural language processing publication-title: Synth. Lect. Hum. Lang. Technol. – volume: 60 start-page: 1880 issue: 3 year: 2012 ident: 10.1016/j.neuroimage.2023.119892_bib0012 article-title: An event-based model for disease progression and its application in familial Alzheimer’s disease and Huntington’s disease publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.01.062 – volume: 9 issue: 1 year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0011 article-title: Machine learning for comprehensive forecasting of Alzheimer’s disease progression publication-title: Sci. Rep. doi: 10.1038/s41598-019-49656-2 – volume: 63 start-page: 11 issue: 1 year: 2012 ident: 10.1016/j.neuroimage.2023.119892_bib0023 article-title: Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse partial least squares publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.06.061 – volume: 28 start-page: 1508 issue: 7 year: 2017 ident: 10.1016/j.neuroimage.2023.119892_bib0037 article-title: Modeling disease progression via multisource multitask learners: a case study with Alzheimer’s disease publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2520964 – volume: 9 issue: 1 year: 2018 ident: 10.1016/j.neuroimage.2023.119892_bib0050 article-title: Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with subtype and stage inference publication-title: Nat. Commun. doi: 10.1038/s41467-018-05892-0 – volume: 190 start-page: 56 year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0026 article-title: Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer’s disease publication-title: Neuroimage doi: 10.1016/j.neuroimage.2017.08.059 – volume: 141 start-page: 37 issue: 1 year: 2017 ident: 10.1016/j.neuroimage.2023.119892_bib0019 article-title: The cerebellum in Alzheimer’s disease: evaluating its role in cognitive decline publication-title: Brain doi: 10.1093/brain/awx194 – volume: 9 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0024 article-title: Predicting Alzheimer’s disease progression using multi-modal deep learning approach publication-title: Sci. Rep. – year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0044 article-title: Learning factorized multimodal representations – volume: 412 start-page: 197 year: 2020 ident: 10.1016/j.neuroimage.2023.119892_bib0008 article-title: Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.05.087 – volume: 23 start-page: 42 issue: 1 year: 2014 ident: 10.1016/j.neuroimage.2023.119892_bib0045 article-title: The analysis of multivariate longitudinal data: areview publication-title: Stat. Methods Med. Res. doi: 10.1177/0962280212445834 – volume: 53 start-page: 1181 issue: 4 year: 2010 ident: 10.1016/j.neuroimage.2023.119892_bib0039 article-title: Highly accurate inverse consistent registration: arobust approach publication-title: Neuroimage doi: 10.1016/j.neuroimage.2010.07.020 – ident: 10.1016/j.neuroimage.2023.119892_bib0015 doi: 10.1162/0899766042321814 – volume: 61 start-page: 1402 issue: 4 year: 2012 ident: 10.1016/j.neuroimage.2023.119892_bib0040 article-title: Within-subject template estimation for unbiased longitudinal image analysis publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.02.084 – volume: 65 start-page: 403 issue: 4 year: 2009 ident: 10.1016/j.neuroimage.2023.119892_bib0041 article-title: Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects publication-title: Ann. Neurol. doi: 10.1002/ana.21610 – year: 2015 ident: 10.1016/j.neuroimage.2023.119892_bib0010 article-title: Variational recurrent auto-encoders – start-page: 689 year: 2011 ident: 10.1016/j.neuroimage.2023.119892_bib0035 article-title: Multimodal deep learning – volume: 79 start-page: 3009 issue: 10 year: 2009 ident: 10.1016/j.neuroimage.2023.119892_bib0043 article-title: Derivative based global sensitivity measures and their link with global sensitivity indices publication-title: Math. Comput. Simul. doi: 10.1016/j.matcom.2009.01.023 – volume: 11 start-page: 1 issue: 1 year: 2021 ident: 10.1016/j.neuroimage.2023.119892_bib0009 article-title: A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease publication-title: Sci. Rep. doi: 10.1038/s41598-021-82098-3 – volume: 53 start-page: 1147 issue: 3 year: 2010 ident: 10.1016/j.neuroimage.2023.119892_bib0046 article-title: Discovering genetic associations with high-dimensional neuroimaging phenotypes: a sparse reduced-rank regression approach publication-title: Neuroimage doi: 10.1016/j.neuroimage.2010.07.002 – ident: 10.1016/j.neuroimage.2023.119892_bib0002 – volume: 25 start-page: 59 issue: 1 year: 2018 ident: 10.1016/j.neuroimage.2023.119892_bib0022 article-title: Alzheimer’s disease publication-title: Eur. J. Neurol. doi: 10.1111/ene.13439 – volume: 24 start-page: 626 year: 2015 ident: 10.1016/j.neuroimage.2023.119892_bib0027 article-title: Efficient Gaussian process-based modelling and prediction of image time series publication-title: Inf. Process. Med. Imaging – volume: 14 start-page: e0211558 issue: 2 year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0033 article-title: Random forest prediction of Alzheimer’s disease using pairwise selection from time series data publication-title: PLoS ONE doi: 10.1371/journal.pone.0211558 – volume: 15 start-page: 869 issue: 4 year: 2005 ident: 10.1016/j.neuroimage.2023.119892_bib0034 article-title: The Alzheimer’s disease neuroimaging initiative publication-title: Neuroimaging Clin. N. Am. doi: 10.1016/j.nic.2005.09.008 – volume: Vol. 5 start-page: 3854 year: 2017 ident: 10.1016/j.neuroimage.2023.119892_bib0032 article-title: Variational dropout sparsifies deep neural networks – year: 2018 ident: 10.1016/j.neuroimage.2023.119892_bib0048 article-title: Multimodal generative models for scalable weakly-supervised learning – volume: 8 start-page: 9161 issue: 1 year: 2018 ident: 10.1016/j.neuroimage.2023.119892_bib0047 article-title: Predictive modeling of the progression of Alzheimer’s disease with recurrent neural networks publication-title: Sci. Rep. doi: 10.1038/s41598-018-27337-w – year: 2015 ident: 10.1016/j.neuroimage.2023.119892_bib0020 article-title: Adam: a method for stochastic optimization – volume: 53 start-page: 39 year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0030 article-title: Training recurrent neural networks robust to incomplete data: application to Alzheimer’s disease progression modeling publication-title: Med. Image Anal. doi: 10.1016/j.media.2019.01.004 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 10.1016/j.neuroimage.2023.119892_bib0016 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 134 start-page: 550 year: 2016 ident: 10.1016/j.neuroimage.2023.119892_bib0017 article-title: STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data publication-title: Neuroimage doi: 10.1016/j.neuroimage.2016.04.023 – volume: Vol. 2020 year: 2020 ident: 10.1016/j.neuroimage.2023.119892_bib0031 article-title: Jacobian of generative models for sensitivity analysis of photovoltaic device processes – start-page: 2980 year: 2015 ident: 10.1016/j.neuroimage.2023.119892_bib0006 article-title: A recurrent latent variable model for sequential data publication-title: Adv. Neural Inf. Process. Syst. – start-page: 711 year: 2015 ident: 10.1016/j.neuroimage.2023.119892_bib0052 article-title: Multiple orderings of events in disease progression – start-page: 453 year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0003 article-title: Sparse multi-channel variational autoencoder for the joint analysis of heterogeneous data – volume: 189 start-page: 105348 year: 2020 ident: 10.1016/j.neuroimage.2023.119892_bib0029 article-title: A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer’s disease publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2020.105348 – volume: 87 start-page: 425 issue: 4 year: 2016 ident: 10.1016/j.neuroimage.2023.119892_bib0049 article-title: Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to alzheimer’s disease publication-title: J. Neurol. Neurosurg. Psychiatry doi: 10.1136/jnnp-2014-309105 – year: 2019 ident: 10.1016/j.neuroimage.2023.119892_bib0042 article-title: Variational mixture-of-experts autoencoders for multi-modal deep generative models |
| SSID | ssj0009148 |
| Score | 2.5142014 |
| Snippet | •A multi-channel model based on recurrent variational autoencoders was proposed to capture spatial and temporal evolution of AD using multimodal data.•Proposed... The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and... The progression of neurodegenerative diseases, such as Alzheimer’s Disease, is the result of complex mechanisms interacting across multiple spatial and... |
| SourceID | doaj proquest pubmed crossref elsevier |
| SourceType | Open Website Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 119892 |
| SubjectTerms | Alzheimer Disease - diagnostic imaging Alzheimer's disease Biomarkers Cognitive ability Disease Progression Disease progression modelling Factor analysis Humans Longitudinal Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical research Multimodal Neural networks Neural Networks, Computer Neurodegenerative diseases Neuroimaging - methods Positron-Emission Tomography - methods Recurrent neural network Variables Variational autoencoder |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NbtQwELZQhRAXxD-BgozENZAfJ47hVKpWHGiFEFS9WXY8FlvtZlF2twdOvAavx5MwYzsLPSD2gBTlkNiJNTMef7bH3zD2QlVQeekgR-Mxuahkn9uisblswTocz0UTtgvO3svT0-78XH34I9UXxYRFeuAouFcgGnKz0JjKC2Mqa11tjBINlN6Bt-R9EfVMk6mJbhdRforbidFcgR1ytsA--pIShqOnUJ2qrgxGgbP_ypj0N8wZxp7j2-xWAo38IDb2DrsGw1124yRti99jq5PD_OPZwdFrHs7T5nSad4A5H2kxneiX-CVOidOyHzeb9ZLoKx2MHCErDzGFi6WjP8y_fYHZAsaf33-seNq84SGGK_J38JA5h46w32efj48-Hb7LUzaFvEdIts6tAt8WoFB2wkqcNvpSGvC1qYxSwinrmsa1dWlNUfuisGWF5Xugqy3bXtYP2N6wHOAR461rpDe2RN8khPFgbS0Q2eBnvZfKdRmTk1h1n6jGKePFXE8xZRf6t0I0KURHhWSs3Nb8Guk2dqjzljS3LU-E2eEBmpFOZqT_ZUYZU5Pe9XQmFb0ofmi2QwPebOsm3BLxyI619ycz08l_rHQlZU0RAV2Rsefb19jzaTvHDLDcUJm260q6ZexhNM-tDOq2xal_oR7_D9k8YTepvTHybp_trccNPGXX-8v1bDU-Cx3vF9DxOVI priority: 102 providerName: Directory of Open Access Journals |
| Title | MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1053811923000411 https://dx.doi.org/10.1016/j.neuroimage.2023.119892 https://www.ncbi.nlm.nih.gov/pubmed/36682509 https://www.proquest.com/docview/2773700180 https://www.proquest.com/docview/2768817688 https://doaj.org/article/e451303e5a2f4aa2bbd3aa945e1fdefb |
| Volume | 268 |
| WOSCitedRecordID | wos000934066200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1095-9572 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVESC databaseName: ScienceDirect database customDbUrl: eissn: 1095-9572 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: AIEXJ dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1095-9572 dateEnd: 20251007 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: M7P dateStart: 19980501 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1095-9572 dateEnd: 20251007 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: 7X7 dateStart: 20020801 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1095-9572 dateEnd: 20251007 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: BENPR dateStart: 19980501 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Psychology Database customDbUrl: eissn: 1095-9572 dateEnd: 20251007 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: M2M dateStart: 20020801 isFulltext: true titleUrlDefault: https://www.proquest.com/psychology providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3Nb9MwFLfYhhAXvmGBURmJqyFOnDiGA9qmThxoVU0w9RbZsQ1FbbOl7Q6c-Df49_hL8HOcVDuAKiFFPiS28_Gen1_ex-8h9FokJrFcG-KYRxKW8IqoOFOE50Zpt5-zzLsLLj7x8biYTsUkGNxWIayyk4leUOu6Ahv524TzFHykRfzh8opA1SjwroYSGnvoAFASUh-6N9mC7lLWpsJlKSkoFSGSp43v8niRs4VbtW-ghLiTHaIQyY3tyaP439il_qaF-t3o7P7_vscDdC_oofi4ZZyH6JZZPkJ3RsHT_hitRqfk_OJ4-A77FF0CCcJLM8cN2OcB0Qlfu7_sYEnEcrOuARFTmwY7LRj7MMVFreEO8x_fzGxhmt8_f61w8AdhHxbWQoJgX4wHsuKfoC9nw8-nH0ko0EAqp-WtiRLG5rERUjCmuPsTtZRLY1OZSCGYFkpnmc5TqmSc2jhWNHH9KwNHTvOKp0_R_rJemkOEc51xKxV14o4xaY1SKXPKkpvWWi50ESHe0aWsAno5FNGYl12Y2vdyS9ESKFq2FI0Q7UdetggeO4w5AdL3_QGD25-om69lWNKlYRkoACaTiWVSJkrpVLpPkRlqtbEqQqJjnLJLc3WC2U002-EB3vdjgyrUqjg7jj7qeK8MImlVbhkvQq_6y06YgIdILk29gT55UVBoIvSs5e_-G6R5Xjh9WTz_9-Qv0F14kjZM7wjtr5uNeYluV9fr2aoZoD0-5b4tBujgZDienA-8GcS1o2Q08Ov3D4ndTK0 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbhMxFLVKQZQN70eggJFg6TL2eMZjEEKltGrVJEKoVN0Ze2yXoCRTJkkRrPgNfoKP4kvwnUeiLkDZdIE0ymJieybO8bnXvi-EnkrmmBfWkQAeTTgTOTFRYohInbFBnvOkMhccdkW_nx0dyXcr6FcbCwNulS0nVkRtixzOyJ8zIWKwkWbR65MvBKpGgXW1LaFRw2LfffsatmyTV3tvw__7jLGd7YOtXdJUFSB5UE2mxEjn08hJLTk3ImyfPBXa-VgzLSW30tgksWlMjY5iH0WGstA-d3ClNM1FHMa9gC7ysBOCUhE91lsk-aW8Dr1LYpJRKhvPodqfrMpPORgFltiAkuWBq2Qm2RlxWFUNOCMV_6b1VtJv59r_Nm_X0dVGz8ab9cK4gVbc-Ca63Gs8CW6hSW-LvD_c3H6BqxBkAgHQYzfEJdgfIGMVPtXloDkpxXo2LSDjp3UlDlo-rtwwR4WFJwy_f3KDkSt___g5wY29C1dub3XKE1wVG4Ko_9vow7n85DtodVyM3T2EU5sIrw0NdM659s6YmAdlMAzrvZA26yDR4kDlTXZ2KBIyVK0b3me1QJACBKkaQR1E5z1P6gwlS_R5A1Cbt4cc49WNojxWDWUpxxNQcFyimedaM2NsrMNUJI5667zpINkCVbVhvEHwhIEGS7zAy3nfRtWrVbgle6-3WFcN5U7UAugd9GT-dSBLsIDpsStm0CbNMgofHXS3Xk_zOYjTNAv7AXn_34M_Rmu7B72u6u719x-gK_BWtUviOlqdljP3EF3KT6eDSfmo4gaMPp73ovoD33OlpA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3dbtMwFLbGQBM3_P8UBhgJLsNix4ljEEJjW8W0raoQTLszdmxvRW0z0nYIrngNXoXH4UnwcZJWuwD1ZhdIVS9a20ndz-cc53z-DkLPBLXUcWMjDx4VMcqLSMepjnhmtfH-nKUhXXC4z3u9_OhI9FfQr_YsDNAqW5sYDLUpC3hGvkE5TyBHmscbrqFF9Le7b06_RFBBCjKtbTmNGiJ79ttXv32bvN7d9v_1c0q7Ox-23kVNhYGo8GHKNNLCuiy2QgnGNPdbKUe4si5RVAnBjNAmTU2WEK3ixMWxJtS3Lyy8MpIVPPHjXkKXOYiWB9pgfyH4S1h9DC9NopwQ0bCIam5Z0KocjLzFeAHly73dErmg51xjqCBwzkP-LQIOnrB7_X-ewxvoWhN_4816wdxEK3Z8C60dNAyD22hysBW9P9zceYnD0eQIDkaP7RBXkJcAJSt8pqpB8wQVq9m0BCVQYyvso38c6Jmj0sAVht9P7GBkq98_fk5wkwfDgQ5XS6HgUIQI1ADuoI8X8pPvotVxObb3Ec5Myp3SxJt5xpSzWifMB4l-WOe4MHkH8RYTsmhU26F4yFC29LzPcoEmCWiSNZo6iMx7ntbKJUv0eQuwm7cH7fHwQVkdy8aUSctSCHxsqqhjSlGtTaL8VKSWOGOd7iDRgla2x3u9Q_IDDZa4gVfzvk0IWId2S_Zeb3EvG1M8kQvQd9DT-dfeiEJmTI1tOYM2WZ4TeOuge_Xams9BkmW53yeIB_8e_Ala82tJ7u_29h6iq3BTNVNxHa1Oq5l9hK4UZ9PBpHoczARGny56Tf0B9wmudQ |
| 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=MC-RVAE%3A+Multi-channel+recurrent+variational+autoencoder+for+multimodal+Alzheimer%E2%80%99s+disease+progression+modelling&rft.jtitle=NeuroImage+%28Orlando%2C+Fla.%29&rft.au=Mart%C3%AD-Juan%2C+Gerard&rft.au=Lorenzi%2C+Marco&rft.au=Piella%2C+Gemma&rft.date=2023-03-01&rft.pub=Elsevier+Inc&rft.issn=1053-8119&rft.volume=268&rft_id=info:doi/10.1016%2Fj.neuroimage.2023.119892&rft.externalDocID=S1053811923000411 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-8119&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-8119&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-8119&client=summon |