Hauptseite > Publikationsdatenbank > Explainable artificial intelligence identifies an AQP4 polymorphism-based risk score associated with brain amyloid burden > print

001     1030442
005     20250203133200.0
024 7 _ |a 10.1016/j.neurobiolaging.2024.08.002
|2 doi
024 7 _ |a 0197-4580
|2 ISSN
024 7 _ |a 1558-1497
|2 ISSN
024 7 _ |a 10.34734/FZJ-2024-05303
|2 datacite_doi
024 7 _ |a 39208715
|2 pmid
024 7 _ |a WOS:001316490600001
|2 WOS
037 _ _ |a FZJ-2024-05303
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Beer, Simone
|0 P:(DE-Juel1)133864
|b 0
|e Corresponding author
|u fzj
245 _ _ |a Explainable artificial intelligence identifies an AQP4 polymorphism-based risk score associated with brain amyloid burden
260 _ _ |a Amsterdam [u.a.]
|c 2024
|b Elsevier Science
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1737639185_31172
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012), as well as by the A4 study (a4study.org).
520 _ _ |a Aquaporin-4 (AQP4) is hypothesized to be a component of the glymphatic system, a pathway for removing brain interstitial solutes like amyloid-β (Aβ). Evidence exists that genetic variation of AQP4 impacts Aβ clearance, clinical outcome in Alzheimer’s disease as well as sleep measures. We examined whether a risk score calculated from several AQP4 single-nucleotide polymorphisms (SNPs) is related to Aβ neuropathology in older cognitively unimpaired white individuals. We used a machine learning approach and explainable artificial intelligence to extract information on synergistic effects of AQP4 SNPs on brain amyloid burden from the ADNI cohort. From this information, we formulated a sex-specific AQP4 SNP-based risk score and evaluated it using data from the screening process of the A4 study. We found in both cohorts significant associations of the risk score with brain amyloid burden. The results support the hypothesis of an involvement of the glymphatic system, and particularly AQP4, in brain amyloid aggregation pathology. They suggest also that different AQP4 SNPs exert a synergistic effect on the build-up of brain amyloid burden.
536 _ _ |a 5253 - Neuroimaging (POF4-525)
|0 G:(DE-HGF)POF4-5253
|c POF4-525
|f POF IV
|x 0
536 _ _ |a 5252 - Brain Dysfunction and Plasticity (POF4-525)
|0 G:(DE-HGF)POF4-5252
|c POF4-525
|f POF IV
|x 1
536 _ _ |a 5254 - Neuroscientific Data Analytics and AI (POF4-525)
|0 G:(DE-HGF)POF4-5254
|c POF4-525
|f POF IV
|x 2
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Elmenhorst, David
|0 P:(DE-Juel1)131679
|b 1
|u fzj
700 1 _ |a Bischof, Gerard N.
|0 P:(DE-Juel1)166265
|b 2
|u fzj
700 1 _ |a Ramirez, Alfredo
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Bauer, Andreas
|0 P:(DE-Juel1)131672
|b 4
|u fzj
700 1 _ |a Drzezga, Alexander
|0 P:(DE-Juel1)177611
|b 5
|u fzj
773 _ _ |a 10.1016/j.neurobiolaging.2024.08.002
|g Vol. 143, p. 19 - 29
|0 PERI:(DE-600)1498414-3
|p 19 - 29
|t Neurobiology of aging
|v 143
|y 2024
|x 0197-4580
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/1030442/files/1-s2.0-S0197458024001337-main.pdf
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/1030442/files/AQP4%20interaction%20Post%20Print.pdf
856 4 _ |y OpenAccess
|x icon
|u https://juser.fz-juelich.de/record/1030442/files/AQP4%20interaction%20Post%20Print.gif?subformat=icon
856 4 _ |y OpenAccess
|x icon-1440
|u https://juser.fz-juelich.de/record/1030442/files/AQP4%20interaction%20Post%20Print.jpg?subformat=icon-1440
856 4 _ |y OpenAccess
|x icon-180
|u https://juser.fz-juelich.de/record/1030442/files/AQP4%20interaction%20Post%20Print.jpg?subformat=icon-180
856 4 _ |y OpenAccess
|x icon-640
|u https://juser.fz-juelich.de/record/1030442/files/AQP4%20interaction%20Post%20Print.jpg?subformat=icon-640
909 C O |o oai:juser.fz-juelich.de:1030442
|p openaire
|p open_access
|p OpenAPC_DEAL
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)133864
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)131679
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)166265
910 1 _ |a Universität Köln
|0 I:(DE-HGF)0
|b 3
|6 P:(DE-HGF)0
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)131672
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)177611
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5253
|x 0
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5252
|x 1
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5254
|x 2
914 1 _ |y 2024
915 p c |a APC keys set
|0 PC:(DE-HGF)0000
|2 APC
915 p c |a Local Funding
|0 PC:(DE-HGF)0001
|2 APC
915 p c |a DFG OA Publikationskosten
|0 PC:(DE-HGF)0002
|2 APC
915 p c |a DEAL: Elsevier 09/01/2023
|0 PC:(DE-HGF)0125
|2 APC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2023-08-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2023-08-24
915 _ _ |a Creative Commons Attribution-NonCommercial CC BY-NC 4.0
|0 LIC:(DE-HGF)CCBYNC4
|2 HGFVOC
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2023-08-24
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2024-12-13
|w ger
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NEUROBIOL AGING : 2022
|d 2024-12-13
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-13
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-13
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2024-12-13
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2024-12-13
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-13
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2024-12-13
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2024-12-13
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-13
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2024-12-13
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-2-20090406
|k INM-2
|l Molekulare Organisation des Gehirns
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-2-20090406
980 _ _ |a APC
980 1 _ |a APC
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21