Home > Publications Database > Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models. > print

001     272860
005     20250127091444.0
024 7 _ |a 10.1016/j.celrep.2024.114870
|2 doi
024 7 _ |a pmid:39427315
|2 pmid
024 7 _ |a 2211-1247
|2 ISSN
024 7 _ |a 2639-1856
|2 ISSN
024 7 _ |a altmetric:169532029
|2 altmetric
037 _ _ |a DZNE-2024-01278
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Miller, Stephanie R
|b 0
245 _ _ |a Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.
260 _ _ |a [New York, NY]
|c 2024
|b Elsevier
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 1729851078_18936
|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
520 _ _ |a Computer-vision and machine-learning (ML) approaches are being developed to provide scalable, unbiased, and sensitive methods to assess mouse behavior. Here, we used the ML-based variational animal motion embedding (VAME) segmentation platform to assess spontaneous behavior in humanized App knockin and transgenic APP models of Alzheimer's disease (AD) and to test the role of AD-related neuroinflammation in these behavioral manifestations. We found marked alterations in spontaneous behavior in AppNL-G-F and 5xFAD mice, including age-dependent changes in motif utilization, disorganized behavioral sequences, increased transitions, and randomness. Notably, blocking fibrinogen-microglia interactions in 5xFAD-Fggγ390-396A mice largely prevented spontaneous behavioral alterations, indicating a key role for neuroinflammation. Thus, AD-related spontaneous behavioral alterations are prominent in knockin and transgenic models and sensitive to therapeutic interventions. VAME outcomes had higher specificity and sensitivity than conventional behavioral outcomes. We conclude that spontaneous behavior effectively captures age- and sex-dependent disease manifestations and treatment efficacy in AD models.
536 _ _ |a 351 - Brain Function (POF4-351)
|0 G:(DE-HGF)POF4-351
|c POF4-351
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: pub.dzne.de
650 _ 7 |a App-KI
|2 Other
650 _ 7 |a CP: Neuroscience
|2 Other
650 _ 7 |a DeepLabCut
|2 Other
650 _ 7 |a Keypoint-MoSeq
|2 Other
650 _ 7 |a amyloid
|2 Other
650 _ 7 |a behavioral segmentation
|2 Other
650 _ 7 |a cognition
|2 Other
650 _ 7 |a naturalistic behavior
|2 Other
650 _ 7 |a open field
|2 Other
650 _ 7 |a pose estimation
|2 Other
650 _ 7 |a preclinical
|2 Other
650 _ 2 |a Alzheimer Disease: pathology
|2 MeSH
650 _ 2 |a Alzheimer Disease: genetics
|2 MeSH
650 _ 2 |a Animals
|2 MeSH
650 _ 2 |a Machine Learning
|2 MeSH
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Mice, Transgenic
|2 MeSH
650 _ 2 |a Disease Models, Animal
|2 MeSH
650 _ 2 |a Mice
|2 MeSH
650 _ 2 |a Behavior, Animal
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Treatment Outcome
|2 MeSH
650 _ 2 |a Amyloid beta-Protein Precursor: genetics
|2 MeSH
650 _ 2 |a Amyloid beta-Protein Precursor: metabolism
|2 MeSH
650 _ 2 |a Mice, Inbred C57BL
|2 MeSH
700 1 _ |a Luxem, Kevin
|0 P:(DE-2719)2812453
|b 1
|u dzne
700 1 _ |a Lauderdale, Kelli
|b 2
700 1 _ |a Nambiar, Pranav
|b 3
700 1 _ |a Honma, Patrick S
|b 4
700 1 _ |a Ly, Katie K
|b 5
700 1 _ |a Bangera, Shreya
|b 6
700 1 _ |a Bullock, Mary
|b 7
700 1 _ |a Shin, Jia
|b 8
700 1 _ |a Kaliss, Nick
|b 9
700 1 _ |a Qiu, Yuechen
|b 10
700 1 _ |a Cai, Catherine
|b 11
700 1 _ |a Shen, Kevin
|b 12
700 1 _ |a Mallen, K Dakota
|b 13
700 1 _ |a Yan, Zhaoqi
|b 14
700 1 _ |a Mendiola, Andrew S
|b 15
700 1 _ |a Saito, Takashi
|b 16
700 1 _ |a Saido, Takaomi C
|b 17
700 1 _ |a Pico, Alexander R
|b 18
700 1 _ |a Thomas, Reuben
|b 19
700 1 _ |a Roberson, Erik D
|b 20
700 1 _ |a Akassoglou, Katerina
|b 21
700 1 _ |a Bauer, Pavol
|0 P:(DE-2719)2812543
|b 22
|u dzne
700 1 _ |a Remy, Stefan
|0 P:(DE-2719)2810375
|b 23
|u dzne
700 1 _ |a Palop, Jorge J
|b 24
773 _ _ |a 10.1016/j.celrep.2024.114870
|g Vol. 43, no. 11, p. 114870 -
|0 PERI:(DE-600)2649101-1
|n 11
|p 114870
|t Cell reports
|v 43
|y 2024
|x 2211-1247
856 4 _ |y OpenAccess
|u https://pub.dzne.de/record/272860/files/DZNE-2024-01278.pdf
856 4 _ |y OpenAccess
|x pdfa
|u https://pub.dzne.de/record/272860/files/DZNE-2024-01278.pdf?subformat=pdfa
909 C O |o oai:pub.dzne.de:272860
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
|k DZNE
|b 1
|6 P:(DE-2719)2812453
910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
|k DZNE
|b 22
|6 P:(DE-2719)2812543
910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
|k DZNE
|b 23
|6 P:(DE-2719)2810375
913 1 _ |a DE-HGF
|b Gesundheit
|l Neurodegenerative Diseases
|1 G:(DE-HGF)POF4-350
|0 G:(DE-HGF)POF4-351
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Brain Function
|x 0
914 1 _ |y 2024
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2023-10-26
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
|0 LIC:(DE-HGF)CCBYNCND4
|2 HGFVOC
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b CELL REP : 2022
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2023-05-02T08:49:39Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2023-05-02T08:49:39Z
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2023-10-26
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-10-26
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2023-05-02T08:49:39Z
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2023-10-26
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b CELL REP : 2022
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-10-26
920 1 _ |0 I:(DE-2719)1013006
|k AG Remy
|l Neuronal Networks
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-2719)1013006
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21