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000272860 1001_ $$aMiller, Stephanie R$$b0
000272860 245__ $$aMachine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.
000272860 260__ $$a[New York, NY]$$bElsevier$$c2024
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000272860 520__ $$aComputer-vision and machine-learning (ML) approaches are being developed to provide scalable, unbiased, and sensitive methods to assess mouse behavior. Here, we used the ML-based variational animal motion embedding (VAME) segmentation platform to assess spontaneous behavior in humanized App knockin and transgenic APP models of Alzheimer's disease (AD) and to test the role of AD-related neuroinflammation in these behavioral manifestations. We found marked alterations in spontaneous behavior in AppNL-G-F and 5xFAD mice, including age-dependent changes in motif utilization, disorganized behavioral sequences, increased transitions, and randomness. Notably, blocking fibrinogen-microglia interactions in 5xFAD-Fggγ390-396A mice largely prevented spontaneous behavioral alterations, indicating a key role for neuroinflammation. Thus, AD-related spontaneous behavioral alterations are prominent in knockin and transgenic models and sensitive to therapeutic interventions. VAME outcomes had higher specificity and sensitivity than conventional behavioral outcomes. We conclude that spontaneous behavior effectively captures age- and sex-dependent disease manifestations and treatment efficacy in AD models.
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000272860 650_7 $$2Other$$aApp-KI
000272860 650_7 $$2Other$$aCP: Neuroscience
000272860 650_7 $$2Other$$aDeepLabCut
000272860 650_7 $$2Other$$aKeypoint-MoSeq
000272860 650_7 $$2Other$$aamyloid
000272860 650_7 $$2Other$$abehavioral segmentation
000272860 650_7 $$2Other$$acognition
000272860 650_7 $$2Other$$anaturalistic behavior
000272860 650_7 $$2Other$$aopen field
000272860 650_7 $$2Other$$apose estimation
000272860 650_7 $$2Other$$apreclinical
000272860 650_2 $$2MeSH$$aAlzheimer Disease: pathology
000272860 650_2 $$2MeSH$$aAlzheimer Disease: genetics
000272860 650_2 $$2MeSH$$aAnimals
000272860 650_2 $$2MeSH$$aMachine Learning
000272860 650_2 $$2MeSH$$aHumans
000272860 650_2 $$2MeSH$$aMice, Transgenic
000272860 650_2 $$2MeSH$$aDisease Models, Animal
000272860 650_2 $$2MeSH$$aMice
000272860 650_2 $$2MeSH$$aBehavior, Animal
000272860 650_2 $$2MeSH$$aMale
000272860 650_2 $$2MeSH$$aFemale
000272860 650_2 $$2MeSH$$aTreatment Outcome
000272860 650_2 $$2MeSH$$aAmyloid beta-Protein Precursor: genetics
000272860 650_2 $$2MeSH$$aAmyloid beta-Protein Precursor: metabolism
000272860 650_2 $$2MeSH$$aMice, Inbred C57BL
000272860 7001_ $$0P:(DE-2719)2812453$$aLuxem, Kevin$$b1$$udzne
000272860 7001_ $$aLauderdale, Kelli$$b2
000272860 7001_ $$aNambiar, Pranav$$b3
000272860 7001_ $$aHonma, Patrick S$$b4
000272860 7001_ $$aLy, Katie K$$b5
000272860 7001_ $$aBangera, Shreya$$b6
000272860 7001_ $$aBullock, Mary$$b7
000272860 7001_ $$aShin, Jia$$b8
000272860 7001_ $$aKaliss, Nick$$b9
000272860 7001_ $$aQiu, Yuechen$$b10
000272860 7001_ $$aCai, Catherine$$b11
000272860 7001_ $$aShen, Kevin$$b12
000272860 7001_ $$aMallen, K Dakota$$b13
000272860 7001_ $$aYan, Zhaoqi$$b14
000272860 7001_ $$aMendiola, Andrew S$$b15
000272860 7001_ $$aSaito, Takashi$$b16
000272860 7001_ $$aSaido, Takaomi C$$b17
000272860 7001_ $$aPico, Alexander R$$b18
000272860 7001_ $$aThomas, Reuben$$b19
000272860 7001_ $$aRoberson, Erik D$$b20
000272860 7001_ $$aAkassoglou, Katerina$$b21
000272860 7001_ $$0P:(DE-2719)2812543$$aBauer, Pavol$$b22$$udzne
000272860 7001_ $$0P:(DE-2719)2810375$$aRemy, Stefan$$b23$$udzne
000272860 7001_ $$aPalop, Jorge J$$b24
000272860 773__ $$0PERI:(DE-600)2649101-1$$a10.1016/j.celrep.2024.114870$$gVol. 43, no. 11, p. 114870 -$$n11$$p114870$$tCell reports$$v43$$x2211-1247$$y2024
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