TY  - JOUR
AU  - Miller, Stephanie R
AU  - Luxem, Kevin
AU  - Lauderdale, Kelli
AU  - Nambiar, Pranav
AU  - Honma, Patrick S
AU  - Ly, Katie K
AU  - Bangera, Shreya
AU  - Bullock, Mary
AU  - Shin, Jia
AU  - Kaliss, Nick
AU  - Qiu, Yuechen
AU  - Cai, Catherine
AU  - Shen, Kevin
AU  - Mallen, K Dakota
AU  - Yan, Zhaoqi
AU  - Mendiola, Andrew S
AU  - Saito, Takashi
AU  - Saido, Takaomi C
AU  - Pico, Alexander R
AU  - Thomas, Reuben
AU  - Roberson, Erik D
AU  - Akassoglou, Katerina
AU  - Bauer, Pavol
AU  - Remy, Stefan
AU  - Palop, Jorge J
TI  - Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.
JO  - Cell reports
VL  - 43
IS  - 11
SN  - 2211-1247
CY  - [New York, NY]
PB  - Elsevier
M1  - DZNE-2024-01278
SP  - 114870
PY  - 2024
AB  - 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.
KW  - Alzheimer Disease: pathology
KW  - Alzheimer Disease: genetics
KW  - Animals
KW  - Machine Learning
KW  - Humans
KW  - Mice, Transgenic
KW  - Disease Models, Animal
KW  - Mice
KW  - Behavior, Animal
KW  - Male
KW  - Female
KW  - Treatment Outcome
KW  - Amyloid beta-Protein Precursor: genetics
KW  - Amyloid beta-Protein Precursor: metabolism
KW  - Mice, Inbred C57BL
KW  - App-KI (Other)
KW  - CP: Neuroscience (Other)
KW  - DeepLabCut (Other)
KW  - Keypoint-MoSeq (Other)
KW  - amyloid (Other)
KW  - behavioral segmentation (Other)
KW  - cognition (Other)
KW  - naturalistic behavior (Other)
KW  - open field (Other)
KW  - pose estimation (Other)
KW  - preclinical (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:39427315
DO  - DOI:10.1016/j.celrep.2024.114870
UR  - https://pub.dzne.de/record/272860
ER  -