RRC ID 81960
Author Miller SR, Luxem K, Lauderdale K, Nambiar P, Honma PS, Ly KK, Bangera S, Bullock M, Shin J, Kaliss N, Qiu Y, Cai C, Shen K, Mallen KD, Yan Z, Mendiola AS, Saito T, Saido TC, Pico AR, Thomas R, Roberson ED, Akassoglou K, Bauer P, Remy S, Palop JJ.
Title Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.
Journal Cell Rep
Abstract 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.
Volume 43(11)
Pages 114870
Published 2024-10-19
DOI 10.1016/j.celrep.2024.114870
PII S2211-1247(24)01221-X
PMID 39427315
Resource
Mice RBRC06344