1. Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models
- Author
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Stephanie R. Miller, Kevin Luxem, Kelli Lauderdale, Pranav Nambiar, Patrick S. Honma, Katie K. Ly, Shreya Bangera, Mary Bullock, Jia Shin, Nick Kaliss, Yuechen Qiu, Catherine Cai, Kevin Shen, K. Dakota Mallen, Zhaoqi Yan, Andrew S. Mendiola, Takashi Saito, Takaomi C. Saido, Alexander R. Pico, Reuben Thomas, Erik D. Roberson, Katerina Akassoglou, Pavol Bauer, Stefan Remy, and Jorge J. Palop
- Subjects
CP: Neuroscience ,Biology (General) ,QH301-705.5 - Abstract
Summary: 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.
- Published
- 2024
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