Deep learning quantification of mouse nesting behavior for tracking cognitive decline in models of aging & Alzheimer’s disease

The aging brain and development of Alzheimer’s disease (AD) are characterized by neurological degeneration. Nest-building performance captures deteriorating cognitive and motor functions in rodent models of AD; albeit qualitative while limited in physiological scope. To enhance sampling depth and frequency, we developed an artificial intelligence (AI)-powered image analysis pipeline (“CozyScores”) built on the ResNet18 architecture.

In male & female C57BL6/J wild-type (WT) & 3xTg-AD mice (AD) mice [Young, 3–6 mo; WT, n = 28; AD, n = 32) & (Old, 21–26 mo; WT, n = 20; AD, n = 12)], we monitored progressive nest organization using photo acquisition across timepoints (0.5 to 24 h) relative to baseline on a 5-point scoring system. At the 4 h benchmark timepoint, a significant (P Young AD (3.16 ± 0.24) > Old WT (3.08 ± 0.32) > Old AD (1.71 ± 0.14)]. At 24 h, maximum performance scores declined in Old (WT: 4.24 ± 0.13; AD: 3.59 ± 0.35) versus Young (WT: 4.60 ± 0.05; AD: 4.33 ± 0.13) groups.

Males generally performed better than females throughout groups, particularly during nest construction (2 to 8 h). Overall, CozyScores is an automated, high-resolution analysis of behavioral kinetics in mouse models of aging and AD. The authors wish to thank Apprenticeship to Bridge College (ABC) student Kevin Nguyen, Undergraduate Training Program (UTP) student Robert Lister, and Ankita Hooda for technical assistance.

This work was supported by the National Institutes of Health (NIH) grant R01AG073230. The content of this original article is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. These authors contributed equally: Fritz E.

Department of Basic Sciences, Loma Linda University, 11041 Campus Street, Risley Hall, Loma Linda, CA, 92350, USA Fritz E. Shih, Stephen Salloum, Phoebe P. Behringer The authors declare no competing interests.

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