Effective identification of Alzheimer’s disease in mouse models via deep learning and motion analysis
Spatial disorientation is an early symptom of Alzheimer’s disease (AD). Detecting this impairment effectively in animal models can provide valuable insights into the disease and reduce experimental burdens. We have developed a markerless motion analysis system (MMAS) using deep learning techniques f...
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Elsevier
2024-12-01
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| Series: | Heliyon |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024153844 |
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| author | Yuanhao Liang Zhongqing Sun Kin Chiu Yong Hu |
| author_facet | Yuanhao Liang Zhongqing Sun Kin Chiu Yong Hu |
| author_sort | Yuanhao Liang |
| collection | DOAJ |
| description | Spatial disorientation is an early symptom of Alzheimer’s disease (AD). Detecting this impairment effectively in animal models can provide valuable insights into the disease and reduce experimental burdens. We have developed a markerless motion analysis system (MMAS) using deep learning techniques for the Morris water maze test. This system allows for precise analysis of behaviors and body movements from video recordings. Using the MMAS, we identified unilateral head-turning and tail-wagging preferences in AD mice, which distinguished them from wild-type mice with greater accuracy than traditional behavioral parameters. Furthermore, the cumulative turning and wagging angles were linearly correlated with escape latency and cognitive scores, demonstrating comparable effectiveness in differentiating AD mice. These findings underscore the potential of motion analysis as an advanced method for improving the effectiveness, sensitivity, and interpretability of AD mouse identification, ultimately aiding in disease diagnosis and drug development. |
| format | Article |
| id | doaj-art-d30ab4f3a12545ff8d40215e9b5bf43f |
| institution | OA Journals |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-d30ab4f3a12545ff8d40215e9b5bf43f2025-08-20T02:38:09ZengElsevierHeliyon2405-84402024-12-011023e3935310.1016/j.heliyon.2024.e39353Effective identification of Alzheimer’s disease in mouse models via deep learning and motion analysisYuanhao Liang0Zhongqing Sun1Kin Chiu2Yong Hu3Department of Orthopaedics & Traumatology, School of Clinical Medicine, Li Kai Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, 518053, China; AI and Big Data Lab, The University of Hong Kong-Shenzhen Hospital, Shenzhen, G.D, 518053, ChinaDepartment of Neurology, Xijing Hospital, Fourth Military Medical University, Xi’an, 710032, China; Department of Ophthalmology, School of Clinical Medicine, Li Kai Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, ChinaDepartment of Ophthalmology, School of Clinical Medicine, Li Kai Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; State Key Lab of Brain and Cognitive Sciences, Li Kai Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Department of Psychology, The University of Hong Kong, Hong Kong SAR, China; Corresponding author. Department of Psychology, The University of Hong Kong, Room 409, Hong Kong Jockey Club Building for Interdisciplinary Research, 5 Sassoon Road, Pokfulam, SAR Hong Kong, China.Department of Orthopaedics & Traumatology, School of Clinical Medicine, Li Kai Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, 518053, China; AI and Big Data Lab, The University of Hong Kong-Shenzhen Hospital, Shenzhen, G.D, 518053, China; Corresponding author. Department of Orthopaedics & Traumatology, School of Clinical Medicine, Li Kai Shing Faculty of Medicine, The University of Hong Kong, SAR Hong Kong, China.Spatial disorientation is an early symptom of Alzheimer’s disease (AD). Detecting this impairment effectively in animal models can provide valuable insights into the disease and reduce experimental burdens. We have developed a markerless motion analysis system (MMAS) using deep learning techniques for the Morris water maze test. This system allows for precise analysis of behaviors and body movements from video recordings. Using the MMAS, we identified unilateral head-turning and tail-wagging preferences in AD mice, which distinguished them from wild-type mice with greater accuracy than traditional behavioral parameters. Furthermore, the cumulative turning and wagging angles were linearly correlated with escape latency and cognitive scores, demonstrating comparable effectiveness in differentiating AD mice. These findings underscore the potential of motion analysis as an advanced method for improving the effectiveness, sensitivity, and interpretability of AD mouse identification, ultimately aiding in disease diagnosis and drug development.http://www.sciencedirect.com/science/article/pii/S2405844024153844 |
| spellingShingle | Yuanhao Liang Zhongqing Sun Kin Chiu Yong Hu Effective identification of Alzheimer’s disease in mouse models via deep learning and motion analysis Heliyon |
| title | Effective identification of Alzheimer’s disease in mouse models via deep learning and motion analysis |
| title_full | Effective identification of Alzheimer’s disease in mouse models via deep learning and motion analysis |
| title_fullStr | Effective identification of Alzheimer’s disease in mouse models via deep learning and motion analysis |
| title_full_unstemmed | Effective identification of Alzheimer’s disease in mouse models via deep learning and motion analysis |
| title_short | Effective identification of Alzheimer’s disease in mouse models via deep learning and motion analysis |
| title_sort | effective identification of alzheimer s disease in mouse models via deep learning and motion analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024153844 |
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