深度学习在SNCA E46K帕金森病果蝇多尺度行为分析中的应用

IF 3.9 3区 工程技术 Q2 NEUROSCIENCES
Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-06-30 DOI:10.1007/s11571-025-10294-2
Keyi Dong, April Burch, Kang Huang
{"title":"深度学习在SNCA E46K帕金森病果蝇多尺度行为分析中的应用","authors":"Keyi Dong, April Burch, Kang Huang","doi":"10.1007/s11571-025-10294-2","DOIUrl":null,"url":null,"abstract":"<p><p><i>Drosophila melanogaster</i> is widely used as a model organism in Parkinson's disease research. However, due to the complexity of motion capture and the challenges of quantitatively assessing spontaneous behavior in <i>Drosophila melanogaster</i>, it remains technically difficult to identify symptoms of Parkinson's disease within <i>Drosophila</i> based on objective spontaneous behavioral characteristics. Here, we present an automated multi-scale behavioral phenotyping pipeline that classifies phenotypes related to Parkinson's disease using motion features extracted from pose estimation data of wild-type and Synuclein Alpha E46K mutant <i>Drosophila melanogaster</i>. Locomotor activity was recorded in a custom-designed 3D-printed behavioral trap, and body kinematics were analyzed using a markerless pose estimation tool to extract numerical features such as movement speed, tremor-like oscillations, and limb motion patterns. Beyond kinematic analysis, we applied unsupervised clustering to the pose-derived trajectories to extract recurrent movement subtypes that characterize spontaneous behavioral sequences. We found that kinematic features alone were insufficient to distinguish mutant flies from normal individuals, whereas behavioral sequence patterns captured through unsupervised clustering enabled robust group separation. Combining both feature types further enhanced classification accuracy, with the best model achieving 85%. This system provides an objective and scalable approach for analyzing behavior related to Parkinson's disease in <i>Drosophila melanogaster</i>, with potential applications in monitoring disease progression and screening pharmaceutical compounds.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"105"},"PeriodicalIF":3.9000,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209173/pdf/","citationCount":"0","resultStr":"{\"title\":\"Application of deep learning for multi-scale behavioral analysis in SNCA E46K Parkinson's disease drosophila.\",\"authors\":\"Keyi Dong, April Burch, Kang Huang\",\"doi\":\"10.1007/s11571-025-10294-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Drosophila melanogaster</i> is widely used as a model organism in Parkinson's disease research. However, due to the complexity of motion capture and the challenges of quantitatively assessing spontaneous behavior in <i>Drosophila melanogaster</i>, it remains technically difficult to identify symptoms of Parkinson's disease within <i>Drosophila</i> based on objective spontaneous behavioral characteristics. Here, we present an automated multi-scale behavioral phenotyping pipeline that classifies phenotypes related to Parkinson's disease using motion features extracted from pose estimation data of wild-type and Synuclein Alpha E46K mutant <i>Drosophila melanogaster</i>. Locomotor activity was recorded in a custom-designed 3D-printed behavioral trap, and body kinematics were analyzed using a markerless pose estimation tool to extract numerical features such as movement speed, tremor-like oscillations, and limb motion patterns. Beyond kinematic analysis, we applied unsupervised clustering to the pose-derived trajectories to extract recurrent movement subtypes that characterize spontaneous behavioral sequences. We found that kinematic features alone were insufficient to distinguish mutant flies from normal individuals, whereas behavioral sequence patterns captured through unsupervised clustering enabled robust group separation. Combining both feature types further enhanced classification accuracy, with the best model achieving 85%. This system provides an objective and scalable approach for analyzing behavior related to Parkinson's disease in <i>Drosophila melanogaster</i>, with potential applications in monitoring disease progression and screening pharmaceutical compounds.</p>\",\"PeriodicalId\":10500,\"journal\":{\"name\":\"Cognitive Neurodynamics\",\"volume\":\"19 1\",\"pages\":\"105\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209173/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Neurodynamics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s11571-025-10294-2\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/6/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Neurodynamics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11571-025-10294-2","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/30 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

黑腹果蝇作为一种模式生物被广泛应用于帕金森病的研究。然而,由于动作捕捉的复杂性和定量评估黑腹果蝇自发行为的挑战,基于客观自发行为特征识别果蝇帕金森病症状在技术上仍然存在困难。在这里,我们提出了一个自动化的多尺度行为表型管道,该管道使用从野生型和Synuclein Alpha E46K突变果蝇的姿势估计数据中提取的运动特征对帕金森病相关的表型进行分类。在定制设计的3d打印行为陷阱中记录运动活动,并使用无标记姿态估计工具分析身体运动学,以提取运动速度,震颤样振荡和肢体运动模式等数值特征。除了运动学分析之外,我们还将无监督聚类应用于姿态衍生轨迹,以提取表征自发行为序列的反复运动亚型。我们发现单独的运动学特征不足以区分突变果蝇和正常个体,而通过无监督聚类捕获的行为序列模式可以实现稳健的群体分离。结合两种特征类型进一步提高了分类准确率,最佳模型达到85%。该系统为分析果蝇帕金森病相关行为提供了一种客观、可扩展的方法,在监测疾病进展和筛选药物化合物方面具有潜在的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of deep learning for multi-scale behavioral analysis in SNCA E46K Parkinson's disease drosophila.

Drosophila melanogaster is widely used as a model organism in Parkinson's disease research. However, due to the complexity of motion capture and the challenges of quantitatively assessing spontaneous behavior in Drosophila melanogaster, it remains technically difficult to identify symptoms of Parkinson's disease within Drosophila based on objective spontaneous behavioral characteristics. Here, we present an automated multi-scale behavioral phenotyping pipeline that classifies phenotypes related to Parkinson's disease using motion features extracted from pose estimation data of wild-type and Synuclein Alpha E46K mutant Drosophila melanogaster. Locomotor activity was recorded in a custom-designed 3D-printed behavioral trap, and body kinematics were analyzed using a markerless pose estimation tool to extract numerical features such as movement speed, tremor-like oscillations, and limb motion patterns. Beyond kinematic analysis, we applied unsupervised clustering to the pose-derived trajectories to extract recurrent movement subtypes that characterize spontaneous behavioral sequences. We found that kinematic features alone were insufficient to distinguish mutant flies from normal individuals, whereas behavioral sequence patterns captured through unsupervised clustering enabled robust group separation. Combining both feature types further enhanced classification accuracy, with the best model achieving 85%. This system provides an objective and scalable approach for analyzing behavior related to Parkinson's disease in Drosophila melanogaster, with potential applications in monitoring disease progression and screening pharmaceutical compounds.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信