2012年8月10日 讯 /生物谷BIOON/ --近日，来自德国柏林自由大学和西班牙基因组研究中心的研究者设计出了一款开放源的软件，其可以在行为实验中跟踪果蝇及它们幼虫的足迹，从而理解人类大脑决策的制定以及神经系统的相关功能，相关研究成果刊登在了国际杂志PLoS One上。
此前，许多科学家依赖于昂贵的商业软件来研究果蝇成体和幼虫的行为；如今研究者开发出了这款新型软件，这款软件写入了简单的编程语言，其可以促进科学家来研究果蝇的行为。果蝇是一种很好的研究大脑功能的模型，通过仔细观察果蝇是否左转还是右转，我们希望更深入地理解人类如何进行决策制定。
这款新型软件工具可以允许研究者不仅仅提高研究结果的准确率，以及开发出新的分析方法。研究者表示，他们很希望看到同行使用这款软件来帮助其进行科研。下一步研究者将使得软件数据在线可以获取，而且可以自动化分析。最终研究者希望包含不同实验的模板可以转化成电脑可读的格式，比如3D打印机可以重新创建精确的实验数据等。
最后，研究者Bjom表示，我们希望开发出最普通和便宜的软件，供任何人来进行科研实验。（生物谷Bioon.com）
编译自：Tracking Fruit Flies to Understand the Function of the Nervous System
doi:10.1371/journal.pone.0041642
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Automated Tracking of Animal Posture and Movement during Exploration and Sensory Orientation Behaviors
Alex Gomez-Marin1*, Nicolas Partoune1,2, Greg J. Stephens3, Matthieu Louis1*
Background The nervous functions of an organism are primarily reflected in the behavior it is capable of. Measuring behavior quantitatively, at high-resolution and in an automated fashion provides valuable information about the underlying neural circuit computation. Accordingly, computer-vision applications for animal tracking are becoming a key complementary toolkit to genetic, molecular and electrophysiological characterization in systems neuroscience. Methodology/Principal Findings We present Sensory Orientation Software (SOS) to measure behavior and infer sensory experience correlates. SOS is a simple and versatile system to track body posture and motion of single animals in two-dimensional environments. In the presence of a sensory landscape, tracking the trajectory of the animal's sensors and its postural evolution provides a quantitative framework to study sensorimotor integration. To illustrate the utility of SOS, we examine the orientation behavior of fruit fly larvae in response to odor, temperature and light gradients. We show that SOS is suitable to carry out high-resolution behavioral tracking for a wide range of organisms including flatworms, fishes and mice. Conclusions/Significance Our work contributes to the growing repertoire of behavioral analysis tools for collecting rich and fine-grained data to draw and test hypothesis about the functioning of the nervous system. By providing open-access to our code and documenting the software design, we aim to encourage the adaptation of SOS by a wide community of non-specialists to their particular model organism and questions of interest.
doi:10.1371/journal.pone.0042247
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Open Source Tracking and Analysis of Adult Drosophila Locomotion in Buridan's Paradigm with and without Visual Targets
Julien Colomb1*, Lutz Reiter1, Jedrzej Blaszkiewicz1, Jan Wessnitzer2, Bjoern Brembs1,3
Background Insects have been among the most widely used model systems for studying the control of locomotion by nervous systems. In Drosophila, we implemented a simple test for locomotion: in Buridan's paradigm, flies walk back and forth between two inaccessible visual targets [1]. Until today, the lack of easily accessible tools for tracking the fly position and analyzing its trajectory has probably contributed to the slow acceptance of Buridan's paradigm. Methodology/Principal Findings We present here a package of open source software designed to track a single animal walking in a homogenous environment (Buritrack) and to analyze its trajectory. The Centroid Trajectory Analysis (CeTrAn) software is coded in the open source statistics project R. It extracts eleven metrics and includes correlation analyses and a Principal Components Analysis (PCA). It was designed to be easily customized to personal requirements. In combination with inexpensive hardware, these tools can readily be used for teaching and research purposes. We demonstrate the capabilities of our package by measuring the locomotor behavior of adult Drosophila melanogaster (whose wings were clipped), either in the presence or in the absence of visual targets, and comparing the latter to different computer-generated data. The analysis of the trajectories confirms that flies are centrophobic and shows that inaccessible visual targets can alter the orientation of the flies without changing their overall patterns of activity. Conclusions/Significance Using computer generated data, the analysis software was tested, and chance values for some metrics (as well as chance value for their correlation) were set. Our results prompt the hypothesis that fixation behavior is observed only if negative phototaxis can overcome the propensity of the flies to avoid the center of the platform. Together with our companion paper, we provide new tools to promote Open Science as well as the collection and analysis of digital behavioral data.
