Sagol Department of Neurobiology, University of Haifa, 3498838, Haifa, Israel.
The Integrated Brain and Behavior Research Center (IBBR), Faculty of Natural Sciences, University of Haifa, Mt. Carmel, 3498838, Haifa, Israel.
BMC Biol. 2022 Jul 12;20(1):159. doi: 10.1186/s12915-022-01299-y.
Various mammalian species emit ultrasonic vocalizations (USVs), which reflect their emotional state and mediate social interactions. USVs are usually analyzed by manual or semi-automated methodologies that categorize discrete USVs according to their structure in the frequency-time domains. This laborious analysis hinders the effective use of USVs as a readout for high-throughput analysis of behavioral changes in animals.
Here we present a novel automated open-source tool that utilizes a different approach towards USV analysis, termed TrackUSF. To validate TrackUSF, we analyzed calls from different animal species, namely mice, rats, and bats, recorded in various settings and compared the results with a manual analysis by a trained observer. We found that TrackUSF detected the majority of USVs, with less than 1% of false-positive detections. We then employed TrackUSF to analyze social vocalizations in Shank3-deficient rats, a rat model of autism, and revealed that these vocalizations exhibit a spectrum of deviations from appetitive calls towards aversive calls.
TrackUSF is a simple and easy-to-use system that may be used for a high-throughput comparison of ultrasonic vocalizations between groups of animals of any kind in any setting, with no prior assumptions.
各种哺乳动物都会发出超声波(USVs),这些声音反映了它们的情绪状态,并介导了它们的社交互动。USVs 通常通过手动或半自动的方法进行分析,这些方法根据它们在频域和时域中的结构对离散的 USVs 进行分类。这种繁琐的分析方式阻碍了 USVs 作为一种高通量分析动物行为变化的指标的有效应用。
在这里,我们提出了一种新颖的自动化开源工具,该工具采用了一种不同的 USV 分析方法,称为 TrackUSF。为了验证 TrackUSF,我们分析了来自不同物种的动物(包括老鼠、大鼠和蝙蝠)在不同环境下录制的叫声,并将结果与经过训练的观察者的手动分析进行了比较。我们发现 TrackUSF 能够检测到大部分的 USVs,假阳性检测率不到 1%。然后,我们使用 TrackUSF 分析了 Shank3 缺陷型大鼠(自闭症的大鼠模型)的社交叫声,结果显示这些叫声表现出从趋近性叫声到回避性叫声的一系列偏差。
TrackUSF 是一个简单易用的系统,可以用于在任何环境下对任何种类的动物群体的超声波进行高通量比较,而无需事先进行任何假设。