Ensuring NHP welfare through in-cage cognitive testing and remote sensing of physiological parameters

NHP中的神经科学实验通常涉及一套在其他研究领域中很少找到的实验程序。在这个项目中，我们致力于系统地量化它们对NHP中动物福利的潜在影响，重点是液体控制、运动限制和由于长期参与实验而推测的累积严重程度。在神经科学研究中，我们已经朝着改善NHP福利的长期目标取得了重要进展。首先，通过测量圈养恒河猴每日的液体摄取量，我们发现个体内和个体间存在很大的变异性，并得出结论，简单的归一化模型(每动物体重的液体体积)不太适合描述恒河猴的液体消耗行为。其次，我们使用并改进了我们的自动化笼式测试系统，以演示在动物行为训练的某些阶段可能减少运动限制。第三，我们开发了一个数据库，用于登记来自不同实验室的动物长期的实验、行为和生理参数，以及跨实验室的标准化数据收集工具。第四，作为概念验证，我们量化了不同实验条件下唾液皮质醇的短期反应，并开发了一种用于采样长期头发皮质醇测量的SOP。对于更新项目，我们建议通过增加新的技术特征和先进的自动行为训练算法，使我们基于笼子的测试系统在界面和行为控制方面与传统的实验环境最相似。对于后者，我们将描述在笼子里接受认知任务训练的恒河猴在学习行为方面的变异性和共性。此外，我们将系统地收集我们数据库中几个实验室的生理参数，并对数据进行荟萃分析，以确定与福利相关的生物标记物。

Neuroscientific experiments in NHPs typically involve a set of experimental procedures rarely found in other areas of research. In this project we work towards a systematic quantification of their potential effects on animal welfare in NHPs, with a focus on fluid control, movement restraint, and putative cumulative severity due to long-term participation in experiments. We already made important progress towards our long-term goal of improving the welfare of NHPs in neuroscience research. First, by measuring the daily fluid uptake of captive rhesus monkeys, we show substantial intra- and inter-individual variability and conclude that simple normalization models (fluid volume per animal weight) are not well suited for describing fluid consumption behaviour in rhesus monkeys. Second, we used and advanced our automated cage-based testing system to demonstrate the possible reduction of movement restraints in certain phases of the behavioural training of animals. Third, we developed a database for registering experimental, behavioural and physiological parameters of animals from different labs over long periods of time with tools for standardized data collection across labs. Forth, as a proof-of-concept, we quantified short-term salivary cortisol responses in different exper-imental conditions and developed an SOP for sampling longer-term hair cortisol measures. For the renewal project, we propose to make our cage-based testing system most similar to conventional experimental settings regarding interfaces and behavioural control by adding new technical features and advanced algorithms for automated behavioural training. With the latter we will characterize the variability and commonalities in learning behaviour of rhesus monkeys trained on cognitive tasks in their cages. Further, we will systematically collect physiological parameters from several labs in our database and run meta-analyses on the data to identify welfare-relevant biomarkers.