Severity assessment in neuroscientific research: generalisability of multidimensional approaches and application to refinement

在资助阶段1和2，一套全面的行为，生化和遥测分析的生理参数已经在各种诱导和遗传的小鼠和大鼠神经儿科和神经疾病模型中进行了评估。跨年龄、跨模型、跨物种和跨性别验证提供了有关所选参数候选者的通用性的宝贵信息。在设计和验证测试电池时考虑了各自的信息，该测试电池允许评估年轻转基因小鼠的发育轨迹中的累积严重性。此外，我们进一步开发和优化了多维复合测量方案的参数选择和设计的生物信息学工作流程。以癫痫、神经发育和神经精神疾病模型为重点的案例研究表明，由此产生的多维方案将严重程度评估提高到一个新的敏感性水平，允许对痛苦程度进行精确分级的比较评估。多变量方法也已成功应用于验证神经科学研究中常见干预和技术的潜在改进措施（例如，优化的多模态镇痛；遥测与系留记录）。实验研究和系统评价的结果是为深入的实验室内部和总体实验室间分析选择最有希望的参数的基础，提供有关其信息价值、敏感性、可复制性、稳健性和通用性的进一步信息。沿着这些思路，多维复合测量方案设计的生物信息学工作流程将应用于来自不同合作伙伴的数据集，以确认其对不同动物模型的有效性并确定必要的调整。此外，我们将把RELSA应用于我们的数据集，以确认这种多维评分系统对持续严重性监测的有效性，并将支持针对特定研究设计的变体的开发。在循证改进方面，对实验室内和实验室间数据的分析以及多模式围手术期镇痛方案的常规使用将告知这些方案的普遍性。通过在多中心研究中使用传输器植入物和遗传小鼠模型记录的数据集，我们将有助于验证新的智能家居笼子系统。根据我们的研究结果和与利益相关者的讨论，我们将为基于证据的严重性评估和基于证据的改进措施的多变量方案的设计和应用提供建议。

During funding phase 1 and 2, a comprehensive set of behavioural, biochemical, and telemetrically-analysed physiological parameters has been assessed in various induced and genetic mouse and rat models of neuropediatric and neurological disorders. A cross-age, -model, -species and -sex validation provided valuable information about generalisability of selected parameter candidates. Respective information was considered for the design and validation of a test battery allowing assessment of cumulative severity across the developmental trajectory of young genetically-modified mice. Moreover, we have further developed, and optimised a bioinformatic workflow for parameter selection and design of multidimensional composite measure schemes. Case studies, focussed on models of epilepsy, neurodevelopmental and neuropsychiatric disorders, demonstrated that the resulting multidimensional schemes take severity assessment to a new level of sensitivity allowing comparative assessment with precise grading of distress levels. Multivariate approaches have also been successfully applied to validate potential refinement measures (e.g., optimised multimodal analgesia; telemetric vs. tethered recordings) for common interventions and techniques in neuroscientific research. Findings from experimental studies and systematic reviews serve as a basis for selecting the most promising parameters for an in-depth intra-laboratory and an overarching inter-laboratory analysis, providing further information about their informative value, sensitivity, replicability, robustness, and generalisability. Along these lines, the bioinformatic workflow for the design of multidimensional composite measure schemes will be applied to datasets from different collaboration partners to confirm its validity for different animal models and identify necessary adaptations. In addition, we will apply RELSA to our data sets to confirm the validity of this multidimensional scoring system for continuous severity monitoring and will support the development of variants for specific study designs. In terms of evidence-based refinement, analysis of intra- and inter-laboratory data and routine use of multimodal perioperative analgesic regimens will inform about the generalisability of such regimens. With data sets recorded during a multicentre study with transmitter implants and from a genetic mouse model, we will contribute to the validation of a new smart home cage system. Based on our findings and discussions with stakeholders, we will provide recommendations for the design and application of multivariate schemes for evidence-based severity assessment and for evidence-based refinement measures.