This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: ⁴He, ¹⁶O, ²⁸Si, ⁴⁸Ti, or ⁵⁶Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in attentional set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of ²⁸Si or ⁵⁶Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of ⁵⁶Fe in the compound discrimination (CD) stage. On a subject-based level, implementing machine learning (ML) classifiers such as the Gaussian naïve Bayes, support vector machine, and artificial neural networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreen performance scores as multidimensional input features to predict each rodent’s susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when ⁵⁶Fe is the ion in question in both SD and CD stages. They, however, do not depict impairment due to ⁴He in SD and ²⁸Si in CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for more robust predictions of cognitive decrements due to space radiation exposure.

这项研究使用机器学习的计算分析来预测辐射引起的大鼠认知能力障碍。分析中的实验数据来自暴露于 ≤15 cGy 的单个银河宇宙辐射 (GCR) 离子的啮齿动物模型：⁴ He、¹⁶ O、²⁸ Si、⁴⁸ Ti 或⁵⁶Fe，预计用于月球或火星任务。这项工作在基于受试者的水平上调查大鼠，并使用辐照前的表现评分来预测辐照后注意力转移 (ATSET) 数据的损害。在这里，对照组中表现最差的大鼠通过累积分布函数根据群体分析定义损伤阈值，从而为每个受试者标记损伤。一个重要的发现是，在 ATSET 的简单鉴别 (SD) 阶段，1 到 10 cGy 的²⁸ Si 或⁵⁶ Fe 以及 1 到 10 cGy 的⁵⁶Fe 在复合歧视 (CD) 阶段。在基于学科的层面上，实施机器学习 (ML) 分类器，如高斯朴素贝叶斯、支持向量机和人工神经网络，可以识别出 GCR 暴露后具有更高损伤倾向的大鼠。该算法采用实验预筛选性能分数作为多维输入特征来预测每只啮齿动物对空间辐射暴露引起的认知障碍的易感性。当⁵⁶ Fe 是 SD 和 CD 阶段的相关离子时，ML 模型的接收器操作特性和精确召回曲线显示出更好的损伤预测。然而，它们并未描述由于SD 中的⁴ He 和²⁸CD 中的 Si，表明在这些情况下没有剂量依赖性损伤反应。我们研究的一个关键发现是预筛选性能分数可用于预测 ATSET 性能损伤。这一结果对载人航天任务具有重要意义，因为它支持通过实施经过适当训练的 ML 工具在航天飞行前预测宇航员在特定任务中受损的潜力。未来的研究可以专注于构建 ML 集成方法，以整合本研究中实施的方法的结果，以更可靠地预测由于空间辐射暴露引起的认知减退。