In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k{k} -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.

中文翻译：

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使用基于 fNIRS 的动态功能连接检测情绪敏感性


在这项研究中，我们提出了一个分析框架，通过结合使用无监督和监督机器学习技术，将基于任务的动态功能连接（FC）特征识别为护生情绪敏感性的新生物标志物。动态 FC 通过功能近红外光谱 (fNIRS) 进行测量，并使用滑动窗口相关 (SWC) 分析进行计算。应用 k{k} 均值聚类技术来导出四种重复连接状态。这些状态通过图论和半度量分析来表征。提取发生概率和状态转换作为动态 FC 网络特征，并实现随机森林（RF）分类器来检测情绪敏感性。该方法在 39 名护生和 19 名注册护士的决策过程中进行了试验，我们假设注册护士已经制定了应对情绪敏感性的策略。情感刺激选自国际情感数字化声音系统（IADS）数据库。实验结果表明，注册护士表现出任务相关性的单一显性连接状态，而护生则表现出两种状态，并且具有较高水平的任务无关状态连接性。结果还表明，学生对情绪刺激更敏感，并且派生的动态 FC 特征作为情绪敏感度的生物标志物提供了比心率变异性更强的辨别力（准确度为 81.65% vs 71.03%）。这项工作构成了第一项证明基于 fNIRS 的动态 FC 态作为生物标志物稳定性的研究。 总之，结果支持动态FC的状态分布有助于揭示护生和注册护士在决策过程中的差异因素，并且预计生物标志物可以作为开展与情绪敏感度相关的专业培训时的指标。