IEEE Trans Neural Syst Rehabil Eng. 2021;29:894-904. doi: 10.1109/TNSRE.2021.3078460. Epub 2021 May 20.
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 -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.
在这项研究中，我们提出了一种分析框架，通过结合无监督和监督机器学习技术，将动态任务型功能连接（FC）特征识别为护理专业学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）测量，并使用滑动窗口相关（SWC）分析计算。应用 k-均值聚类技术得出四个反复出现的连通状态。这些状态通过图论和半度量分析进行特征描述。出现概率和状态转换被提取为动态 FC 网络特征，并实施随机森林（RF）分类器来检测情绪敏感性。该方法在 39 名护理专业学生和 19 名注册护士进行决策时进行了试用，我们假设注册护士已经制定了应对情绪敏感性的策略。情绪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，注册护士表现出单一主导的任务相关性连通状态，而护理学生则表现出两种状态，且任务无关状态的连通性水平更高。结果还表明，学生更容易受到情绪刺激的影响，并且衍生的动态 FC 特征比心率变异性（准确性为 81.65%比 71.03%）作为情绪敏感性的生物标志物具有更强的区分能力。这项工作首次证明了基于 fNIRS 的动态 FC 状态作为生物标志物的稳定性。总之，结果支持动态 FC 的状态分布可以帮助揭示护理专业学生和注册护士在决策过程中的区别因素，并预计这些生物标志物可能用于开发与情绪敏感性相关的专业培训的指标。
