Jafari Samira, Sharini Hamid, Foroughi Aliakbar, Almasi Afshin
Modeling in Health Research Center Institute for Futures Studies in Health Kerman University of Medical Sciences, Kerman, Iran.
Department of Biomedical Engineering, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Brain Res Bull. 2025 Feb;221:111205. doi: 10.1016/j.brainresbull.2025.111205. Epub 2025 Jan 9.
Due to the importance and the consequences of anxiety, the goals of the current study are brain mapping, biomarker identification and the use of an assessment method for diagnosis of anxiety during emotional face in preschool children.
45 preschool children participated in this study. Functional Magnetic Resonance Imaging (fMRI) data were taken in fearful and angry conditions. The functional connectivity (FC) for the limbic system were extracted by ROI-to-ROI method. The fMRI biomarkers (FC) were given to machine learning models as input features to diagnose anxiety in children for angry and fearful conditions.
The results of the brain mapping comparisons between anxiety and the non-anxiety showed that there was an increased FC between medial prefrontal cortex (MPFC) and right lateral amygdala (RLA) and a decreased FC between left anterior hippocampus (LAH) and left posterior hippocampus (LPH) in the angry condition. There was an increased FC between the pairs of regions, RLA- right anterior hippocampus (RAH), MPFC-LPH, and RAH-LPH in fearful condition. It is possible to use the FC between LAH- right medial amygdala (RMA) and the FC between left medial amygdala (LMA)-RMA, LMA-RLA, LMA-RAH, and left lateral amygdala (LLA)-RLA instead of IQ in angry and fearful conditions, respectively. Based on metrics such as accuracy, recall, precision, and area under the receiver operating characteristic curve, the Logistic Lasso Regression model outperformed the other model in diagnosing anxiety.
With these findings, psychiatrists and psychologists can have a better understanding of the brain connectivity in children.
由于焦虑的重要性及其后果，本研究的目标是进行脑图谱绘制、生物标志物识别以及使用一种评估方法来诊断学龄前儿童在面对情绪面孔时的焦虑情况。
45名学龄前儿童参与了本研究。在恐惧和愤怒状态下采集功能磁共振成像（fMRI）数据。通过感兴趣区域到感兴趣区域的方法提取边缘系统的功能连接（FC）。将fMRI生物标志物（FC）作为输入特征输入到机器学习模型中，以诊断儿童在愤怒和恐惧状态下的焦虑情况。
焦虑组与非焦虑组的脑图谱比较结果显示，在愤怒状态下，内侧前额叶皮质（MPFC）与右侧杏仁核（RLA）之间的FC增加，左侧前海马体（LAH）与左侧后海马体（LPH）之间的FC减少。在恐惧状态下，RLA与右侧前海马体（RAH）、MPFC与LPH以及RAH与LPH之间的FC增加。在愤怒和恐惧状态下，分别可以使用LAH与右侧内侧杏仁核（RMA）之间的FC以及左侧内侧杏仁核（LMA）与RMA、LMA与RLA、LMA与RAH以及左侧外侧杏仁核（LLA）与RLA之间的FC来替代智商。基于准确率、召回率、精确率和受试者工作特征曲线下面积等指标，逻辑套索回归模型在诊断焦虑方面优于其他模型。
基于这些发现，精神科医生和心理学家可以更好地理解儿童的脑连接情况。
