College of Electrical Engineering, Sichuan University, No. 24 South Section One of Yihuan Road, Wuhou district, Chengdu 610065, China.
State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China.
Cereb Cortex. 2023 Jun 20;33(13):8122-8130. doi: 10.1093/cercor/bhad103.
Brain network analysis is an effective method to seek abnormalities in functional interactions for brain disorders such as autism spectrum disorder (ASD). Traditional studies of brain networks focus on the node-centric functional connectivity (nFC), ignoring interactions of edges to miss much information that facilitates diagnostic decisions. In this study, we present a protocol based on an edge-centric functional connectivity (eFC) approach, which significantly improves classification performance by utilizing the co-fluctuations information between the edges of brain regions compared with nFC to build the classification mode for ASD using the multi-site dataset Autism Brain Imaging Data Exchange I (ABIDE I). Our model results show that even using the traditional machine-learning classifier support vector machine (SVM) on the challenging ABIDE I dataset, relatively high performance is achieved: 96.41% of accuracy, 98.30% of sensitivity, and 94.25% of specificity. These promising results suggest that the eFC can be used to build a reliable machine-learning framework to diagnose mental disorders such as ASD and promote identifications of stable and effective biomarkers. This study provides an essential complementary perspective for understanding the neural mechanisms of ASD and may facilitate future investigations on early diagnosis of neuropsychiatric disorders.
脑网络分析是一种有效的方法，可以寻找自闭症谱系障碍（ASD）等脑部疾病功能相互作用的异常。传统的脑网络研究主要关注以节点为中心的功能连接（nFC），忽略了边缘的相互作用，从而错过了有助于诊断决策的大量信息。在本研究中，我们提出了一种基于边缘为中心的功能连接（eFC）方法的方案，与 nFC 相比，该方法利用脑区之间边缘的共波动信息，通过构建基于多站点数据集 Autism Brain Imaging Data Exchange I（ABIDE I）的 ASD 分类模式，显著提高了分类性能。我们的模型结果表明，即使在具有挑战性的 ABIDE I 数据集上使用传统的机器学习分类器支持向量机（SVM），也能实现较高的性能：准确率为 96.41%，敏感度为 98.30%，特异性为 94.25%。这些有希望的结果表明，eFC 可用于构建可靠的机器学习框架，以诊断 ASD 等精神疾病，并促进稳定有效的生物标志物的识别。这项研究为理解 ASD 的神经机制提供了一个重要的互补视角，并可能促进对神经精神疾病早期诊断的进一步研究。
