Wang Huan, Zhu Rongxin, Tian Shui, Shao Junneng, Dai Zhongpeng, Xue Li, Sun Yurong, Chen Zhilu, Yao Zhijian, Lu Qing
School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China.
Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China.
Cogn Neurodyn. 2023 Dec;17(6):1609-1619. doi: 10.1007/s11571-022-09907-x. Epub 2022 Dec 3.
The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients.
The online version contains supplementary material available at 10.1007/s11571-022-09907-x.
双相情感障碍（BD）的诊断主要依赖于临床病史和行为观察，而仅使用临床工具往往会限制诊断准确性。本研究旨在利用动态最小生成树（MST）中的多层模块化创建一种新型的BD诊断框架。我们收集了45名未用药的BD患者和47名健康对照（HC）。采用滑动窗口方法通过静息态功能磁共振成像（fMRI）数据构建动态MST。首先，我们使用三种空模型来探索动态MST中多层模块化的有效性。此外，将从动态MST中提取的模块忠诚度应用于训练分类器以区分BD患者。最后，我们探讨了功能连接（FC）估计器和MST尺度对模型性能的影响。研究结果表明，动态MST中的多层模块化在人类大脑中不是一个随机过程。该模型识别BD患者的准确率达到83.70%。此外，我们发现默认模式网络、皮层下网络（SubC）和注意力网络在分类中起关键作用。这些发现表明，动态MST中的多层模块化可以突出HC和BD患者之间的差异，为BD患者开辟了一种新的诊断工具。
在线版本包含可在10.1007/s11571-022-09907-x获取的补充材料。
