Du Yuhui, Kong Yanshu, He Xingyu
School of Computer and Information Technology, Shanxi University, Taiyuan, China.
Neuroinformatics. 2023 Apr;21(2):303-321. doi: 10.1007/s12021-022-09617-z. Epub 2023 Jan 7.
Brain functional networks and connectivity have played an important role in exploring brain function for understanding the brain and disclosing the mechanisms of brain disorders. Independent component analysis (ICA) is one of the most widely applied data-driven methods to extract brain functional networks/connectivity. However, it is hard to guarantee the reliability of networks/connectivity due to the randomness of component order and the difficulty in selecting an optimal component number in ICA. To facilitate the analysis of brain functional networks and connectivity using ICA, we developed a MATLAB toolbox called Intelligent Analysis of Brain Connectivity (IABC). IABC incorporates our previously proposed group information guided independent component analysis (GIG-ICA), NeuroMark, and splitting-merging assisted reliable ICA (SMART ICA) methods, which can estimate reliable individual-subject neuroimaging measures for further analysis. After user inputs functional magnetic resonance imaging (fMRI) data of multiple subjects that are regularly organized (e.g., in Brain Imaging Data Structure (BIDS)) and clicks a few buttons to set parameters, IABC automatically outputs brain functional networks, their related time courses, and functional network connectivity of each subject. All these neuroimaging measures are promising for providing clues in understanding brain function and differentiating brain disorders.
脑功能网络和连接性在探索脑功能以理解大脑和揭示脑部疾病机制方面发挥了重要作用。独立成分分析（ICA）是提取脑功能网络/连接性应用最广泛的数据驱动方法之一。然而，由于成分顺序的随机性以及在ICA中选择最佳成分数量的困难，很难保证网络/连接性的可靠性。为便于使用ICA分析脑功能网络和连接性，我们开发了一个名为脑连接性智能分析（IABC）的MATLAB工具箱。IABC整合了我们之前提出的组信息引导独立成分分析（GIG - ICA）、NeuroMark以及分裂合并辅助可靠ICA（SMART ICA）方法，这些方法可以估计可靠的个体受试者神经影像测量值以供进一步分析。在用户输入多个受试者定期组织的（例如，脑成像数据结构（BIDS）中的）功能磁共振成像（fMRI）数据并点击几个按钮设置参数后，IABC会自动输出每个受试者的脑功能网络、其相关的时间历程以及功能网络连接性。所有这些神经影像测量值都有望为理解脑功能和鉴别脑部疾病提供线索。
