IABC: A Toolbox for Intelligent Analysis of Brain Connectivity.

IF 2.7 4区医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Neuroinformatics Pub Date : 2023-04-01 DOI:10.1007/s12021-022-09617-z

Yuhui Du, Yanshu Kong, Xingyu He

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引用次数: 2

Abstract

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.

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IABC:大脑连接智能分析工具箱。

脑功能网络和连通性在探索脑功能、认识大脑和揭示脑疾病机制方面发挥着重要作用。独立分量分析(ICA)是一种应用最广泛的数据驱动的脑功能网络/连通性提取方法。然而，在ICA中，由于组件顺序的随机性和选择最优组件数的困难，难以保证网络的可靠性/连通性。为了便于使用ICA分析脑功能网络和连通性，我们开发了一个名为脑连通性智能分析(IABC)的MATLAB工具箱。IABC结合了我们之前提出的群体信息引导的独立成分分析(GIG-ICA)、NeuroMark和分裂合并辅助可靠的独立成分分析(SMART ICA)方法，可以估计可靠的个体-受试者神经成像措施，以供进一步分析。用户输入有规律组织的多个被试的功能磁共振成像(fMRI)数据(如在脑成像数据结构(BIDS)中)，点击几个按钮设置参数后，IABC自动输出每个被试的脑功能网络及其相关的时间过程和功能网络连通性。所有这些神经影像学测量都有望为了解脑功能和区分脑疾病提供线索。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neuroinformatics 医学-计算机：跨学科应用

CiteScore

6.00

自引率

6.70%

发文量

审稿时长

3 months

期刊介绍： Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.