MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands.
Neuroimage. 2012 Aug 15;62(2):891-901. doi: 10.1016/j.neuroimage.2012.02.020. Epub 2012 Feb 18.
Independent Component Analysis (ICA) is a computational technique for identifying hidden statistically independent sources from multivariate data. In its basic form, ICA decomposes a 2D data matrix (e.g. time × voxels) into separate components that have distinct characteristics. In FMRI it is used to identify hidden FMRI signals (such as activations). Since the first application of ICA to Functional Magnetic Resonance Imaging (FMRI) in 1998, this technique has developed into a powerful tool for data exploration in cognitive and clinical neurosciences. In this contribution to the commemorative issue 20 years of FMRI I will briefly describe the basic principles behind ICA, discuss the probabilistic extension to ICA and touch on what I think are some of the most notorious loose ends. Further, I will describe some of the most powerful 'killer' applications and finally share some thoughts on where I believe the most promising future developments will lie.
独立成分分析（ICA）是一种从多维数据中识别隐藏的统计独立源的计算技术。在其基本形式中，ICA 将二维数据矩阵（例如时间×体素）分解为具有不同特征的独立分量。在 fMRI 中，它用于识别隐藏的 fMRI 信号（如激活）。自 1998 年首次将 ICA 应用于功能磁共振成像（FMRI）以来，这项技术已发展成为认知和临床神经科学中数据探索的强大工具。在这篇为纪念 FMRI 20 周年的特刊上，我将简要描述 ICA 的基本原理，讨论 ICA 的概率扩展，并探讨我认为一些最臭名昭著的未解决问题。此外，我将描述一些最强大的“杀手”应用程序，最后分享一些我认为最有前途的未来发展方向的想法。
