Formisano Elia, De Martino Federico, Valente Giancarlo
Faculty of Psychology, Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.
Magn Reson Imaging. 2008 Sep;26(7):921-34. doi: 10.1016/j.mri.2008.01.052. Epub 2008 May 27.
Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.
机器学习和模式识别技术在功能磁共振成像（fMRI）数据分析中的应用越来越广泛。通过考虑在多个位置同时测量的大脑活动的完整空间模式，这些方法能够检测到一些细微的、并非严格局限于局部的效应，而这些效应在用单变量统计方法进行传统分析时可能会被忽略。在典型的fMRI应用中，模式识别算法“学习”大脑反应模式与以标签表示的受试者的感知、认知或行为状态之间的功能关系，该标签可以取离散值（分类）或连续值（回归）。然后，利用这种学习到的功能关系从新的数据集中预测未知的标签（“脑阅读”）。在本文中，我们描述了机器学习在fMRI中应用的数学基础。我们重点介绍两种方法，支持向量机和相关向量机，它们分别适用于fMRI模式的分类和回归。此外，通过几个例子和应用，我们阐述并讨论了在fMRI数据分析背景下使用机器学习算法所面临的方法学挑战。
