Machine learning classifiers and fMRI: a tutorial overview.
Machine learning classifiers and fMRI: a tutorial overview.
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DOI：
10.1016/j.neuroimage.2008.11.007
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发表时间：
2009-03
期刊：
影响因子：
5.7
通讯作者：
Botvinick M
中科院分区：
文献类型：
--
作者：
Pereira F;Mitchell T;Botvinick M
解释大脑图像实验需要分析复杂的多变量数据。近年来，一种越来越流行的分析方法是使用机器学习算法来训练分类器，以从fMRI数据中解码刺激，精神状态，行为和其他感兴趣的变量，从而显示数据包含有关它们的信息。在本教程概述中，我们回顾了使用这种方法所面临的一些关键选择，以及如何获得统计上显著的结果，并从案例研究中说明了每一点。此外，我们展示了如何，除了回答问题的“有关于感兴趣的变量的信息”（模式歧视），分类器可以用来解决其他类的问题，即“在哪里的信息”（模式本地化）和“如何编码的信息”（模式表征）。
Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of ‘is there information about a variable of interest’ (pattern discrimination), classifiers can be used to tackle other classes of question, namely ‘where is the information’ (pattern localization) and ‘how is that information encoded’ (pattern characterization).