Machine learning classifiers and fMRI: A tutorial overview

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 f...

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Bibliographic Details
Published inNeuroImage (Orlando, Fla.) Vol. 45; no. 1; pp. S199 - S209
Main Authors Pereira, Francisco, Mitchell, Tom, Botvinick, Matthew
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.03.2009
Elsevier Limited
Subjects
Accuracy
Algorithms
Artificial Intelligence
Classification
Conflicts of interest
Humans
Image Processing, Computer-Assisted - methods
Magnetic Resonance Imaging
Medical imaging
Methods
Software
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Summary: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).
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ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2008.11.007