Traditional (
univariate) analysis of functional
MRI (
fMRI) data relies exclusively on the information contained in the time course of individual voxels.
Multivariate analyses can take advantage of the information contained in activity patterns across space, from multiple voxels. Such analyses have the potential to greatly expand the amount of information extracted from
fMRI data sets. In the present study,
multivariate statistical pattern recognition methods, including
linear discriminant analysis and
support vector machines, were used to classify patterns of
fMRI activation evoked by the
visual presentation of various categories of objects. Classifiers were trained using data from voxels in predefined regions of interest during a subset of trials for each subject individually. Classification of subsequently collected
fMRI data was attempted according to the similarity of
activation patterns to
prior training examples. Classification was done using only small amounts of data (20 s worth) at a time, so such a technique could, in principle, be used to extract information about a subject's
percept on a near real-time basis. Classifiers trained on data
acquired during one session were equally
accurate in classifying data collected within the same session and across sessions separated by more than a week, in the same subject. Although the highest classification accuracies were obtained using patterns of activity including lower
visual areas as input, classification accuracies well above chance were achieved using regions of interest restricted to higher-order object-selective
visual areas. In contrast to typical
fMRI data analysis, in which hours of data across many subjects are averaged to reveal slight differences in
activation, the use of
pattern recognition methods allows a subtle 10-way discrimination to be performed on an essentially trial-by-trial basis within individuals, demonstrating that
fMRI data contain far more information than is typically appreciated.
