Functional magnetic resonance imaging (fMRI) "brain reading": detecting
and classifying distributed patterns of fMRI activity in human visual
cortex.
Neuroimage, (2003)Abstract
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.