Abstract
Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.
Highlights
Our ability to understand brain function is limited by the scale and accuracy with which we can quantify neural activity
While a number of techniques from statistical learning have been applied to functional magnetic resonance imaging (fMRI) data, here we focus on the use of multivariate pattern classification (MVPC) to decode mental states
While the potential for overfitting is high in the case of fMRI (Misaki et al, 2010; Pereira and Botvinick, 2011), several studies (e.g., Hanson et al, 2004; Rasmussen et al, 2011) have demonstrated that pattern classifiers are capable of decoding information bound in non-linear relationships across multivariate samples
Summary
Our ability to understand brain function is limited by the scale and accuracy with which we can quantify neural activity. Linear classification algorithms (Figure 1A, top) use a weighted combination of signals from voxels within a feature set (e.g., a brain region) to decode perceptual or cognitive states.
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