Multivariate classification of neuroimaging data - New publication by Jamalabadi et al.

When data are analyzed using multivariate pattern classification, any systematic similarities between subsets of trials (e.g. shared physical properties among a subgroup of stimuli, trials belonging to the same session or subject, etc.) form distinct nested subclasses within each class. Pattern classification is sensitive to this kind of structure in the data and uses such groupings to increase classification accuracies even when data from both conditions are sampled from the same distribution, i.e. the null hypothesis is true. Here, we show that the bias is higher for larger subclass variances and that it is directly related to the number of subclasses and the intraclass correlation (ICC). Because the increased classification accuracy in such data sets is not based on class differences, the null distribution should be adjusted to account for this type of bias. To do so, we propose to use blocked permutation testing on subclass levels and show that it can confine the false positive rate to the predefined α-levels.

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