Applying multivariate techniques to high-dimensional temporally correlated fMRI data

Abstract In first-level analyses of functional magnetic resonance imaging data, adjustments for temporal correlation as a Satterthwaite approximation or a prewhitening method are usually implemented in the univariate model to keep the nominal test level. In doing so, the temporal correlation structure of the data is estimated, assuming an autoregressive process of order one. We show that this is applicable in multivariate approaches too – more precisely in the so-called stabilized multivariate test statistics. Furthermore, we propose a block-wise permutation method including a random shift that renders an approximation of the temporal correlation structure unnecessary but also approximately keeps the nominal test level in spite of the dependence of sample elements. Although the intentions are different, a comparison of the multivariate methods with the multiple ones shows that the global approach may achieve advantages if applied to suitable regions of interest. This is illustrated using an example from fMRI studies.