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Abstract

Traditionally fMRI data analysis aims at identifying brain areas in which the amplitude of the BOLD signal responds to experimental stimulations. However, since the brain acts as a network, we would expect differential effects on network topology. Therefore, the target of statistical inference should not only be individual voxels or brain areas but rather network connections. Here we introduce a new approach to correlation-based statistics in fMRI. At the heart of our approach is the concept of correlation bundles as a functional analogy to anatomical fibre bundles. Statistical tests are applied to these bundles using large-scale inference methods such as FDR. We call this approach correlation bundle statistics (CBS). In contrast to previous correlation-based approaches to fMRI statistics, CBS does not require a presegmentation or smoothing of the data so that anatomical specificity is preserved. The result of a CBS analysis is not a set of voxels or brain regions but rather a set of correlation bundles that are found to be significantly affected by some experimental manipulation.