Modular preprocessing pipelines can reintroduce artifacts into fMRI data.

ABSTRACT: The preprocessing pipelines typically used in both task and resting-state functional magnetic resonance imaging (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this article, we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariat

SUBMITTER: Lindquist MA 

PROVIDER: S-EPMC6865661 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature