Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates

Abstract

Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomisation of events between subjects. We consider the lack of validity found with one-sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all datasets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (P = 0.01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.

References in corpus (5)

• Can parametric statistical methods be trusted for fMRI based group studies?
• FMRI Clustering and False Positive Rates
• Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates
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Cited by in corpus (4)

• Accurate autocorrelation modeling substantially improves fMRI reliability
• Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates
• Reply to Chen et al.: Parametric methods for cluster inference perform worse for two-sided t-tests
• Robust Spatial Extent Inference with a Semiparametric Bootstrap Joint Testing Procedure