Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates. Issue 7 (15th October 2018)

• Record Type: Journal Article
• Authors: Eklund, Anders; Knutsson, Hans; Nichols, Thomas E.
• Is Part Of: Human brain mapping. Volume 40:Issue 7(2019)
• Page Start: 2017
• Page End: 2032
• Publication Date: 2018-10-15
• Subjects: cluster inference -- false positives -- functional magnetic resonance imaging -- ICA FIX -- permutation -- physiological noise; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24350
• Languages: English
• ISSNs: 1065-9471

Abstract

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 randomization 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 data sets, 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 = .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.