Journal Article

Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations

Woo, Choong-Wan, Wager, Tor D., Krishnan, Anjali

2014

Overview

Cluster-extent based thresholding is currently the most popular method for multiple comparisons correction of statistical maps in neuroimaging studies, due to its high sensitivity to weak and diffuse signals. However, cluster-extent based thresholding provides low spatial specificity; researchers can only infer that there is signal somewhere within a significant cluster and cannot make inferences about the statistical significance of specific locations within the cluster. This poses a particular problem when one uses a liberal cluster-defining primary threshold (i.e., higher p-values), which often produces large clusters spanning multiple anatomical regions. In such cases, it is impossible to reliably infer which anatomical regions show true effects. From a survey of 814 functional magnetic resonance imaging (fMRI) studies published in 2010 and 2011, we show that the use of liberal primary thresholds (e.g., p<.01) is endemic, and that the largest determinant of the primary threshold level is the default option in the software used. We illustrate the problems with liberal primary thresholds using an fMRI dataset from our laboratory (N=33), and present simulations demonstrating the detrimental effects of liberal primary thresholds on false positives, localization, and interpretation of fMRI findings. To avoid these pitfalls, we recommend several analysis and reporting procedures, including 1) setting primary p<.001 as a default lower limit; 2) using more stringent primary thresholds or voxel-wise correction methods for highly powered studies; and 3) adopting reporting practices that make the level of spatial precision transparent to readers. We also suggest alternative and supplementary analysis methods. •Cluster-extent based thresholding is popular because of its high sensitivity.•However, cluster-extent based thresholding has several important problems.•One pitfall is low spatial specificity when significant clusters are large.•Another pitfall is increased false positives when a liberal primary threshold is used.•We recommend using stringent primary thresholds and augmented reporting procedures.

• Publisher: Elsevier Inc,Elsevier,Elsevier Limited
• Subject: Biological and medical sciences / Cluster Analysis / Cluster-extent thresholding / Computer Simulation / Data Interpretation, Statistical / Expected values / False discovery rate / False Positive Reactions / Family-wise error rate / fMRI / FSL / Fundamental and applied biological sciences. Psychology / Gaussian random fields / Humans / Hypotheses / Image Processing, Computer-Assisted - methods / Magnetic Resonance Imaging - methods / Magnetic Resonance Imaging - statistics & numerical data / Methods / Multiple comparisons / Neuroimaging - methods / Normal Distribution / Primary threshold / Signal-To-Noise Ratio / Software / SPM / Vertebrates: nervous system and sense organs