Journal article
Control of false positive rates in clusterwise fMRI inferences
Journal of applied statistics, v 46(11), pp 1956-1972
18 Aug 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Random field theory (RFT) provided a theoretical foundation for cluster-extent-based thresholding, the most widely used method for multiple comparison correction of statistical maps in neuroimaging research. However, several studies questioned the validity of the standard clusterwise inference in fMRI analyses and observed inflated false positive rates. In particular, Eklund et al. [Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates, Proc. Natl. Acad. Sci. 113 (2016), pp. 7900-7905. Available at
http://www.pnas.org/content/113/28/7900.abstract
] used resting-state fMRI as null data and found false positive rates of up to
, which immediately led to many discussions. In this study, we summarize the assumptions in RFT clusterwise inference and propose new parametric ways to approximate the distribution of the cluster size by properly combining the limiting distribution of the cluster size given by Nosko [Local structure of Gaussian random fields in the vicinity of high-level shines, Sov. Math. Dokl. 10 (1969), pp. 1481-1484] and the expected value of the cluster size provided by Friston et al. [Assessing the significance of focal activations using their spatial extent, Hum. Brain Mapp. 1 (1994), pp. 210-220. Available at
http://dx.doi.org/10.1002/hbm.460010306
]. We evaluated our proposed method using four different classic simulation settings in published papers. Results show that our method produces a more stringent estimation of cluster extent size, which leads to a better control of false positive rates.
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Details
- Title
- Control of false positive rates in clusterwise fMRI inferences
- Creators
- Fengqing Zhang - Drexel UniversityJiangtao Gou - Temple University Health System
- Publication Details
- Journal of applied statistics, v 46(11), pp 1956-1972
- Publisher
- Taylor & Francis
- Grant note
- Professional Staff Congress-City University of New York Drexel University
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000472110500003
- Scopus ID
- 2-s2.0-85060927994
- Other Identifier
- 991019167918704721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Statistics & Probability