Nonstationary cluster-size inference with random field and permutation methods

By Satoru Hayasaka, K. Phan, Israel Liberzon, Keith J. Worsley, T. E. Nichols. NeuroImage, 2004

305 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1614a87a2d00ae9b081b55f9242204535fdcff14

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Citations in the corpus

Development of functional and structural connectivity within the default mode network in young children (2010, methodology): “001 (uncorrected), and non-stationary cluster extent threshold (Hayasaka et al., 2004) of p < 0.”
Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data (2006, methodology): “It has been previously noted that such resel estimates are quite noisy (Hayasaka et al., 2004).”
Increased sensitivity in neuroimaging analyses using robust regression (2005, background): “Also, although we focus here on ordinary statistical techniques, even nonparametric techniques such as permutation tests (Hayasaka et al., 2004; Nichols and Holmes, 2002) are not immune to the effects of outliers, which influence the distribution of statistics used for thresholding and can result in”
Quantifying and exploring camouflaging in men and women with autism (2016, methodology): “01 for each contrast and a spatial extent threshold (corrected for non-stationarity (Hayasaka et al., 2004)) that ensures a cluster-wise FDR at q < 0.”
Faster permutation inference in brain imaging (2016, background): “…VBM example permitted inspection of the results of a practical example of an imaging modality that offers various statistical challenges, particularly with respect to non-stationarity (Hayasaka et al., 2004; Salimi-Khorshidi et al., 2011) and skewness (Salmond et al., 2002; Viviani et al., 2007).”
Unified univariate and multivariate random field theory. (2004, methodology): “Aside from this, allowances can be made for nonisotropic errors in the same way as for univariate data—see Worsley et al. (1999), Taylor and Adler (2003), and Hayasaka et al. (2004).”
Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations (2014, background): “With respect to the anti-conservative bias, it has been shown that cluster-extent based thresholding can become anti-conservative when the data violate the assumptions of GRF theory, such as uniform smoothness and the use of a sufficiently stringent primary threshold (Hayasaka et al., 2004; Silver e”
Permutation inference for the general linear model (2014, background): “For these spatial statistics, the effect of lack of pivotality can be mitigated by non-stationarity correction (Hayasaka et al., 2004; Salimi-Khorshidi et al., 2011).”