Nonparametric Permutation Tests for Functional Neuroimaging

By Thomas E. Nichols, Andrew Holmes. 2003

1,210 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computational Theory and Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/18b3ce781238c1ef0d13e099fbeb98525a78efb7

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Citations in the corpus

Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres (2015, positive): “Random ﬁ eld theory (RFT) (Worsley et al., 1992) and non-parametric permutation testing (Nichols and Holmes, 2002) are two commonly used methods to compute family-wise error (FWE) corrected p-values.”
Controlling the familywise error rate in functional neuroimaging: a comparative review (2003, neutral): “For more a more detailed introduction to the permutation test for functional neuroimaging see [36].”
Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control (2011, positive): “To address this multiple testing problem, we followed Nichols and Holmes (2002) and used a cluster size inference on the group level.”
On testing for spatial correspondence between maps of human brain structure and function (2018, positive): “general, there is an analogy between spatial permutation and other welldescribed (although perhaps under-utilized) permutation approaches (Bullmore et al., 1999; Nichols and Holmes, 2002; Winkler et al., 2014); the spatial coordinates of the maps are permuted, rather than the task/rest labels of a f”
Attentional Stimulus Selection through Selective Synchronization between Monkey Visual Areas (2012, neutral): “entered into the randomization distributions (Nichols and Holmes, 2002; Maris et al., 2007).”
Oscillatory synchronization in large-scale cortical networks predicts perception. (2011, positive): “The null-distribution was constructed from the largest clusters (two-tailed) of each resample therefore accounting for multiple comparisons (Nichols and Holmes, 2002).”
Longitudinal changes in grey and white matter during adolescence (2010, positive): “Inference on these statistics was carried out using the “randomize” program within FSL, which performs permutation testing (5000 permutations) (Nichols and Holmes, 2002).”
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data (2006, neutral): “Alternatively, one could use a permutation-based approach (Nichols and Holmes, 2001), testing an appropriate test statistic (e.g., voxel t value, cluster size4 or cluster mass) against the null distribution (generated via multiple random permutations of 4 Note that for cluster-based inference, one…”
Gender differences and age-related white matter changes of the human brain: A diffusion tensor imaging study (2008, positive): “To explore the potential gender differences using the voxelbased approach, we applied a two-sample t-test with age as covariate (Nichols and Holmes, 2002).”
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2015, positive): “The mean covariance matrix across all 92 subjects from the discovery group was used to generate 2,500 independent null datasets, which allows for the appropriate estimation of the tails of non-parametric distributions (Nichols and Holmes, 2002).”
MEG and EEG data analysis with MNE-Python (2013, neutral): “The more general class of non-parametric statistics, which we will focus on here, do not require such assumptions to be satisﬁed (Nichols and Holmes, 2002; Pantazis et al., 2005).”
Exploring the role of the posterior middle temporal gyrus in semantic cognition: Integration of anterior temporal lobe with executive processes (2016, positive): “Nonparametric voxelwise statistical testing was performed using FSL Randomize with 25,000 permutations in order to get a group tractography map (Nichols and Holmes, 2002).”
Whole Brain Resting-State Analysis Reveals Decreased Functional Connectivity in Major Depression (2010, positive): “Separate null distributions of t-values were derived for the contrasts reflecting the between and within group effects by performing 5000 random permutations and testing the difference between groups or against zero for each iteration (Nichols and Holmes, 2002).”
Tensor-Based Morphometry as a Neuroimaging Biomarker for Alzheimer’s Disease: An MRI Study of 676 AD, MCI, and Normal Subjects (2008, positive): “We computed the overall significance of group differences using permutation tests, corrected for multiple comparisons (Bullmore et al., 1999; Nichols and Holmes, 2002; Thompson et al., 2003; Chiang et al., 2007a; Chiang et al., 2007b).”