Regular ArticleCluster Significance Testing Using the Bootstrap
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Enhanced limbic/impaired cortical-loop connection onto the hippocampus of NHE rats: Application of resting-state functional connectivity in a preclinical ADHD model
2017, Behavioural Brain ResearchCitation Excerpt :Resampling is a powerful technique, allowing to assess data reliability and reproducibility and to identify, through a significance threshold, the brain “active” voxels. By using the bootstrap or jack-knife method, it is possible, for fMRI analyses in particular, to overcome the problem of the test-retest method [28] and/or to assess significance of activation clustering [29]. Present bootstrap resampling is an alternative, computer-intensive method that provides a strong control of the family wise control rate, which is more conservative than the false discovery rate among the combined Type I error rate [30].
Analysis of Speed Patterns on Inter-urban Parallel Highways: A Case Study in the Southeast Florida
2017, Transportation Research ProcediaFalse positive control of activated voxels in single fMRI analysis using bootstrap resampling in comparison to spatial smoothing
2013, Magnetic Resonance ImagingCitation Excerpt :The jackknife method [11,12] was used to compute the reliability and confidence intervals of fMRI parameters during bilateral finger tapping. Auffermann et al. [14] applied the bootstrap method to assess the significance of the self-organizing maps in clustering algorithm applied on event-related data. In another study, the bootstrap analysis was used to investigate the stability of the clusters found by K-means algorithm for resting-state networks [15].
Uncertainty estimations for quantitative in vivo MRI T<inf>1</inf> mapping
2012, Journal of Magnetic ResonanceCitation Excerpt :The variation found in fits of these datasets is a measure of the uncertainty of the fit. Wild bootstrapping has already been applied successfully in the assessment of diffusion tensor imaging (DTI) parameters [3–8] and the determination of functional MRI resting state network nodes [9]. T1 fitting and conventional bootstrapping (i.e. random permutations from repeated measurements) were used in spectroscopic imaging to assess the standard error of T1 fitting [10].
Understanding the variability of speed distributions under mixed traffic conditions caused by holiday traffic
2010, Transportation Research Part C: Emerging TechnologiesDissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis
2007, Magnetic Resonance ImagingCitation Excerpt :Other studies compared FCM to alternative techniques in the field of fMRI, like correlation [18] or PCA [19]. Several studies dealt with other aspects, like the cluster validity problem [20–22] or the influence of higher fields on FCM results [23]. Clustering on features extracted from the fMRI time series at each voxel was also investigated [24,25].