Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference

By Stephen M. Smith, Thomas E. Nichols. NeuroImage, 2009

5,323 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a2404efe53398d7900830f1c25bdecf42bf1002e

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Citations in the corpus

DTI measures in crossing-fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease (2011, methodology): “05, corrected for multiple comparisons using the “2D” parameter settings with threshold-free cluster enhancement (TFCE), a method which avoids using an arbitrary threshold for the initial cluster-formation (Smith and Nichols, 2009).”
The relation between statistical power and inference in fMRI (2017, methodology): “In addition, a common approach is to apply cluster-extend thresholds which is in general a more sensitive method than these voxel-wise threshold methods (see for instance threshold free cluster enhancement [47]), though certain common practices have recently also been debated [48,49].”
Multimodal Surface Matching with Higher-Order Smoothness Constraints☆ (2017, methodology): “This assesses at each vertex whether distortions are statistically greater than zero, performing family-wise error correction through Threshold Free Cluster enhancement (TFCE,Smith and Nichols (2009)).”
Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers (2014, background): “FIX uses other spatial features that are measures of an IC's map's smoothness and its TFCE (threshold-free cluster enhancement) (Smith and Nichols, 2009) statistics.”
CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave (2016, background): “It has been proposed (Spinellis & Gousios, 2009) that it is desirable for an architecture to follow the following principles / properties: versatility: offer “good enough” mechanisms to address a variety of problems with an economy of expression.”
Consolidation alters motor sequence-specific distributed representations (2018, methodology): “TFCE enabled a locally adaptive statistics and cluster size correction that particularly fitted our BOLD sampling of sparse gray-matter coordinates, as well as the large differences in the sizes of the structures that were investigated.”
Training confounder-free deep learning models for medical applications (2020, methodology): “The resulting one-tailed voxel-wise p-values associated with the diagnosis label were corrected for spatial coherence by FSL Threshold-Free Cluster Enhancement (TFCE) [8] and for family-wise error at the 5% level across space.”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, methodology): “A non-parametric cluster-based statistic (e.g., permutation methods with threshold-free cluster enhancement correction, Smith and Nichols, 2009) should be another option to perform multiple corrections for statistical comparisons of ReHo.”
Exploring the role of the posterior middle temporal gyrus in semantic cognition: Integration of anterior temporal lobe with executive processes (2016, positive): “01, Family-Wise Error (FWE) corrected, using the Threshold-Free Cluster Enhancement (TFCE) technique (Smith and Nichols, 2009).”
CAT: a computational anatomy toolbox for the analysis of structural MRI data (2024, methodology): “CAT comes with its own thr eshold-fr ee cluster enhancement (TFCE) toolbox and provides the option to apply TFCE [ 45 ] in any statistical second-level analysis in SPM, for both voxel-based and surface-based analyses.”
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, methodology): “, 1996; Nichols and Holmes, 2002], or cluster sum statistics [Maris and Oostenveld, 2007] possibly with threshold-free cluster enhancement [Pernet et al., 2015; Smith and Nichols, 2009].”
Methodological considerations on tract-based spatial statistics (TBSS) (2014, methodology): “, 2012), these may also be attributed in part to the TFCE approach (Smith and Nichols, 2009).”
Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations (2014, methodology): “By contrast, cluster-extent based thresholding has relatively high sensitivity (Friston et al., 1994; Smith and Nichols, 2009).”
Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. (2012, methodology): “…represented on the native mesh of the individual hemisphere, the 2 successive transformations (native / fs_L or fs_R / fs_LR) can be concatenated into a single individual-tofs_LR deformation map by a 2-step process (http://brainvis.wustl.edu/ wiki/index.php/Caret:Operations/FreeSurferAndFsAverage).”