Jun 21, 2019 · The intuition of TFCE is that we are going to try out all possible thresholds and see whether a given time-point belongs to a significant cluster under any of our set of cluster-thresholds.

May 15, 2016 · TFCE is an image transform that accentuates the "clusteriness" in data. One feeds in a statistical map (e.g. t- for F-statistics) and TFCE re-weights every point based on those around it .

Threshold‐free cluster enhancement (TFCE) is a sensitive means to incorporate spatial neighborhood information in neuroimaging studies without using arbitrary thresholds. The majority of methods have applied TFCE to voxelwise data. The need to ...

Jan 1, 2009 · We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-based and voxel-based thresholding. We find that TFCE gives generally better sensitivity than other methods over a wide range of test signal shapes and SNR values.

In this study, we evaluated three of these methods, Statistical non-Parametric Mapping (SnPM), 3DClustSim, and Threshold Free Cluster Enhancement (TFCE), by examining which method produced the most consistent outcomes even when spatially-autocorrelated noise was added to the original images.

Cluster-level inferences based on Gaussian Random Field theory (Worsley et al. 1996) start with a statistical parametric map of T- or F- values estimated using a General Linear Model. This map is first thresholded using an a priori "height" threshold level (e.g. T>3 or p<0.001).

voxelwise values represent the amount of cluster-like local spatial support. The method is thus referred to as “threshold-free cluster enhancement” (TFCE). We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-bas.

Jan 1, 2009 · The TFCE approach aims to enhance areas of signal that exhibit some spatial contiguity without relying on hard-threshold-based clustering. The image is passed through an algorithm which should enhance the intensity within cluster-like …

pTFCE (probabilistic TFCE) is a cluster-enahncement method to improve detectability of neuroimaging signal. It performs topology-based belief boosting by integrating cluster information into voxel-wise statistical inference.

Cluster-extent based thresholding has become the most popular correction method for multiple comparisons in fMRI data analysis because it is more sensitive (more powerful) and reflects the spatially correlated nature of fMRI signal.