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Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference
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Smith, Stephen M. and Nichols, Thomas E. (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, Vol.44 (No.1). pp. 83-98. doi:10.1016/j.neuroimage.2008.03.061 ISSN 1053-8119.
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Official URL: http://dx.doi.org/10.1016/j.neuroimage.2008.03.061
Abstract
Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and yet its exact choice can have a large impact on the results, particularly at the lower (e.g., t, z < 4) cluster-forming thresholds frequently used. Furthermore, the amount of spatial pre-smoothing is also arbitrary (given that the expected signal extent is very rarely known in advance of the analysis). In the light of such problems, we propose a new method which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of “clusters” of signal), while avoiding (or at least minimising) these problems. The method takes a raw statistic image and produces an output image in which the voxel-wise 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-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. We also show an example on a real imaging dataset, suggesting that TFCE does indeed provide not just improved sensitivity, but richer and more interpretable output than cluster-based thresholding.
| Item Type: | Journal Article | ||||
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| Subjects: | Q Science > QA Mathematics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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| Library of Congress Subject Headings (LCSH): | Cluster analysis, Mathematical statistics, Gaussian processes, Brain -- Imaging -- Data processing, Simulation methods | ||||
| Journal or Publication Title: | NeuroImage | ||||
| Publisher: | Elsevier | ||||
| ISSN: | 1053-8119 | ||||
| Official Date: | 1 January 2009 | ||||
| Dates: |
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| Volume: | Vol.44 | ||||
| Number: | No.1 | ||||
| Number of Pages: | 16 | ||||
| Page Range: | pp. 83-98 | ||||
| DOI: | 10.1016/j.neuroimage.2008.03.061 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Funder: | Engineering and Physical Sciences Research Council (EPSRC) |
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