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Thanks a lot, Anderson In the online manual of randomise (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/UserGuide), it states TFCE (Threshold-Free Cluster Enhancement) is a new method for finding "clusters" in your data without having to define clusters in a binary way. Cluster-like structures are enhanced but the image remains fundamentally voxelwise; you can use the -tfce option in fslmaths to test this on an existing stats image. See the TFCE research page for more information. The "E", "H" and neighbourhood-connectivity parameters have been optimised and should be left unchanged. These optimisations are different for different "dimensionality" of your data; for normal, 3D data (such as in an FSL-VBM analysis), you should just just the -T option, while for TBSS analyses (that is in effect on the mostly "2D" white matter skeleton), you should use the --T2 option. So, could I understand the aforementioned information in the following way, For the VBM analysis, I can just use the "-T" option. This is an accepted way for multiple comparison for VBM. Right? However, using the "-T --two pass" option, this is a better way than just use the "-T" option, right? In the paper "Adjusting the effect of nonstationarity in cluster-based and TFCE inference", it writes "In this study, adjusted cluster sizes are used in a permutation-testing framework for both cluster-based and threshold-free cluster enhancement (TFCE) inference and tested on both simulated and real data." So, I can also use the "-c --two pass" option, right? Which is the best of the above three methods do you think for VBM analysis? The "-T --two pass" option? the "-c --two pass" or just the "-T" option? Thanks, Feng 发件人："Anderson M. Winkler" <[log in to unmask]> 发送时间：2015-11-06 16:30 主题：Re: [FSL] Could I use the TFCE method for VBM's multiple comparison correction? 收件人："FSL"<[log in to unmask]> 抄送： Hi Feng, Randomise tells the name of the file at the end of the permutations (in the message about the critical value). It's the _tfcen_ that you'd be looking into. All the best, Anderson On 6 November 2015 at 07:42, chenhf_uestc <[log in to unmask]> wrote: Thanks for your quick reply. The last question is, which file should I use that setting the 0.95 threshold for correction? There are lots of files if I use the --twopass -T option: Best, Feng 发件人：Stephen Smith <[log in to unmask]> 发送时间：2015-11-06 15:02 主题：Re: [FSL] Could I use the TFCE method for VBM's multiple comparison correction? 收件人："FSL"<[log in to unmask]> 抄送： Hi - it should be fine to use the two options together. Cheers On 6 Nov 2015, at 06:56, chenhf_uestc <[log in to unmask]> wrote: Thanks, Stephen You mean that I cannot use -T option directly in the randomise, right? Like that, randomise -i TwoSamp4D (two group's VBM data) -o TwoSampT -d design.mat -t design.con -m mask -T (carry out Threshold-Free Cluster Enhancement) You say that I need use the --twopass option (carry out cluster normalisation thresholding), how could I input the command? Just like, randomise -i TwoSamp4D (two group's VBM data) -o TwoSampT -d design.mat -t design.con -m mask --twopass or I need use the -T and --twopass at the same time to achieve that use the TFCE method for VBM's multiple comparison correction. randomise -i TwoSamp4D (two group's VBM data) -o TwoSampT -d design.mat -t design.con -m mask -T --twopass Which one is right? If I just use the --twopass option, the randomise command could know that I want to modified (adjusting for local smoothness via a separate resampling procedure) TFCE method? Thanks, Feng 发件人：Stephen Smith <[log in to unmask]> 发送时间：2015-11-06 13:15 主题：Re: [FSL] Could I use the TFCE method for VBM's multiple comparison correction? 收件人："FSL"<[log in to unmask]> 抄送： Hi - yes, this is the --twopass mode in randomise. Cheers > On 6 Nov 2015, at 02:42, chenhf_uestc <[log in to unmask]> wrote: > > Dear FSLers, > > The results of VBM are actually nonstationary images, and thus we cannot directly use cluster-based inference. We need correct the inference for such nonstationary. Previously, I used nonstationary cluster extent correction based on RFT for multiple comparison correction for VBM results. Recently, I read the paper, titled "Adjusting the effect of nonstationarity in cluster-based and TFCE inference". In this study, Dr. Salimi-Khorshidi and colleagues find that adjusting for local smoothness via a separate resampling procedure is more effective at removing nonstationarity than an adjustment via a random field theory based smoothness estimator. > > Where could I use this method? In the FSL software, there is nonparametric randomise TFCE method. However, in the original TFCE paper (Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference, 2009, neuroimage), I do not find the separate resampling procedure in this paper. > > What about the current version of FSL software? Has used the separate resampling procedure or not? Has used the method introduced in "Adjusting the effect of nonstationarity in cluster-based and TFCE inference"? If not, could I use the TFCE method for VBM's multiple comparison correction. > > Best, > Feng --------------------------------------------------------------------------- Stephen M. Smith, Professor of Biomedical Engineering Head of Analysis, Oxford University FMRIB Centre FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK +44 (0) 1865 222726 (fax 222717) [log in to unmask] http://www.fmrib.ox.ac.uk/~steve --------------------------------------------------------------------------- Stop the cultural destruction of Tibet --------------------------------------------------------------------------- Stephen M. Smith, Professor of Biomedical Engineering Head of Analysis, Oxford University FMRIB Centre FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK +44 (0) 1865 222726 (fax 222717) [log in to unmask] http://www.fmrib.ox.ac.uk/~steve --------------------------------------------------------------------------- Stop the cultural destruction of Tibet