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