Those vertices with p-value lower than 0.0001 are used to form the clusters for the cluster-wise correction. If  the signal in your SPM is weak but extended you should use a permissive cluster-forming p-value (eg 0.01). On the other hand, if your signal is strong and very localized you should use a more stringent p-value. The sign parameter simply tells the command if you want to perform a one-tailed test. For instance, if you are sure that group1 has reduced cortical thickness in comparison with group 2 then you can test the null hypothesis that group2=group1 against the one-tailed alternative hypothesis group2>group1.

-Jorge
 


De: ZhiLiangLong <lagosslong1987@163.com>
Para: freesurfer <freesurfer@nmr.mgh.harvard.edu>
Enviado: Domingo 18 de marzo de 2012 4:46
Asunto: [Freesurfer] question about Cluster-wise Correction for Multiple Comparisions

Hi all:
       I'm performing the group analysis  on my own data using command line on FS5.1.0 and i'm a little confused about the Clusterwise Correction for Multiple Comparisions. the command is as follows:
      mri_glmfit-sim \
             --glmdir lh.gender_age.glmdir \
            --sim mc-z 10000 4 mc-z.negative \
            --sim-sign neg --cwpvalthresh 0.05\
            --overwrite
      In this command, does the vertex-wise threshold (here, '4') mean that the vertex whose p value is lower than 0.0001 can be considered to used for the Correction for Multiple Comparisions ? likewise, does the sign (here,'neg')  mean that only negtive vertexs can be considered? Is there a criterion which vertex-wise threshold  and sign i should choose?
Hope you can give me some suggestions !



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