 |
 |
Anderson, I am hesitant to trouble you again, but with my minimal understanding of imaging and FSL I wanted to clarify a few points. I apologize for the rudimentary nature of my questions. Prior to running tbss_1_preproc, participant FA images copied to a specified folder are renamed in a "logical order" so patients are distinguished from controls. However, if left vs right testing is planned (tbss_sym) in a single group of patients with no controls, a group differentiating prefix (e.g. Con_FA_c1.nii.gz, Pat_FA_p1.nii.gz) is not used; patient FA files should be distinguished by unique name only (e.g. AK_FA.nii.gz, JB_FA.nii.gz). Is this correct? In the last correspondence you mentioned to first load all images directly into R (recall, I need to run additional stats such as histograms in R on the tbss_sym data). Subsequently I should then load the 4D and the mask, then mask the data inside R before making the histograms or any other plot. I know I am asking for a "paint by numbers response" but I don't want to misinterpret your instructions. -By "load all images", you are referring to... -By "load the 4D and the mask", you are referring to... My confusion stems in part from a meager grasp of file types and vernacular: dtifit and tbss_syn files show datatype 16 in fslinfo;, only the originally dcm2nii files and eddy corrected files have the datatype 4. I assumed that I would be able to run R stats on tbss_sym stats output files. Thank you for your patience Anderson, Charlie On Mon, Jan 27, 2014 at 5:39 PM, Anderson M. Winkler <[log in to unmask] > wrote: > Hi Charlie, > > Your randomise call looks correct to me. You can drop the option -V, as it > has no effect in recent version (it was to produce verbose outputs). > > For the histograms, it depends. For a global histogram (all subjects and > across space), probably the simplest is just to load the images directly > into R. I don't use R myself so I can't help. In any case, you'd then load > the 4D and the mask, then mask the data inside R before making the > histograms or any other plot. > Using fslmeants will give too few data: for 10 subjects, just 10 values). > I think I wouldn't use it for this purpose. > > All the best, > > Anderson > > > Am 27.01.14 21:56, schrieb Charles Leger: > > Thank you Anderson for replying so promptly and in such detail. First, > I will have access to additional diffusion-weighted data which will > increase the number of participants to 10 (n=10) , still too low a number > to have much statistical power but better than the previously planned n=4. > > At this stage , my understanding of TBSS and FSL is only elementary, and > based on what you have conveyed, and in respect to tbss_sym, I would prefer > to start with just a basic left-right ( or the reverse) test. > > Following from the TBSS guide, and after completing steps 1 to 4, would > this be correct for a simple left-right test of asymmetry? > > randomise -i all_FA_skeletonised_left_minus_right.nii.gz -o tbss -m > mean_FA_symmetrised_skeleton_mask.nii.gz -1 -n 5000 --T2 -V > > Also, if I want to complete a histographic analysis of the FA data in R > statistics, would you recommend fslmeants and if so would an appropriate > string be > > fslmeants -i all_FA_skeletonised_left_minus_right.nii.gz -o > mean_values_file.txt > > Thank you, > Charlie > > > > > > > On Mon, Jan 27, 2014 at 3:19 PM, Anderson M. Winkler < > [log in to unmask]> wrote: > >> Hi Charlie, >> >> To run this as a single design on its full generality, with the 3 >> contrasts (effect of side, effect of time, and interaction), and taking the >> repeated measures into account (something as a 2x2 ANOVA, with the repeated >> measures), it'd be necessary a certain permutation strategy that, as of >> today, isn't available in randomise. However, you can still investigate >> differences, but you'll need to tweak a bit the pipeline and run randomise >> 3 times: >> >> - To test the effect of side regardless of time: average the two >> timepoints for each subject (or just sum t1+t2), then subtract L-R, and run >> randomise with the option -1. >> - To test the effect of time regardless of side: average each image with >> itself after flipping (or just sum, L+R), then compute t2-t1, and run >> randomise with the option -1. >> - To test the interaction of side and time, compute L-R, then t2-t1, then >> run randomise with the option -1. >> >> To do this, you'll have to take the file >> all_FA_symmetrised_skeletonised.nii.gz, which is a 4D file, disassemble it >> (with fslsplit), rename the outputs with meaningful names, e.g. >> L_subj01_t1.nii.gz, etc, then flip each one (fslswapdim -y) to have the >> corresponding R_subj01_t1.nii.gz, etc, then compute the sums and >> differences as above (use fslmaths -add and -sub), then assemble back as 3 >> new 4D files (fslmerge), and only them run randomise. Note that the file >> all_FA_skeletonised_left_minus_right.nii.gz already contains the L-R >> differences, but it's probably simpler to begin and do all starting with >> the all_FA_symmetrised_skeletonised.nii.gz. >> >> For each randomise run you'll have only 4 images in each 4D file, so only >> 2^4=16 possible sign-flips, which isn't much even for a pilot study. >> Replacing randomise with a parametric test wouldn't help much either, as >> you'd have only 3 degrees of freedom per run. I suggest that you try to >> have more images, even for a pilot. >> >> Hope this helps. >> >> All the best, >> >> Anderson >> >> >> Am 27.01.14 18:24, schrieb charlie Leger: >> >> A few questions regarding tbss_sym FA (pilot project) with a >>> repeated-measures design (within-subjects) 4 subjects (n=4), scanned at two >>> time points; initially and 8 months later. >>> >>> The tbss guide suggests using the option -1 for left-right testing in >>> randomise. I don't think this randomise setting can be used for this >>> project which has two factors: left vs right and time which is a total of 4 >>> levels. This could be set-up as a 2 factor, 2-level Anova (asymmetry: left >>> vs right, time: before and after 8 months), though pairwise test are >>> ultimately needed. >>> >>> I did use the glm_gui to attempt make 2-factor, 2-level design (n=4) and >>> can forward it. >>> >>> Any suggestions in helping to set up the appropriate design and >>> "randomise" call (e.g. randomise -i >>> all_FA_skeletonised_left_minus_right.nii.gz -o tbss -m >>> mean_FA_symmetrised_skeleton_mask.nii.gz -1 -n 5000 --T2 -V) would be much >>> appreciated. >>> >>> Charlie >>> >> > >