 |
 |
Hi Kavous, Please see below: On 14 June 2017 at 02:40, Kavous Salehzadeh <[log in to unmask]> wrote: > Hi Anderson, > > Thanks for your prompt answer. Please see below: > > - t4-t1 > - t3-t1 > - t2-t1 > > This leaves you with 3 "modalities". Assemble these differences into 3 > files (4D, with subjects in the 4th dimension). > > - So does it make sense to add more modalities like t4-t3, t4-t2 and t3-t2? > Not really because these the information given by these subtractions are already provided by the three earlier ones (taking t1 as references). In fact, using more than just 3 differences will cause an error if you try to run MANOVA. For NPC it will work without error, but the result will be the same as if these hadn't been included. So, just 3 differences are fine. > > The design is a simple two-sample t-test (no repeated measurements). If > you want to further include nuisance variables (e.g., age, sex, etc), just > add them as extra EVs to the design. > > - Could you please look at the attached design? I am not so confident! > This isn't right I'm afraid. Just 3 EVs: EV1: group 1 EV2: group 2 EV3: age The contrasts are then just [1 -1 0] and [-1 1 0]. Also, all subjects go in the same large EB (that is, there is no need for EBs in this arrangement). All the best, Anderson > > Sincerely, > > Kavous > > > > On Wed, Jun 14, 2017 at 10:33 AM, Anderson M. Winkler < > [log in to unmask]> wrote: > >> Hi Kavous, >> >> That seems a very specific question. There are some possibilities: >> >> 1) Very broadly, this is to test whether the two curves have the same or >> different profiles. This can be tested with NPC or MANOVA (the latter is >> more "classical", but less powerful). In either case, compute, for each >> subject: >> >> - t4-t1 >> - t3-t1 >> - t2-t1 >> >> This leaves you with 3 "modalities". Assemble these differences into 3 >> files (4D, with subjects in the 4th dimension). Then run PALM with >> something as: >> >> *palm -i diff_t4-t1.nii.gz -i diff_t3-t1.nii.gz -i diff_t2-t1.nii.gz -d >> design.mat -t design.con -npc [other options]* >> >> or >> >> >> *palm -i diff_t4-t1.nii.gz -i diff_t3-t1.nii.gz -i diff_t2-t1.nii.gz -d >> design.mat -t design.con -mv [other options]* >> >> The former will do NPC, the latter will do MANOVA. The design is a simple >> two-sample t-test (no repeated measurements). If you want to further >> include nuisance variables (e.g., age, sex, etc), just add them as extra >> EVs to the design. >> >> This is a very broad test, that investigates any curve differences >> between the two groups, not only the one in the profiles you suggested. >> >> It is also possible to devise other tests as to whether the two curves >> are not only parallel, but have the same average level, and whether both >> curves are zero, but I think the above should already accommodate what you >> need. >> >> Hope this helps! >> >> All the best, >> >> Anderson >> >> >> On 13 June 2017 at 09:17, Kavous Salehzadeh <[log in to unmask]> >> wrote: >> >>> Hi Anderson, >>> >>> I faced with a new question! Let's assume I have 2 groups. In my VBM >>> study, different time points are: >>> >>> t1=before intervention (PRE) >>> t2= x weeks after starting intervention (MIDDLE) >>> t3= 2x weeks after starting intervention (POST) >>> t4= x weeks after *stopping *intervention (FOLLOW UP) >>> >>> Please see attached which is my hypothesis about GM changes. So as you >>> see a decrease is hypothesized between week-2x and week-3x after stopping >>> the intervention. Do you think despite the trend change can I still use >>> PALM and the design you previously offered me? >>> >>> Sincerely, >>> >>> Kavous >>> >>> On Sat, Jun 10, 2017 at 11:13 PM, Kavous Salehzadeh < >>> [log in to unmask]> wrote: >>> >>>> Hi Anderson, >>>> >>>> Got it! I really appreciate your help. >>>> >>>> Sincerely, >>>> >>>> Kavous >>>> >>>> ---------- >>>> >>>> *PhD Candidate* >>>> >>>> *Center for Human Engaged Computing <http://xrenlab.com/>* >>>> >>>> *782-0003, Kochi University of Technology, Kami City, Japan* >>>> >>>> *Skype: kavus.salehzadeh* >>>> >>>> On Sat, Jun 10, 2017 at 11:07 PM, Anderson M. Winkler < >>>> [log in to unmask]> wrote: >>>> >>>>> Hi Kavous, >>>>> >>>>> When there are only 2 timepoints, subtraction is an option. It is also >>>>> possible to do as in the proposed model, with all observations in the same >>>>> run without subtractions. With 4 measurements subtraction becomes more >>>>> complicated, hence the model as suggested in the last email using all 4 >>>>> observations (indicated as A, B, C and D). >>>>> >>>>> All the best, >>>>> >>>>> Anderson >>>>> >>>>> >>>>> On 6 June 2017 at 00:47, Kavous Salehzadeh <[log in to unmask] >>>>> > wrote: >>>>> >>>>>> Hi Anderson, >>>>>> >>>>>> Thank you very much for your answer. Here is one more question! >>>>>> >>>>>> Following discussions about longitudinal VBM (see the below links), I >>>>>> know that I have to give the 4D difference image of two time-points to the >>>>>> randomise (or here PALM). >>>>>> >>>>>> https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=FSL;d6651f48.1008 >>>>>> https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=FSL;3dc8868b.1408 >>>>>> >>>>>> However, it seems in your proposed design matrix, I have to feed PALM >>>>>> with each time-point data without subtracting them. Am I right? >>>>>> >>>>>> Sincerely, >>>>>> Kavous >>>>>> >>>>>> On Tue, Jun 6, 2017 at 11:35 AM, Anderson M. Winkler < >>>>>> [log in to unmask]> wrote: >>>>>> >>>>>>> Hi Kavous, >>>>>>> >>>>>>> Please see below: >>>>>>> >>>>>>> On 2 June 2017 at 02:12, Kavous Salehzadeh < >>>>>>> [log in to unmask]> wrote: >>>>>>> >>>>>>>> Hi Anderson, >>>>>>>> >>>>>>>> I am so grateful for your prompt response. I am new to FSL and in >>>>>>>> particular to PALM. So, sorry for any stupid questions. >>>>>>>> >>>>>>>> Let me explain more about the experiment and the hypothesis. My >>>>>>>> time points are: >>>>>>>> >>>>>>>> t1: pre-intervention >>>>>>>> t2: mid-intervention >>>>>>>> t3: post-intervention >>>>>>>> t4: follow-up (8 weeks after stopping intervention to see whether >>>>>>>> effect is persisting or not) >>>>>>>> >>>>>>>> And my three groups are: >>>>>>>> >>>>>>>> G1: control (no effect hypothesized within time points) >>>>>>>> G2: intervention1 (small effect hypothesized within t1-t2-t3 + *NO* persisting >>>>>>>> effect on t4) >>>>>>>> G3: intervention2 (greater effect hypothesized within t1-t2-t3 + >>>>>>>> persisting effect on t4) >>>>>>>> >>>>>>>> So according to my hypothesis, it seems I should follow your advice >>>>>>>> on using PALM and -ISE (without the assumption of compound symmetry). >>>>>>>> >>>>>>>> So now a couple of questions arose in my mind: >>>>>>>> >>>>>>>> 1) Can I still use semi-autonomous FSLVBM process (i.e., >>>>>>>> fslvbm_1_bet - fslvbm_2_template - fslvbm_3_proc) and later use PALM? >>>>>>>> >>>>>>> >>>>>>> Yes and no. PALM won't know where the data is coming from. Now I >>>>>>> notice you are using VBM, which has skewed data, so the ISE assumption >>>>>>> won't hold. That leaves you with a choice between the ugly and the bad: >>>>>>> compound symmetry (unlikely to be valid) versus symmetric errors (certainly >>>>>>> invalid). >>>>>>> >>>>>>> Violating ISE is on average slightly conservative (we have seen with >>>>>>> quite extensive simulations), whereas violating compound symmetry leads to >>>>>>> excess false positives. So, if I were to choose, I'd go with ISE. >>>>>>> >>>>>>> >>>>>>> >>>>>>>> >>>>>>>> 2) I know that in the longitudinal VBM analysis with >>>>>>>> two time-points, I should treat each time point as an individual subject, >>>>>>>> and later before randomize subtract GM_mod_merg_s* files between two >>>>>>>> time-points. I wonder in my case should I compute all pairwise differences >>>>>>>> and do PALM for each? (i.e., t1-t2, t1-t3, t1-t4, t2-t3, t2-t4, >>>>>>>> t3-t4). Does the following command seem right? >>>>>>>> >>>>>>>> *palm -i diff_t1_t2_GM_mod_merg_s3.nii -d design.mat -t design.con >>>>>>>> -m GM_mask.nii -f design.fts -eb eb_file.csv -vg vg_file.csv -T -C 3.1 -n >>>>>>>> 5000 -save1-p -corrcon -o myresults -ise* >>>>>>>> >>>>>>>> >>>>>>> It wouldn't be crazy to test the various pairwise differences and do >>>>>>> a joint test with NPC, but that wouldn't be specific for trends evolving >>>>>>> over timepoints, and for your particular hypothesis it would amount to >>>>>>> overfitting. >>>>>>> >>>>>>> >>>>>>> >>>>>>>> 3) Should I use [Pset, VG] = palm_quickperms(M, EB, P, ISE) or can >>>>>>>> I make my own vg.csv file (likewise attachment). If this command is >>>>>>>> necessary can I use design.mat for M? >>>>>>>> >>>>>>> >>>>>>> You don't need palm_quickperms at all. This command is only if you >>>>>>> want to generate the set of permutations and VG to use in some different >>>>>>> software (e.g., to use with your own code for some unrelated task). >>>>>>> >>>>>>> The VGs can be constructed manually or, in most cases, created >>>>>>> automatically with "-vg auto". >>>>>>> >>>>>>> >>>>>>>> >>>>>>>> 4) Can I still use FSL GLM command for making the design matrix? >>>>>>>> >>>>>>> >>>>>>> Yes. Design matrix, contrasts, and EB (file *.grp) can be created >>>>>>> with the Glm command. >>>>>>> >>>>>>> >>>>>>> >>>>>>>> Please see attached design matrix. Is it right matrix in my case? >>>>>>>> >>>>>>> >>>>>>> This design isn't correct I'm afraid. Please see a different one >>>>>>> here: https://dl.dropboxusercontent.com/u/2785709/outbox/mai >>>>>>> linglist/design_kavous.ods >>>>>>> >>>>>>> >>>>>>>> >>>>>>>> 5) Should I run PALM separately for permuting within-block (using >>>>>>>> -eb -vg and -within options) and permuting whole-block (using -eb -vg and >>>>>>>> -whole options)? >>>>>>>> >>>>>>> >>>>>>> If you take my suggestion of not violating compound symmetry, then >>>>>>> there will be no -within, only -whole. That said, in general, it's possible >>>>>>> to do both things simultaneously (see the Multi-level block permutation >>>>>>> paper >>>>>>> <http://www.sciencedirect.com/science/article/pii/S105381191500508X> >>>>>>> ). >>>>>>> >>>>>>> Hope this helps! >>>>>>> >>>>>>> All the best, >>>>>>> >>>>>>> Anderson >>>>>>> >>>>>>> >>>>>>> >>>>>>>> >>>>>>>> Best regards, >>>>>>>> Kavous >>>>>>>> >>>>>>>> >>>>>>>> On Thu, Jun 1, 2017 at 11:07 AM, Anderson M. Winkler < >>>>>>>> [log in to unmask]> wrote: >>>>>>>> >>>>>>>>> PS: If you choose to use PALM, with the 4 timepoints, consider >>>>>>>>> using the "-ise" option. It doesn't require compound symmetry, but requires >>>>>>>>> that the errors themselves are symmetrical (i.e., have a symmetrical >>>>>>>>> distribution around zero). >>>>>>>>> >>>>>>>>> On 30 May 2017 at 23:23, Anderson M. Winkler < >>>>>>>>> [log in to unmask]> wrote: >>>>>>>>> >>>>>>>>>> Hi Kavous, >>>>>>>>>> >>>>>>>>>> What are the research hypotheses? If it's about changes over >>>>>>>>>> timepoints, and interaction group by timepoint, then this needs the >>>>>>>>>> assumption of compound symmetry, which is fine for 2 timepoints, but >>>>>>>>>> becomes harder with 4. If you want to make that assumption, then you can >>>>>>>>>> use PALM, defining one exchangeablity block per subject, and permuting >>>>>>>>>> within-block and also whole-block (for the interactions. >>>>>>>>>> >>>>>>>>>> If you can't make the compound symmetry assumption, consider >>>>>>>>>> Bryan Guillaume's toolbox called SwE. It seems the most recent version is >>>>>>>>>> on GitHub: https://github.com/BryanGuillaume/SwE-toolbox >>>>>>>>>> >>>>>>>>>> If, however, the hypothesis is about group differences regardless >>>>>>>>>> of changes in time, then randomise can be used directly, with the option >>>>>>>>>> --permuteBlocks. This is the Example 6 of the randomise paper: >>>>>>>>>> http://www.sciencedirect.com/science/article/pii/S105 >>>>>>>>>> 3811914000913 >>>>>>>>>> >>>>>>>>>> Hope this helps! >>>>>>>>>> >>>>>>>>>> All the best, >>>>>>>>>> >>>>>>>>>> Anderson >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> On 30 May 2017 at 05:05, Kavous <[log in to unmask]> >>>>>>>>>> wrote: >>>>>>>>>> >>>>>>>>>>> Hi FSLers, >>>>>>>>>>> >>>>>>>>>>> I'm going to analyze a longitudinal VBM between 3 groups and >>>>>>>>>>> within 4 time-points. >>>>>>>>>>> >>>>>>>>>>> By reading following discussions, I know that in 2 time-points >>>>>>>>>>> analysis I have to use all my subjects' data to make a study-specific >>>>>>>>>>> template and subtract pre-post data after smoothing for randomise analysis. >>>>>>>>>>> >>>>>>>>>>> https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=FSL;d6651f48.1008 >>>>>>>>>>> https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=FSL;3dc8868b.1408 >>>>>>>>>>> >>>>>>>>>>> However, my question is that is there any way to consider all 4 >>>>>>>>>>> time-points in a single analysis? >>>>>>>>>>> >>>>>>>>>>> Thanks, >>>>>>>>>>> Kavous >>>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> >>>>>> *PhD Candidate* >>>>>> >>>>>> *Center for Human Engaged Computing <http://xrenlab.com/>* >>>>>> >>>>>> *782-0003, Kochi University of Technology, Kami City, Japan* >>>>>> >>>>>> *Skype: kavus.salehzadeh* >>>>>> >>>>> >>>>> >>>> >>> >> >