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Hi Anderson, I'm back for some follow-up questions. I want to test for the main effects and interaction between my two within-subject factors: Factor A with 3 levels (C, V and M) and Factor B with 3 levels (scan number; S1, S2, S3). I used the GLM matrix that you suggested and loaded the following contrasts (see attached). I'm currently looking at tfce_corrp_fstat3.nii.gz to see which regions show an interaction between factors A and B. Are the f-contrasts set up correctly to test what I want? Thanks for the help. Regards, Florencia. 2018-03-25 23:14 GMT-03:00 Florencia Jacobacci <[log in to unmask] >: > Hi Anderson, thanks again for such a detailed answer. > It is very much appreciated. > Best regards, > Florencia. > > 2018-03-25 21:34 GMT-03:00 Anderson M. Winkler <[log in to unmask]>: > >> Hi Florencia, >> >> Please see below: >> >> >> On 19 March 2018 at 09:56, Florencia Jacobacci < >> [log in to unmask]> wrote: >> >>> Hi Anderson, thank you so much for taking the time to answer my >>> question. >>> I had mainly used the 2x2 within-subject ANOVA in this example to set up >>> my matrix (https://pdfs.semanticscholar.org/a0a0/6723f4d1fb6ce2dcfeb57 >>> 4ce7ca70502ba3e.pdf). >>> >>> Is this the right way to do it, then? >>> >> >> The examples shown in the book chapter depend on the software >> implementation being able to use the contrasts to figure out what is >> nuisance and what is not. Strictly, the models in the examples aren't >> really adequate in that they do not allow estimation of the parameters in >> an unambiguous way. In Matlab, for example, it's easy to show that mldivide >> (backslash division) yields different but just as valid solutions as the >> pinv (pseudoinverse). The best way to avoid these ambiguities is to make >> sure that the design is not rank deficient from the start. >> >> The models in the chapter should work fine in SPM, though, and I believe >> the original (or some earlier version of the text) relates to ANOVAs in >> SPM, in which models as these are not a problem. Move to a different >> software and results may be different. The reader of a paper has no way of >> knowing so the best is really to spell out the full model, and make sure >> the coding leaves no linearly dependent columns. >> >> >> >>> (note: I hadn't included the exchangeability blocks in the matrix in the >>> excel file but I was, indeed, using one per subject in the GLM design) >>> https://docs.google.com/spreadsheets/d/1zTJywYURacxD6q-23TeW >>> 56r4V2D52bk3uFDAzWCwgEg/edit?usp=sharing >>> >>> >> Looks fine if you're following the previous email. >> >> >> >>> Sorry for my naivety, why is this design rank deficient? I thought each >>> column would capture the mean for each subject. What would be the >>> difference with the corrected matrix you suggest, besides having one less >>> EV? >>> >> >> The original design has, say N columns, but these can be represented in a >> R^(N-1) space with no loss of information. There is an "extra" dimension >> there. A way of saying is that at least one of the EVs can be fully >> represented by a linear combination of all others. Here, the sum of all >> subject-specific EVs is the same as the sum of all other EVs. >> >> >>> >>> The contrasts will remain the same since I haven't touched those EVs, >>> right? (just removing the extra EV) >>> >> >> Correct. >> >> >>> >>> My 3 scans per condition: S1, S2, S3 are set at 0, 30 min and 24 hs. I >>> guess the most ideal would be to assume a first-order autoregressive >>> covariance matrix. Is this a possibility? In case it's not, am I still safe >>> assuming compound symmetry? >>> >> >> It's hard to tell really. Do you expect that dependences between 0-30 min >> are the same as 0-24h? For structural scans this is likely ok, maybe less >> so for functional scans. I can't tell which way to take because I don't >> know either. But you can resort to sign-flipping as long as the >> distribution of the residuals at the group level is symmetric (for fMRI his >> is usually the case). >> >> >>> >>> Does PALM's corrcon use Bonferroni to correct for the multiple contrasts? >>> >> >> PALM uses synchronized permutations across contrasts to allow for the >> correction, such that non-independence between contrasts is taken into >> account. In randomise one would use Bonferroni. If contrasts are >> independent, then -corrcon and Bonferroni lead to similar results. >> >> All the best, >> >> Anderson >> >> >>> >>> Thanks again! >>> Regards, >>> Florencia. >>> >>> >>> 2018-03-18 18:19 GMT-03:00 Anderson M. Winkler <[log in to unmask]>: >>> >>>> Hi Florencia, >>>> >>>> This design is rank deficient I'm afraid. However, it can be fixed >>>> easily: for each of the subject specific EVs, instead of coding as 1 or 0, >>>> code as 1 and -1 for any two consecutive subjects. For N subjects, there >>>> should be N-1 such EVs. See for example how the subject-specific EVs were >>>> coded in this other post: https://www.jiscmail.ac. >>>> uk/cgi-bin/webadmin?A2=FSL;f0151f95.1710 >>>> >>>> The EVs the test the various experimental conditions can be kept fixed. >>>> >>>> Note that in this design, only within-subject effects are allowed, >>>> which I believe is what you want. Use one exchangeability block per >>>> subject, such that permutations happen only within subject. This assumes >>>> compound symmetry, though, which may or may not be tenable in your data. >>>> >>>> Regarding correction over contrasts, in PALM use -corrcon. There's no >>>> equivalent option in randomise and you'd need to use Bonferroni. If all >>>> contrasts are orthogonal, this should lead to equivalent results. There's >>>> no need for and F-test. >>>> >>>> All the best, >>>> >>>> Anderson >>>> >>>> >>>> On 13 March 2018 at 09:57, Florencia Jacobacci < >>>> [log in to unmask]> wrote: >>>> >>>>> Dear FSLers, >>>>> >>>>> I am working with a longitudinal set of DWI images which has a >>>>> complete within-subjects design. >>>>> For each subject I have 3 conditions (C, V and M) and 3 scans per >>>>> condition: S1, S2, S3. >>>>> >>>>> After adapting some of the examples shown on the webpage and going >>>>> through this mailing list I've managed to set up my GLM matrix and my >>>>> contrasts for a 2 way repeated measures ANOVA with 3 levels for each >>>>> within-subject factor. >>>>> >>>>> I'm interested in contrasting changes across sessions for each >>>>> condition (VS1-VS2, VS1-VS3, VS2-VS3, etc) but also on the interaction. >>>>> Particularly, I want to see if the difference S1-S2, S2-S3 and S1-S3 >>>>> changes across conditions. C is my control so I've set up interaction >>>>> contrasts comparing these differences in M and V to C. >>>>> I have an a priori hypothesis on the direction of the changes I'm >>>>> looking for but I still would like to explore changes in both directions. >>>>> That is to say I'm interested in both S1-S2 and S2-S1, S3-S1 and S1-S3, >>>>> etc. so I've set up contrasts in all directions. >>>>> The same with the interaction contrasts: I'd like to have info on >>>>> CS1-CS2=VS1-S2 and CS2-CS1=VS2-VS1, etc. >>>>> >>>>> I have some doubts on the F-tests that I designed to test for main >>>>> effects of condition, sessions and interactions. Also, I'm not sure if I >>>>> should use alphaa/2 for significance in the f-tests, since it is two-tailed. >>>>> >>>>> I am using randomise. Another doubt that I have is about correction >>>>> for multiple contrasts. I should divide my alpha by the number of contrasts >>>>> to get the corrected alpha value, right? >>>>> Or would it be best to try PALM and use the option it has to correct >>>>> for multiple contrasts? >>>>> >>>>> Here are the links to the matrices that I have set up so far. Any >>>>> comments and corrections will be greatly appreciated. >>>>> >>>>> Link to GLM matrix >>>>> https://drive.google.com/open?id=1oAjZ4pYRtnkvHticzvpD1f90-R-KC2fc >>>>> >>>>> Link to Contrasts Matrix >>>>> https://drive.google.com/open?id=160sZnirkNDp4JILEKiRInS6mL6VpfLCQ >>>>> >>>>> I hope someone with more experience on this subject can help me >>>>> clarify these doubts. >>>>> Thanks in advance, >>>>> Florencia >>>>> >>>>> -- >>>>> Ing. Florencia Jacobacci. >>>>> PhD student >>>>> >>>>> Instituto de Fisiología y Biofísica (IFIBIO) - Bernardo Houssay >>>>> Laboratorio de Fisiología de la Acción, Facultad de Medicina >>>>> Universidad de Buenos Aires >>>>> >>>>> http://www.physiologyofactionlab.info/en/about-the-lab/ >>>>> https://ar.linkedin.com/in/florenciajacobacci/en >>>>> >>>> >>>> >>> >>> >>> -- >>> Ing. Florencia Jacobacci. >>> >>> Instituto de Fisiología y Biofísica (IFIBIO) - Bernardo Houssay >>> Laboratorio de Fisiología de la Acción, Facultad de Medicina >>> Universidad de Buenos Aires >>> Paraguay 2155, C.A.B.A.(C1121ABG), Argentina >>> ☎ 5950 9500 int 2132 >>> >>> http://www.physiologyofactionlab.info/en/about-the-lab/ >>> https://ar.linkedin.com/in/florenciajacobacci/en >>> >> >> > > > -- > Ing. Florencia Jacobacci. > > Instituto de Fisiología y Biofísica (IFIBIO) - Bernardo Houssay > Laboratorio de Fisiología de la Acción, Facultad de Medicina > Universidad de Buenos Aires > Paraguay 2155, C.A.B.A.(C1121ABG), Argentina > ☎ 5950 9500 int 2132 > > http://www.physiologyofactionlab.info/en/about-the-lab/ > https://ar.linkedin.com/in/florenciajacobacci/en > -- Ing. Florencia Jacobacci. PhD student / Becaria doctoral Instituto de Fisiología y Biofísica (IFIBIO) - Bernardo Houssay Laboratorio de Fisiología de la Acción, Facultad de Medicina Universidad de Buenos Aires Paraguay 2155, C.A.B.A.(C1121ABG), Argentina ☎ 5950 9500 int 2132 http://www.physiologyofactionlab.info/en/about-the-lab/ https://ar.linkedin.com/in/florenciajacobacci/en