On 1/17/2020 10:39 AM, Graduate Imaging wrote:
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Hello,
I recently ran a vertex wise analysis on two different projects the first had three groups with three co-variates that looked at if group membership was associated with brain volume. The second project had two groups with three co-variates looking at psychiatric x BMI interaction effect on brain volume. I'm interested in running ROI analyses in matlab to ensure the same matrices were used.
To run the ROI analyses I'm going to important the design matrix from the vertex wise analysis and use the fast_glmfit and fast_fratio commands as shown below: X = load('Xg.dat'); C = load('C.dat'); y = load('ROI.dat'); text file containing participant ROI values from the DKT atlas. [beta rvar] = fast_glmfit(y,X); [F pvalues] = fast_fratio(beta,X,rvar,C
My first question is the beta values that are calculated from fast_glmfit are unstandardized is there anyway way to have it compute standardized values? I think this is how you would compute that betastddev = sqrt(rvar*diag(inv(X'*X))); betastandard = beta./betastddev; When I try running this in Matlab I get the Error using * incorrect dimensions for matrix multiplication message. That means that the number of items in y (ie, number of subjects in ROI.dat) is different than the number of rows in X (number of subjects in the fsgd). What are the sizes of y and X?
My second question is when I ran the ROI analyses looking at the diagnosis x BMI interaction effect it outputs an array of beta values for each regressor. In addition to each groups beta value (for BMI) I'm interested in the beta value of the interaction (ie diagnosis x BMI). I was wondering how do i go about obtaining this? Would I have to create a new matrix with the interaction term included in it?
If so would the analysis essentially be one group with the diagnosis x BMI interaction term plus the three co-variates? If you are using an FSGD file and have used DODS, then you can create an interaction contrast. Send me your fsgd file if you want further help Yes I was using DODS approach for this analysis, I've attached my FSGD file to this email.
You have 2 groups and 4 coviariates so 10 regressors. The dx BMI interaction would need a contrast matrix 0 0 1 -1 0 0 ... the rest 0s BTW, sex should not be a covariate. You really need to have four groups, P-male, P-female, HC-male, and HC-female (which would change the contrast above) Also, I would normalize the age and ICV
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