Hi all,
I'm making a ROI-ROI analysis using the contrast [1 0 0 0 0] (SUBJECTS, AGE, MOTION, CARDIO, MEAN_CONNECTIVITY). The database has 535 participants with the age ranging 18-88.
The ROI.mat file shows the statistical results [F, dof, h, p, h] according to the contrast, but the connectivity matrix values "y column" is not adjusted by the covariates. I would to calculate the connectivity matrix in which I can see the adjusted connectivity value for each participant. For doing this, I'm considering the subjects-by-connections matrix (Z) of connectivity values, and a subjects-by-one vector of each covariate (X), and I'm computing:
X = [x1, x2, x3, x4, x5]; % design matrix (each column is a covariate)
B = X\Z; % regressor coefficients
B(1,:) = 0;
Zc = Z - X*B; % Z values adjusted at all covariates
(Script Adapted form the original script: https://www.nitrc.org/forum/message.php?msg_id=20577)
When I do that, I get the adjusted matrix, however:
1) The effect of age disappeared from all the connections. For example, If I plot the connectivity between ROI1 x ROI2 against age, the correlation effect is zero. But if I ask for the effect of age [0 1 0 0 0] on the same connection, the results explorer window of Conn shows a positive effect of age for that ROI-ROI connection. Why is there this difference?
2) The script seems to be working well, but I'm not 100% sure of that. The adjusted matrix shows complex numbers for some values, which make the average connectivity of a given pair of ROI slightly different from the column "h" of the ROI.mat (which stores the average correlation for a given pair of ROI). I used the conn_glm script for checking the stats for the adjusted script. The results is almost the same as the result shown in the ROI.mat (Since the complex numbers are making the average/variance slightly, there is a difference too).
Am I doing the right thing here? What should I for getting the subjects-by-connections matrix (Z) of connectivity values adjusted by the covariates and checking its stats?
Thank you very much for your attention.
I'm making a ROI-ROI analysis using the contrast [1 0 0 0 0] (SUBJECTS, AGE, MOTION, CARDIO, MEAN_CONNECTIVITY). The database has 535 participants with the age ranging 18-88.
The ROI.mat file shows the statistical results [F, dof, h, p, h] according to the contrast, but the connectivity matrix values "y column" is not adjusted by the covariates. I would to calculate the connectivity matrix in which I can see the adjusted connectivity value for each participant. For doing this, I'm considering the subjects-by-connections matrix (Z) of connectivity values, and a subjects-by-one vector of each covariate (X), and I'm computing:
X = [x1, x2, x3, x4, x5]; % design matrix (each column is a covariate)
B = X\Z; % regressor coefficients
B(1,:) = 0;
Zc = Z - X*B; % Z values adjusted at all covariates
(Script Adapted form the original script: https://www.nitrc.org/forum/message.php?msg_id=20577)
When I do that, I get the adjusted matrix, however:
1) The effect of age disappeared from all the connections. For example, If I plot the connectivity between ROI1 x ROI2 against age, the correlation effect is zero. But if I ask for the effect of age [0 1 0 0 0] on the same connection, the results explorer window of Conn shows a positive effect of age for that ROI-ROI connection. Why is there this difference?
2) The script seems to be working well, but I'm not 100% sure of that. The adjusted matrix shows complex numbers for some values, which make the average connectivity of a given pair of ROI slightly different from the column "h" of the ROI.mat (which stores the average correlation for a given pair of ROI). I used the conn_glm script for checking the stats for the adjusted script. The results is almost the same as the result shown in the ROI.mat (Since the complex numbers are making the average/variance slightly, there is a difference too).
Am I doing the right thing here? What should I for getting the subjects-by-connections matrix (Z) of connectivity values adjusted by the covariates and checking its stats?
Thank you very much for your attention.