Dear FSL list,

I have worked with fMRI data for a number of years, and I'm now also starting to analyze DTI data. I have a number of questions that I haven't been able to find the answers to.

1) In the function dtifit in FSL, a general linear model is setup (Y = XB + e, just like in fMRI analysis) to estimate the intercept and the 6 tensor components. The "raw" measurements are not used, but instead the logarithm of the measurements are used in the vector Y. I would prefer to setup a generalized linear model, instead of a general linear model, and perform the regression with a logarithmic link function. However, when doing so it seems like there is a problem with colinearity between the three of the tensor regressors and the regressor for the intercept. The reason for this seems to be that the diffusion gradients always have unit length, and therefore sum to 1 (thereby being correlated with the intercept regressor). I cannot find any paper that discusses this problem, is it because everyone uses the general linear model with the logarithm of the measurements? It will of course work, but I suspect that the tensor estimates will have a high variance, especially if the number of measurements collected with a b-value of 0 is very low.

One solution could be to separately estimate the intercept, and then proceed with a generalized linear model with 6 regressors instead of 7.

2) Is there any work done on using linear regression methods without first taking the logarithm of the measurement values? I'm currently not interested in non-linear methods, only linear.

3) Is there any work done on actually using the variance of the tensor components / FA values for each subject? In fMRI it is standard to calculate t-scores, where the variance is needed. In the FSL function FLAME, for example, the variance of each subject can then be used in the group analysis of brain activity, to for example down weight subjects with a high variance. When doing group inference using FA estimates, is there a similar FSL function that uses the variance of the FA estimate for each subject?

4) In fMRI it is common to include the estimated head motion parameters in the design matrix, to further suppress effects of head motion. Is this also common to include the head motion parameters when estimating the diffusion tensor?

Regards,
Linda