Dear FSL users
I conducted a study with network1 option and then did structural connectome analysis, in patients affected with neurodegenerative disease.
I also performed TBSS and found reduced FA values in different areas in patients compared to controls.
The problem is that I have higher values stored in the connectivity matrices in patients compared to controls, and hence after connectome analyses I obtained networks that are more connected in patients. Now I know that these values cannot represent axons, but how you can explain reduced FA in patients and more streamlines evaluated in probtrackx? Or what are the numbers stored in the matrix mean? (after running seed to seed network 1)
Is this because in patients (with white matter disease, lower FA) there is more uncertainty in voxels between roi1 and roi2 and therefore we get more samples so basically tracts tend to spread more and as a results more sample? so at the end more samples which represents more uncertainty (disease)
Should I normalize the matrices in some way
Thank you
Avner
|