 |
 |
Hi The raw DTI data was corrected for artifacts. As you mentioned less uncertainty may enhance measures of connectivity. But in my case I documented reduced FA and not increased FA, the latter is seen perhaps in regions with reduced crossing fibers. Now my question is specific to probabilistic tractography: number of samples obtained from probtrackx between to regions mean number of "steps" track does; are those "steps" depends on the uncertainty? So at the end more samples mean more disease? Thank you Avner On 1/17/14 7:39 AM, "Mark Jenkinson" <[log in to unmask]> wrote: > Hi, > > These are not simple questions and it will depend a lot on the nature of your > data - SNR, artefacts, amount of movement, etc. There are also some potential > biological possibilities, such as reduction in crossing tracts, which can > enhance measures of connectivity (since there is less uncertainty in the > crossing region) without meaning that the axonal tract is biologically > "stronger". You should look very critically at your data and show it to > people who are experienced with diffusion analysis. > > All the best, > Mark > > > On 16 Jan 2014, at 18:08, "Meoded, Avner (NIH/NINDS) [E]" > <[log in to unmask]> wrote: > >> 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