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Dear Mark, I see you like to make assumptions. So let us assume that I am an high school student who does not understand a thing about DTI. Moreover I do not understand the basics of probabilistic tractography. So in order to understand more I read Diffusion MRI book (edited by Berg and Behrens) and also papers that deal with different tractography methods. So now I understand a little bit of tractography but still there are unclear issues that I would like to clarify with the experts in the field- that is the reason I contacted FSL community. If you see the title of my mail it is "Values in matrix1 after probtrackx"; The specific question I have is what those values mean? Probabilistic tractography aim to quantify uncertainty on the PDD and build a connectivity distribution. Now if you go and check matrix obtained from network1 option in probrackx2 you will see that the matrix contain values in the range of 1-1,000,000 and beyond. You mentioned in you last e-mail that: "Uncertainty in direction at any point in the brain will enhance the uncertainty in the tractography from that point onwards for any tracks that pass through that point." How can we learn about this uncertainty from the matrix values? Indeed at the NIH there are many experts who are always available for discussion about all aspects of health and science. However, is FSL/Probtrakcx a NIH application??? Finally I would like to show Timothy Behrens's response to my question : "if you have posted it to the FSL list then you should get an answer soon. It is a very effective community forum. You will understand that with more than 5000 users, there is no way I can personally answer every question and hope to maintain a research career!" This is the answer from the researcher who is the first author on the NeuroImage paper from 2007 about probabilistic tractography, and also the one who wrote the chapter MR diffusion tractography with Saad Jbadi in the book mentioned above. Avner On 1/18/14 4:58 AM, "Mark Jenkinson" <[log in to unmask]> wrote: > Hi, > > Artifact "correction" methods don't fully remove all artifacts, so you cannot > rule out the possibility that artifacts are causing the things you are seeing > just because you have run artifact correction. Such methods remove a lot of > the effect of artifacts but not absolutely everything. > > I'm not sure what you mean by "steps" but the samples in probtrack refer to > individual streamlines (that are selected from the probability distribution of > possible streamlines). Uncertainty in direction at any point in the brain > will enhance the uncertainty in the tractography from that point onwards for > any tracks that pass through that point. > > You definite cannot make categorical statements such as "more samples mean[s] > more disease". > As I said, there are a *lot* of things that can influence tractography results > and you really should discuss you particular case, with your particular > subjects and you particular data acquisition, with someone who is experienced > with tractography. There certainly should be such people in the NIH. > > All the best, > Mark > > > On 17 Jan 2014, at 13:10, "Meoded, Avner (NIH/NINDS) [E]" > <[log in to unmask]> wrote: > >> 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