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Now I learned a lot from you Thank you Matt Avner On 1/18/14 1:39 PM, "Matt Glasser" <[log in to unmask]> wrote: > I've certainly found both Mark and Tim to be extremely helpful over the > years and have learned an enormous amount from both of them. I know it is > greatly appreciated when support requests are directed to the public > e-mail list (so that others may benefit from and participate in the > discussion). > > The relationship between the number of streamlines sent out from an ROI > that reach another and the number (let alone mention functional influence) > of axons is not yet well characterized. It's worth keeping in mind that > FA (and more advanced measures like f1, f2, and f3) do not have any direct > influence on tractography. Tractography is based on sending out some > number streamlines and following samples of fiber orientation > distributions until some stopping criterion is reached (like a stop mask, > exceeding a curvature threshold, looping back on a region previously > traveled, etc). Streamlines do not increase in number depending on the > number of steps (i.e. distance) the tractography algorithm needs to take > between seed and target. One starts with a fixed number (that you set > when running probtrackx) and this represents the maximum possible value > you could get (if all streamlines travelled the whole distance between > seed and target ROIs). > > The two main applications where it has been successfully used are to build > spatial uncertainty distributions of pathways (where the voxels with the > largest number of streamlines represent the higher probability of a > pathway being located there and the voxels with fewer numbers of > streamlines represent lower probability of a pathway being located there. > The other is making comparisons between streamline counts in the same > individual, where many uninteresting factors that influence streamline > counts are controlled for. > > The use of tractography to produce grey matter to grey matter structural > connectomes is a relatively recent one. Interpreting these results across > individuals or between patients and controls relative to the various > potential confounds is quite challenging. In this case, the explanation > could be as simple as the subjects with neurodegenerative disease have > smaller (atrophied) brains and therefore the distance between ROIs is > reduced, which would increase streamline counts because streamline counts > decrease with increasing distance in a log-linear fashion (more steps > means more chances to be stopped/diverted away from the target). > > Peace, > > Matt. > > On 1/18/14 7:30 AM, "Meoded, Avner (NIH/NINDS) [E]" <[log in to unmask]> > wrote: > >> 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