 |
 |
This can be seen from the technical papers that describe the fiber orientation and tractography method, particularly Behrens et al 2003 and Behrens et al 2007. On 1/19/14 6:31 AM, "Meoded, Avner (NIH/NINDS) [E]" <[log in to unmask]> wrote: >P.s > >You mentioned that FA does not have direct influence on tractography, can >you give a reference for that? > >Thank you > >Avner > > >On 1/18/14 6:51 PM, "Matt Glasser" <[log in to unmask]> wrote: > >Hi Paul, > >That was a potential explanation for what might seem like >counter-intuitive results I just thought up on a Saturday morning (there >might be plenty of others!). I suppose someone could test if this effect >is real by looking at the HCP data and comparing a group with the >smallest brains to a group of the largest brains. One important variable >would be whether the data were seeded from standard space (in which case >the number of streamlines sent out would be constant across subjects) or >native space (where the number of streamlines sent out would vary and >potentially counteract the effect because a bigger brain would have more >seeded streamlines). > >Peace, > >Matt. > >From: Chou Paul <[log in to unmask]> >Reply-To: FSL - FMRIB's Software Library <[log in to unmask]> >Date: Saturday, January 18, 2014 5:36 PM >To: <[log in to unmask]> >Subject: Re: [FSL] Values in matrix1 after probtrackx > >Dear Matt > >According to your last point of the reply, are there any references or >approaches about how to adjust for the "smaller brain" issue in such >tractography research ? I am focus on neurodegenerative disease and this >problem seems very important to this field. Would you provide me some >advices on this issue ? > >Thank you > >Best > >Paul > >> Date: Sat, 18 Jan 2014 12:39:51 -0600 >> From: [log in to unmask] >> Subject: Re: [FSL] Values in matrix1 after probtrackx >> To: [log in to unmask] >> >> 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