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Hi Matt and Andreas, Thanks again for your very patient, helpful and informative reply. Best, Huadong 2011/2/13 Andreas Bartsch <[log in to unmask]> > Hi, > > well - my only reason was that I felt the ratio was a more intuitive number > in the particular scenario. In my cases, the reduction of trackability - > i.e. of the waytotal - relative to the normal contralateral hemisphere was > mainly due to perifocal tumor edema. I would use Matt's approach if I were > facing a lateralization of a tract, for example. For a connection such as > the CST that was harder to track due to edema I wanted to assume symmetry > prior to the impact of the pathology (knowing that this may not have been > the case and contribute to the variablility, but any other approach seemed > similarly stipulative). > All will depend on the particular conditions you are examining. For > example, an extra-axial tumor (i.e. outside the parenchyma, such as a > meningeoma) could be conceived to compress a tract reducing the waytotal > without altering the 'true connectivity strength' (whatever that may be, as > Saad already has pointed out). > Cheers- > Andreas > ________________________________ > Von: FSL - FMRIB's Software Library [[log in to unmask]] im Auftrag von > Matt Glasser [[log in to unmask]] > Gesendet: Sonntag, 13. Februar 2011 20:28 > An: [log in to unmask] > Betreff: Re: [FSL] AW: [FSL] Normalizing waytotal itself? > > So I also used the waytotals to calculate an asymmetry index (Waytotal Left > – Waytotal Right) / (Waytotal Left + Waytotal Right). I did not normalize > the waytotal values by the total number of samples sent out. Because in my > case I believed that I was tracking entire fascicles, if a larger mask were > required to track an entire fascicle, then I believed that the pathway was > indeed larger. I was using ROIs that were a single voxel thick and were > orientated perpendicular to the pathway so as to cut it in cross section. > The only thing to be careful of is to ensure that you are not using a > larger ROI because you are cutting the pathway obliquely. I have groups of > 30-35. > > Peace, > > Matt. > > ________________________________ > From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On Behalf > Of hd x > Sent: Sunday, February 13, 2011 11:58 AM > To: [log in to unmask] > Subject: Re: [FSL] AW: [FSL] Normalizing waytotal itself? > > Hi Andreas & Matt, > > Thank you very much for the reply. May I ask some more specific questions? > > To Andreas: Was there a special reason for you to calculate asymmetry index > by useing the ratio of the waytotals from the tracking in one hemisphere > diveded by the other? I asked because in my results regression analysis > suggests the absolute difference between the left and right waytotal is a > better explanation for the individual variance on behavioural performance. > > To Matt：May I ask how large your group is? My idea of using waytotal to > represent the connectivity strength was actually inspired by your replies as > mentioned in the previous email. Since I found significant correlations > between the the waytotal and the individual behavioural performance, the > idea is even more convincing to me now. But since you mentioned that it's > better to normalize pathways by the total number of samples you sent out, do > you think I shall also normalize the waytotal by the total number of samples > sent out? > > Best, > Huadong > > > > > On 10 February 2011 19:29, Matt Glasser <[log in to unmask]<mailto: > [log in to unmask]>> wrote: > I have found that normalizing by waytotal tends to underestimate pathway > asymmetries, because the waytotal values themselves are highly correlated > with pathway asymmetry (therefore, if you normalize by waytotal you remove > the asymmetry you are looking for). If you want the pathways to be about > the same size on both hemispheres (or across subjects) normalizing by > waytotal makes sense. If you want to compare pathways across hemispheres > looking for asymmetries, I think it is better to normalize by the total > number of samples you sent out (and asymmetric pathways will tend to have > different sized ROIs if you are defining the whole pathway, but the affect > on asymmetry is not as strong as the waytotal). > > Across large groups of subjects I get very reasonable asymmetry values just > using the waytotal value for each pathway (e.g. in comparison with what > others have reported in the literature). There is certainly variation in > the waytotal values across subjects as Andreas notes, but it good quality > data that doesn't have pathologies this shouldn't be too bad and reasonable > trends are apparent. Waytotal certainly isn't a proper measure of > strength, > but it is correlated, particularly across a large group. This work was a > part of a side project that hasn't made it into a publication yet, so > unfortunately I can't give you a reference to cite. > > Peace, > > Matt. > > -----Original Message----- > From: FSL - FMRIB's Software Library [mailto:[log in to unmask]<mailto: > [log in to unmask]>] On Behalf > Of Andreas Bartsch > Sent: Thursday, February 10, 2011 4:59 AM > To: [log in to unmask]<mailto:[log in to unmask]> > Subject: [FSL] AW: [FSL] Normalizing waytotal itself? > > Hi, > > my figure so I give it a shot: > In that figure the waytotals were NOT used directly. Instead I used the > ratio of the waytotals from the tracking in one hemisphere diveded by the > other (see the legend). Because masks were equally sized in both > hemispheres > (p. 433 in the book), there was no need to 'normalize' further. So this > would be an easy way to to it. Still I agree with Saad: anatomicofunctional > correspondence should rule over mask sizes - size does not matter! > Also note that I've excluded two outliers. Overall, in my experience the > waytotals can easily differ by a factor of 5 between hemispheres of the > same > subject and that must not have any biological meaning even if you have > high-quality scans. Still, as the figures shows normalizing to the > contralateral hemisphere may be useful. > Note that my measure was neurological, behavioural scores may show weaker > or > no correlations. > Hth- > Andreas > ________________________________ > Von: FSL - FMRIB's Software Library [[log in to unmask]<mailto: > [log in to unmask]>] im Auftrag von hd x > [[log in to unmask]<mailto:[log in to unmask]>] > Gesendet: Donnerstag, 10. Februar 2011 11:17 > An: [log in to unmask]<mailto:[log in to unmask]> > Betreff: Re: [FSL] Normalizing waytotal itself? > > Dear Saad, > > Thanks a lot for your detailed reply. However, I didn't want to use the > normalized fdt_faths to represent the connectivity strength, but use the > waytotal directly as suggested by "Tractography for surgical targeting in > Heidi's & Tim's book. Figure 19.12". This is also suggested by Matt in one > of his replies to this maillinglist: "What is it that you are trying to > show? That subjects with a stronger/weaker pathway have some difference in > behavioral scores? Honestly, I might correlate the waytotal values > themselves (if you are using at least one waypoint mask) with the > behavioral > score." > > Thus my question was if I shall normalize Waytotal itself by the sum of the > seed and target sizes. But not how to normalizde fdt_path by waytotal. I'm > sorry I didn't made this very clear in my first email in the background of > so many discussion about the latter in this maillinglist. > > I really hope I can get some comments on that since I didn't see much > discussion about it yet. > > Best, > Huadong > > > On 9 February 2011 12:18, Saad Jbabdi > <[log in to unmask]<mailto:[log in to unmask]><mailto: > [log in to unmask]<mailto:[log in to unmask]>>> wrote: > Dear Huadong, > > 1) Waytotal normalisation > When you run probtrackx, you are effectively building up a distribution > using sampling, in the same way you may build a histogram by counting > samples that fall into categories (bins). > So for example, the fdt_paths file gives you the histogram for the spatial > distribution of connections, and waytotal counts the number of samples that > satisfy certain conditions (waypoint/exclusion criteria). When you divide > the fdt_paths results by waytotal, you are acknowledging the fact that > fewer > samples have been used to build up the spatial histogram than the maximum > possible number (i.e. #seed voxelsX#samples per voxel). You need to do that > in order for your histograms to be comparable across subjects/hemispheres. > In the same way, if you have a histogram of counts where you've used 100 > samples, you cannot compare the values to a histogram that used only 50 > samples, you can compare the odds but not the raw values. > > > 2) Mask size and other concerns > Although many people think that the mask size bias is a big worry, I > personally think there are other more fundamental issues. > When you calculate the probability of a connection from a seed to a target, > then if the target is appropriately matched across subjects/hemispheres > (e.g. it is the same anatomo-functional entity), then you are comparing > like > with like, so you shouldn't worry about mask size. However, it is true that > the probabilities may be higher for bigger target masks, which is a problem > that comes from the very nature of the probabilities that you calculate. > In probtrackx, you are _not_ calculating tract "strength" (whatever that > is), but instead you are calculating the level of confidence that you have > on the trajectory from the seed to the target (of course through the high > diffusion directions, goes without saying!). So you would expect your > confidence to be higher if, for example, your target area is bigger (other > things may also increase or decrease your confidence). If that is not > satisfactory when it comes to regressing against behavioural data, it is > not > because of the size bias, but because of the quantity that you are > calculating, ie your confidence on the connection. > > So even if you manage to somehow take into account the size of the mask in > the probability (eg if you had a model for the conditional probability on > mask size), you are still faced with the problem that a higher confidence > on > a connection does not necessarily imply a stronger functional connection > between two areas. You may need to consider other quantities (e.g. > microstructural or functional models), that you may calculate using the > tract distribution, and disambiguate low confidence on tract from low > functional connectivity etc. > > Cheers, > Saad. > > > > On 9 Feb 2011, at 10:44, hd x wrote: > > Dear list, > > From previous discussions, I got the impression that it's good to use > waytotal as the connectivity index when you want to correlate pathway > strength with individual behavoural performances and when you are using at > least one waypoint mask. (See > > http://mail.google.com/mail/?shva=1#search/label%3Afsl+interpretation+waytot > al/125995c081e68cd7 > > http://mail.google.com/mail/?shva=1#search/label%3Afsl+threshold+pathway/12a > 2b2c25aec6d52) > > I'm now working on a study which investigates the correlation between > pathway laterality and individual behavioural performances. I have a seed > mask, a waypoint mask and a target mask (which is the same brain area as > the > waypoint mask covers) in each of the hemispheres. The right masks are > homologous areas to the left masks. Then with the left masks I traced my > left pathway and the right masks the right pathway. I used the waytotal of > the pathway to represent the pathway strength and calculated the pathway > laterality accordingly by taking the absolute difference between the left > and right waytotal. > > However, I got another concern. The masks I used for the left hemisphere > are > not of the same size of the right ones, although all subjects used the same > set of left and right masks. In this case, I'm wondering if I should > normalize the waytotal based on the mask size before I calculate the > lateralization index. If I should, what would be the right way to do it? > Would it be good to divide the waytotal by the sum of the size of seed and > target masks? > > Your inputs will be highly appreciated. > > Best, > Huadong > > > > > > -- > Saad Jbabdi > University of Oxford, FMRIB Centre > > JR Hospital, Headington, OX3 9DU, UK > (+44)1865-222466 (fax 717) > www.fmrib.ox.ac.uk/~saad<http://www.fmrib.ox.ac.uk/~saad>< > http://www.fmrib.ox.ac.uk/~saad> > > > > -- > ------------------------------------------------------------ > H Xiang PhD Candidate > Donders Institute for Brain, Cognition and Behaviour > NIJMEGEN, The Netherlands > http://www.ru.nl/neuroimaging > &------- > Founding Webmaster > I Love Brain Science > http://52brain.com >