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>







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Donders Institute for Brain, Cognition and Behaviour

NIJMEGEN, The Netherlands

http://www.ru.nl/neuroimaging

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