<div dir="ltr">Hi James,<div>I've answered each of your questions below.</div><div>Cheers,</div><div><span style="font-family:arial,sans-serif;font-size:14px">Dave</span><br style="font-family:arial,sans-serif;font-size:14px">
</div><div><span style="font-family:arial,sans-serif;font-size:14px"><br></span></div><div class="gmail_extra"><br><br><div class="gmail_quote">On 15 March 2013 23:20, James Cole <span dir="ltr"><<a href="mailto:james.cole@ucl.ac.uk" target="_blank">james.cole@ucl.ac.uk</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">
<div bgcolor="#FFFFFF" text="#000000">
Hi Dave,<br>
Thanks very much for the detailed response, that's really very
useful. I've responded to your points (with a few more questions)
inline:<div class="im"><br>
<br>
<div>On 15/03/13 03:59, David Raffelt wrote:<br>
</div>
<blockquote type="cite">
<div dir="ltr">Hi James,
<div>Just to clarify, do you mean you extract a single peak with
the largest amplitude within each voxel? Then normalise by the
max amplitude across all voxels and subjects? If so, one
potential issue is that the largest peak in corresponding
voxels across subjects is not guaranteed to belong to the same
fibre bundle. Also, during pathology you might see a decrease
in the FOD peaks of one fibre, such that they are no longer
the dominant peak in that region (and therefore are excluded
from your analysis). <br>
</div>
</div>
</blockquote></div>
Yes, that's exactly what I did. I agree that the largest peaks in
corresponding voxels between individuals may well not belong to the
same fibre bundle, but I think you could argue that this is no worse
than the 'averaging' of different fibre bundles inherent in the
tensor model. At least this way you can claim to be actually
measuring a discrete fibre bundle, rather than something potentially
not aligned to any tracts, as may happen with the primary
eigenvector in DTI. <br>
As I understand it the full AFD approach might help rectify this
tract correspondence problem, as could some other methods for
determining common orientations (e.g. FSL bedpostX). These are
things I definitely would like to explore in the long term.<div class="im"><br></div></div></blockquote><div><span style="font-family:arial,sans-serif;font-size:14px">I guess if you see any group differences in the largest peak you will need to be careful about the interpretation and check that it is not due to a miss match in peak correspondence. However, interpreting FA differences requires the same level of care anyway.</span> </div>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div bgcolor="#FFFFFF" text="#000000"><div class="im">
<blockquote type="cite">
<div dir="ltr">
<div>
</div>
<div><br>
</div>
<div>Simulations suggest the AFD along a given direction is
proportional to the intra-axonal volume of axons aligned with
that direction (at least with high b-values and long gradient
pulse durations). Using lower b-values means that the AFD is
also dependent on the signal from hindered extra-axonal water.
And therefore the interpretation of any AFD group differences
is not as clear cut. <br>
</div>
</div>
</blockquote></div>
Interesting to know. I'm generally a bit concerned about the low
bvalue in my data, but keen to try and use some
multi-fibre/non-tensor methods nonetheless, even if it's somewhat
exploratory. One advantage with the sample I'm using (Huntington's
Disease) is that there's strong post-mortem evidence that shows
demyelination, loss of axonal density and number of axons - so even
if the interpretation isn't straightforward, then group differences
are still likely to be pathological. <br></div></blockquote><div><br></div><br class=""><div><span style="font-family:arial,sans-serif;font-size:14px">You are right in that even if the interpretation isn't clear, these changes would cause a decrease in the radial DW signal and therefore a decrease in AFD.</span> </div>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div bgcolor="#FFFFFF" text="#000000"><div class="im">
<blockquote type="cite">
<div dir="ltr">
<div><br>
</div>
<div>The peak AFD amplitude is probably not a bad estimate of
the total intra-axonal volume of axons belonging to
the respective FOD lobe. However, inter-subject differences in
fibre curvature (in subject space) will influence the peak
(due to a difference in the spread of the FOD lobe). A better
measure would be to use the integral of each FOD lobe. Rob
uses the integral for his SIFT method, and I'm presenting a
new tractography-based statistical method that uses the AFD
integral at ISMRM next month.<br>
</div>
</div>
</blockquote></div>
Sounds great. I've had a read of the appendix and Algorithm 1 in
Rob's SIFT paper and can definitely see the justification for using
the integral, rather than the peak. As mention later, you're
planning to implement this in MRtrix in the future, but in the
meantime, do you have a script that runs the density sampling and
calculates the integral that you can make available?</div></blockquote><div><br></div><div><span style="font-family:arial,sans-serif;font-size:14px">We don't currently have any commands that will output the integral per FOD lobe for all voxels in the brain. This is something we could probably add to the next major MRtrix release, however by that point we also hope to release the AFD analysis tools, so you are better of using these instead of comparing the largest FOD integral.</span> </div>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div bgcolor="#FFFFFF" text="#000000"><div class="im"><br>
<blockquote type="cite">
<div dir="ltr">
<div>
</div>
<div><br>
</div>
<div>One other issue is related to transforming the AFD peak
amplitudes into template space. Modulation needs to be applied
when spatially normalising any DWI measure representative of
the restricted intra-axonal water fraction of a specific fibre
population (whether it is the AFD peak, integral, or partial
volume fractions computed using other methods, e.g. Behren's
Ball and Stick model or Assaf's CHARMED). As discussed in the
AFD paper, non-linear transformations may alter the width of a
fibre bundle, and therefore modulation is needed to preserve
the total intra-axonal volume of axons passing through any
given cross-section of a fibre bundle. <br>
</div>
</div>
</blockquote></div>
Again, modulation looks very sensible, I can see the problems that
may arise if it's not done. Would you also happen to have a script
available to do this in using the mrtrix framework?<div class="im"><br></div></div></blockquote><div><br></div><div><span style="font-family:arial,sans-serif;font-size:14px">Sorry we don't have anything currently. I have been slowly porting the FOD registration, reorientation and modulation software to MRtrix, however when this gets released will depend my spare time in the future.</span> </div>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div bgcolor="#FFFFFF" text="#000000"><div class="im">
<blockquote type="cite">
<div dir="ltr">
<div><br>
</div>
<div>Are you computing FODs using a single group-average
response function for all subjects? To do AFD analysis you
also must account for intensity variation across scans. Due to
patient- and scan-specific scanner calibrations, the magnitude
of the MR signal across different scans is not comparable. One
way to account for this is to intensity normalise the DW
images using some reference point. Ideally, the median CSF b=0
signal would be a good reference since it is unlikely to vary
during pathology. However, at the current DWI resolution it is
often hard to get a clean partial-volume-free estimate of the
CSF, particularly in young patient groups. Alternatively you
could use the median b=0 signal from brain parenchyma, however
this is not ideal since it might be affected by the pathology
being investigated. </div>
<div>The other way to account for scan intensity variation is to
perform CSD using a subject-specific response function,
however this is less robust and you might reduce your power to
detect AFD differences due to larger group variation, or the
possibility of pathology affecting voxels used to estimate the
response function.</div>
</div>
</blockquote></div>
I used a subject-specific response function. Interestingly, the
voxels that showed group differences were not those that were
generally included in the single fibre mask used to estimate the
response function. Not sure if this has any bearing the robustness
of the calculation. I think I'll try and calculate the median CSF
signal to intensity normalise the subjects, then use one response
function across all.<div class="im"><br></div></div></blockquote><div><br></div><div style="font-family:arial,sans-serif;font-size:14px">If you have a pathology-induced AFD decrease within voxels of your single fibre mask, then this might cause an artificial AFD increase to be detected in other voxels outside the mask. Using the CSF signal is the best option, however you might want to use the 95th percentile instead of the median since the majority of CSF containing voxels will contain other tissue as well. </div>
<div style="font-family:arial,sans-serif;font-size:14px"><br></div><div style="font-family:arial,sans-serif;font-size:14px">You might want check that the 95th percentile CSF intensities that you estimate are not statistically different between your two groups. This is a good sanity check to make sure that the normalisation is not going to be biased by the pathology (for example one group might have less atrophy and less robust CSF estimates than the other)</div>
<div style="font-family:arial,sans-serif;font-size:14px"><br></div><div><span style="font-family:arial,sans-serif;font-size:14px">Finally, one thing I did not mention in the previous email, is that since the AFD is proportional to the DWI signal you should perform some sort of bias field correction. I currently use N4ITK to estimate the field on the b=0 image, and apply this to correct all DW volumes.</span> </div>
<blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div bgcolor="#FFFFFF" text="#000000"><div class="im">
<blockquote type="cite">
<div dir="ltr">
<div><br>
</div>
<div>Just to clarify, the max_amp metric represents the amount
of diffusion in the peak direction only for diffusion
orientation distributions dOFDs (i.e those computed by Q-ball
and DSI). Whereas FODs model the intra-axonal volume of fibres
as a function of orientation. The amplitude of so-called
"model free" diffusion ODFs is less informative, since it
is impossible to tease out the contribution from multiple
underlying fibre populations without some type of model (which
in the case of spherical deconvolution is the response
function).<br>
</div>
</div>
</blockquote></div>
OK, so hopefully that by using the FODs in my case, I'll be able to
see something more biologically meaningful - even if I can't
directly claim to be measuring diffusion.<div class="im"><br>
<blockquote type="cite">
<div dir="ltr">
<div>
</div>
<div><br>
</div>
<div>Also, the peak FOD amplitude is more akin to the apparent<i>
radial</i> diffusivity of the respective fibre population
(and not the axial diffusivity as you suggest).
Pathology-induced changes to restriction are more likely to
influence the DW signal along radial orientations (especially
at high b-values where the axial DW signal is non-existent). A
decrease in the radial DW signal will present as a decrease in
AFD along the fibre direction, and an increase in the radial
diffusivity. Although I should warn against thinking of AFD in
terms of radial diffusivity, since thinking in terms of
diffusivity is clearly not a good way of looking at the DW
signal arising from a <i>restricted</i> diffusion
environment.<br>
</div>
</div>
</blockquote></div>
Very interesting, thanks for the clarification. On reflection, I can
see that this definitely makes more sense. In my experience, RD is
more sensitive to pathology than AD, so if I can explain this metric
to people as approximately analogous to RD, that might be more
comprehensible. However, as you say, I'll be careful not to make any
claims about diffusivity per se, given the restricted environment
that the metric is derived from.<div class="im"><br>
<br>
<blockquote type="cite">
<div dir="ltr">
<div>
</div>
<div><br>
</div>
<div>Since I'm on a roll, I might as well mention that TBSS is
not ideal for doing 'whole brain' voxel-based analysis. Aside
from the fact that it does not really analyse all brain
voxels, projecting your measure of interest onto a skeleton
based on the highest FA is problematic in crossing-fibre
regions.<br>
</div>
</div>
</blockquote></div>
I quite agree. I didn't actually use a TBSS skeleton for that very
reason. I just used the randomise tool to run the GLM stats, and
thought I'd mention TBSS as that's the context that most people are
familiar with randomise from.<div class="im"><br></div></div></blockquote><div><span style="font-family:arial,sans-serif;font-size:14px">Great.</span> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">
<div bgcolor="#FFFFFF" text="#000000"><div class="im">
<blockquote type="cite">
<div dir="ltr">
<div>
</div>
<div><br>
</div>
<div>In terms of doing AFD analysis in the future, we
are hoping to release the tools as part of the next major
MRtrix release, however this will depend on how much free time
I have in the coming months.</div>
<div><br>
</div>
</div>
</blockquote></div>
Quite understandable! Thanks again for all the advice.<span class=""><font color="#888888"><br>
James</font></span><div><div class="h5"><br>
<blockquote type="cite">
<div dir="ltr">
<div>Cheers,</div>
<div>Dave</div>
<div><br>
</div>
<div><br>
</div>
<div><br>
</div>
<div><br>
</div>
<div><br>
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<div><br>
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<div><br>
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<br>
<div class="gmail_quote">---------- Forwarded message ----------<br>
From: <b class="gmail_sendername">James Cole</b> <span dir="ltr"><<a href="mailto:james.cole@ucl.ac.uk" target="_blank">james.cole@ucl.ac.uk</a>></span><br>
Date: 15 March 2013 04:28<br>
Subject: [Mrtrix-discussion] FOD amplitude<br>
To: <a href="mailto:mrtrix-discussion@www.nitrc.org" target="_blank">mrtrix-discussion@www.nitrc.org</a><br>
<br>
<br>
<div bgcolor="#FFFFFF" text="#000000"> Dear MRtrixers,<br>
I've been using mrtrix for various things and came up with
an approach that I wanted to run by the experts. It might be
that I'm barking up entirely the wrong tree, so could use
some guidance. Regarding voxelwise metrics derived from the
spherical deconvolution (I saw the apparent fibre density
(AFD) paper and this is something I'd be interested in
trying in the long run), I wondered whether the largest FOD
lobe might be sensitive to pathology. Here's my analysis:<br>
<br>
Having run CSD (Lmax = 6, the b-value is only 1000, 42
directions) on my data, I used find_SH_peaks, dir2amp and
FSL's fslroi to extract the FOD of the largest amplitude (I
called this max_amp) and thus was able to generate voxelwise
max_amp maps per subject, in native space. <br>
<br>
I then normalised the max_amp maps (by dividing by 3.5, to
give a scalar in the 0-1 range), having assessed the maximum
amplitude in any voxel across all subjects (which was
3.51ish). The idea being that the the scalar now represents
a score based on the amplitude of the FOD lobe relative to
the highest possible amplitude in vivo tissue; thus 0 =
absence of FOD and 1 = maximum possible FOD amplitude.<br>
<br>
These normalised max_amp maps were then warped into group
space using the warps calculated from generating a groupwise
template from FA maps. I then created a mask using a
threshold of FA>0.2 (to limit analysis to white matter)
and finally ran some stats (using FSL randomise as per
TBSS). In a group of 10 controls and 15 patients there were
a number of significant clusters of increased max_amp in
controls, roughly corresponding to the corticospinal tract
and corona radiata. There were no increases in patients. <br>
<br>
The rationale behind this comes from the idea that the
max_amp metric represents the amount of diffusion in the
principle direction, but uncontaminated by crossing fibre
effects (so perhaps a 'better' version of axial diffusivity,
I suppose). I'm not assuming this idea is original by any
means, perhaps just the implementation of it in mrtrix.<br>
<br>
Apologies for the long post, but I'd be really appreciative
of some expert opinions. Any ideas for theoretical /
practical improvements, or reasons why this might not be
representing what I think it is would be most helpful.<br>
<br>
Many thanks,<br>
James<span><font color="#888888"><br>
<br>
<div>-- <br>
<b>James Cole PhD | Research Associate | Huntington's
Disease Research Group | UCL Institute of Neurology</b></div>
</font></span></div>
<br>
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</div>
<br>
<br clear="all">
<div><br>
</div>
-- <br>
<div dir="ltr">
<div><b><font color="#ff6600">David Raffelt (PhD)</font></b></div>
<div><font color="#ff6600">Post Doctoral Fellow</font></div>
<div><br>
</div>
<div>The Florey Institute of Neuroscience and Mental Health</div>
<div>Melbourne Brain Centre - Austin Campus</div>
<div>245 Burgundy Street</div>
<div>Heidelberg Vic 3084
<div>Ph: <a value="+61390357024">+61
3 9035 7024</a></div>
</div>
<div><a value="+61390357024">www.florey.edu.au</a></div>
</div>
</div>
</blockquote>
<br>
</div></div></div>
</blockquote></div><br><br clear="all"><div><br></div>-- <br><div dir="ltr"><div><b><font color="#ff6600">David Raffelt (PhD)</font></b></div><div><font color="#ff6600">Post Doctoral Fellow</font></div><div><br></div><div>
The Florey Institute of Neuroscience and Mental Health</div><div>Melbourne Brain Centre - Austin Campus</div><div>245 Burgundy Street</div><div>Heidelberg Vic 3084<div>Ph: <a value="+61390357024">+61 3 9035 7024</a></div>
</div><div><a value="+61390357024">www.florey.edu.au</a></div></div>
</div></div>