[Freesurfer] Resample data for longitudinal comparisons

[James Gullickson]

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All,

I would like to compare cortical thickness between two timepoints.

Timepoint 1 has 1mm^3 data.
Timepoint 2 has 0.8mm^3 data.

I realize that there are limitations with this change in resolution between
timepoints and that no analysis is going to be perfect, but I am trying to
come up with the best possible solution.

Would it work to resample both timepoints' raw data into 0.9mm^3 (
mri_convert --conform_size 0.9 invol outvol ), and then run the 0.9mm data
through Freesurfer 6.0 submilimeter recon? If so, are there any special
considerations I need to take into account after the initial recon, or
would longitudinal processing after this point be per normal?

Thanks,

James
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Re: [Freesurfer] Cortical Thickness at Individual Vertices (fwd)

[James Gullickson]

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Thanks for the input, all. Much appreciated.

On Thu, Jul 12, 2018 at 11:01 AM, Bruce Fischl 
wrote:

>
> from Tom
>
> -- Forwarded message --
> Date: Thu, 12 Jul 2018 16:51:31 +0100
> From: Thomas Nichols 
> To: Bruce Fischl 
> Cc: freesurfer@nmr.mgh.harvard.edu
> Subject: Re: [Freesurfer] Cortical Thickness at Individual Vertices
>
>
> External Email - Use Caution
>
> Hi Bruce!
> James: I don't have any particular deep thoughts except, all things equal,
> if you have a tenable
> continuous summary of the mTBI deficits it will probably be more
> sensitivity than a discrete
> count-based summary of the deficits.
>
> Bruce's idea of comparing distributions is sound but probably will only
> work well for mTBI effect
> that are diffuse.  For localised effects (that are not spatially
> consistent), finding some summary
> measure of the deficits are probably the best way forward.
>
> -Tom
>
>
> On Thu, Jul 12, 2018 at 4:31 PM Bruce Fischl 
> wrote:
>   Hi James
>
>   you could use techniques that compare the whole distribution of
>   thicknesses across subject populations. You could do a t-test or
> something
>   non-parametric like a Kolmogorov-Smirnov or use permutation testing.
> I'll
>   cc Tom Nichols so he can chime in with something more sophisticated
> or
>   specific.
>
>   cheers
>   Bruce
>
>
>
>   On Wed, 11 Jul 2018, James Gullickson
>   wrote:
>
>   >
>   > External Email - Use Caution
>   >
>   > All,
>   > I am comparing cortical thickness between subjects with and
> without mild traumatic
>   brain injury
>   > (mTBI). So far the contrasts in QDEC have not been significant
> after correcting for
>   multiple
>   > comparisons. I am not necessarily surprised at this due to the
> heterogeneous nature of
>   mTBI in our
>   > sample, i.e. we do not expect any two subjects to have damage in
> the same area. I am
>   interested in
>   > ways to compare cortical thickness that are not dependent on a
> single ROI having an
>   effect across
>   > subjects. One way I have tried is calculating z-scores for the
> values in the
>   aparc.stats file, and
>   > using the number of abnormally low ROIs as a dependant variable to
> compare between
>   groups.
>   >
>   > Is there a way to look at thickness differences at an even more
> general level? E.g. by
>   comparing the
>   > number of vertices with abnormally low thickness? If so how would
> one go about that
>   with Freesurfer
>   > data?
>   >
>   > This paper takes a similar approach with DTI. I'd like to do
> something analogous to
>   their "number of
>   > voxels with low FA" analysis.
>   > https://www.sciencedirect.com/science/article/pii/S105381191
> 1012146
>   >
>   > Thanks,
>   >
>   > James
>   >
>   >
>
>
>   The information in this e-mail is intended only for the person to
> whom it is
>   addressed. If you believe this e-mail was sent to you in error and
> the e-mail
>   contains patient information, please contact the Partners Compliance
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>
>
>
> --
> __
> Thomas Nichols, PhD
> Professor of Neuroimaging Statistics
> Nuffield Department of Population Health | University of Oxford
> Big Data Institute | Li Ka Shing Centre for Health Information and
> Discovery
> Old Road Campus | Headington | Oxford | OX3 7LF | United Kingdom
> T: +44 1865 743590 | E: thomas.nich...@bdi.ox.ac.uk
> W: http://nisox.org | http://www.bdi.ox.ac.uk
>
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[Freesurfer] Cortical Thickness at Individual Vertices

[James Gullickson]

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All,

I am comparing cortical thickness between subjects with and without mild
traumatic brain injury (mTBI). So far the contrasts in QDEC have not been
significant after correcting for multiple comparisons. I am not necessarily
surprised at this due to the heterogeneous nature of mTBI in our sample,
i.e. we do not expect any two subjects to have damage in the same area. I
am interested in ways to compare cortical thickness that are not dependent
on a single ROI having an effect across subjects. One way I have tried is
calculating z-scores for the values in the aparc.stats file, and using the
number of abnormally low ROIs as a dependant variable to compare between
groups.

Is there a way to look at thickness differences at an even more general
level? E.g. by comparing the number of vertices with abnormally low
thickness? If so how would one go about that with Freesurfer data?

This paper takes a similar approach with DTI. I'd like to do something
analogous to their "number of voxels with low FA" analysis.
https://www.sciencedirect.com/science/article/pii/S1053811911012146

Thanks,

James
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[Freesurfer] Using grad_unwarp

[James Gullickson]

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All,

I am interested in using grad_unwarp. I have been reading up on the
documentation a bit and from what I understand not all the necessary files
are distributed with the public version of Freesurfer, due to proprietary
information in the tables. However, there is a script called grad_unwarp in
 my $FREESURFER_HOME/bin directory. I have access to the .grad files
necessary for my project, so I was planning to just piece together the
environment manually. Based on:  https://surfer.nmr.mgh.
harvard.edu/fswiki/GradUnwarp there are a few pieces I'm missing:

   - create_displacement_tables script
   - grad_unwarp_converter.pl script
   -  matlab files

Is there somewhere I can locate these missing scripts/files?

Thanks,

James
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Re: [Freesurfer] Visualizing Supratentorial volume

[James Gullickson]

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OK. Two follow-up questions.

1. What structures should I add up to get supratentorial? Are they just in
aseg or do I also need aparc?
2. Isn't supratentorial volume calculated differently than simply adding up
individual aseg lables? The reason I ask is that on this page
https://surfer.nmr.mgh.harvard.edu/fswiki/MorphometryStats it notes that a
specific method is used for supratentorial volume calculation.

Thanks


On Thu, May 31, 2018 at 3:35 PM, Douglas Greve 
wrote:

> No, you'll have to pick out each of the segmentation numbers and use
> mri_binarize with the --match option
>
> On 5/31/18 3:27 PM, James Gullickson wrote:
>
> External Email - Use Caution
> Sorry I wasn't clear. Yes, I would like to see it with all the non-STV
> removed, in order to see exactly what the supratentorial segmentation is
> including. So creating a mask would be ideal. Is there a match # for
> supratentorialvol and supratentorialvolnotvent to use in mri_binarize? I
> don't see one on this table https://surfer.nmr.mgh.
> harvard.edu/fswiki/FsTutorial/AnatomicalROI/FreeSurferColorLUT
>
>
>
> On Wed, May 30, 2018 at 12:59 PM, Douglas N. Greve  > wrote:
>
>> I'm not sure what you mean. You can just use freeview to load the aseg.
>> Or do you want all the non-STV removed? If that, you can create a mask
>> of STV using mri_binarize and --match run on the aseg.mgz, then use
>> mri_mask aseg.mgz stvmask.mgz aseg-stvonly.mgz
>>
>>
>>
>> On 05/30/2018 11:56 AM, James Gullickson wrote:
>> >
>> >
>> > All,
>> >
>> > Is there any way to graphically visualize the segmentations for
>> > SupraTentorialVol and SupraTentorialVolNotVent, for example in
>> > freeview or tkmedit? Based on the descriptions I have read for the
>> > supratentorial values, I want to use them for an analysis. However, I
>> > would like to visualize them in my data set for QA purposes to ensure
>> > they are capturing what I think they are capturing.
>> >
>> > Thanks,
>> >
>> > James
>> >
>> >
>> > ___
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>> is
>> addressed. If you believe this e-mail was sent to you in error and the
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>> properly
>> dispose of the e-mail.
>>
>>
>
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Re: [Freesurfer] Visualizing Supratentorial volume

[James Gullickson]

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Sorry I wasn't clear. Yes, I would like to see it with all the non-STV
removed, in order to see exactly what the supratentorial segmentation is
including. So creating a mask would be ideal. Is there a match # for
supratentorialvol and supratentorialvolnotvent to use in mri_binarize? I
don't see one on this table
https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/AnatomicalROI/FreeSurferColorLUT



On Wed, May 30, 2018 at 12:59 PM, Douglas N. Greve 
wrote:

> I'm not sure what you mean. You can just use freeview to load the aseg.
> Or do you want all the non-STV removed? If that, you can create a mask
> of STV using mri_binarize and --match run on the aseg.mgz, then use
> mri_mask aseg.mgz stvmask.mgz aseg-stvonly.mgz
>
>
>
> On 05/30/2018 11:56 AM, James Gullickson wrote:
> >
> >
> > All,
> >
> > Is there any way to graphically visualize the segmentations for
> > SupraTentorialVol and SupraTentorialVolNotVent, for example in
> > freeview or tkmedit? Based on the descriptions I have read for the
> > supratentorial values, I want to use them for an analysis. However, I
> > would like to visualize them in my data set for QA purposes to ensure
> > they are capturing what I think they are capturing.
> >
> > Thanks,
> >
> > James
> >
> >
> > ___
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[Freesurfer] Visualizing Supratentorial volume

[James Gullickson]

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All,

Is there any way to graphically visualize the segmentations for
SupraTentorialVol and SupraTentorialVolNotVent, for example in freeview or
tkmedit? Based on the descriptions I have read for the supratentorial
values, I want to use them for an analysis. However, I would like to
visualize them in my data set for QA purposes to ensure they are capturing
what I think they are capturing.

Thanks,

James
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Re: [Freesurfer] Longitudinal data from different sequences and scanners

[James Gullickson]

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Martin,

Thanks for the feedback. Given our data set (1mm^3 timepoint1 and 0.8mm^3
timepoint 2), what would be the best way to salvage this data and look for
longitudinal changes? Would it be possible to upsample/downsample the
images so that they are the same resolution (i.e. using mriconvert)? Also,
would a normalized measure like ventricle-brain-ratio theoretically be
resistant to these scanner/acquisition induced biases?

Thanks,

James

On Mon, Apr 16, 2018 at 5:15 AM, Martin Reuter 
wrote:

> Hi,
>
> never switch hardware or protocols in a longitudinal study. This is a good
> example for what happens: you will find effects that are caused by
> different acquisition rather than anatomical differences. It will be
> impossible to disentangle true change from scanner/acquisition induced
> changes, especially with only a few time points and no or little subjects
> scanned on both scanners or with both protocols.
>
> Also 5mm resolution is too low for freesurfer and anything can come out of
> that. It won’t be reliable.
>
> Best, Martin
>
>
> On 6. Apr 2018, at 21:21, Matthew Grecsek  wrote:
>
> Here are some statistics I generated for one of my subjects processed
> through the longitudinal stream with T0 as the initial scan and T1 18 mos
> later. (not sure if the inline tables will be properly formatted so I
> attached text files of them).
>
> Unfortunately the only initial scan I had was a 5mm resolution for T0
> versus 1mm for T1. I understand FS recommends a resolution not exceeding
> 1.5mm, but we gave it a try anyway to see if there was anything useful. My
> expectation was that the stats would be off by a consistent ratio due to
> the different resolutions, however I was surprised by the variability.
>
> In particular, as James found, for some ROIs there are net increases in
> cortical thickness and brain volume over time.
>
> Is this simply a factor that the algorithms are confused by the different
> image resolutions and therefore no possible longitudinal study can reliably
> be presumed in this circumstance?
>
> Should we expect similar anomalies in cross-sectional studies, such as if
> my subjects have 1mm resolutions and a collaborating institution has 0.8mm
> subjects?
>
> Cheers,
>
> -Matt
>
> Aseg Stats
> Measure:volume T0 T1 Base T0.long.base T1.long.base
> Left-Lateral-Ventricle 9,455.2   13,085.8
> 12,344.2   10,928.1   13,268.0
> Left-Inf-Lat-Vent   61.8 174.1
> 171.7   90.3 303.3
> Left-Cerebellum-White-Matter   26,892.5   18,563.1
>   17,066.4   23,905.6   17,597.8
> Left-Cerebellum-Cortex   57,390.8   66,458.7
> 64,631.3   60,374.1   65,898.6
> Left-Thalamus-Proper   10,757.3 9,094.2
> 9,918.5   10,350.8 9,588.3
> Left-Caudate 3,619.4 3,673.8
> 3,608.8 3,588.7 3,872.7
> Left-Putamen 5,463.7 5,439.2
> 5,171.0 5,602.0 5,721.7
> Left-Pallidum 2,380.9 2,208.6
> 1,897.1 2,270.8 2,147.9
> 3rd-Ventricle 1,181.8 1,174.6
> 1,328.7 1,432.2 1,416.9
> 4th-Ventricle 1,209.1 1,602.0
> 1,547.7 1,322.4 1,981.1
> Brain-Stem   25,154.2   26,141.3   
> 25,890.7
>   25,988.7   25,954.6
> Left-Hippocampus 4,205.3 4,335.6
> 4,378.0 4,478.2 4,407.2
> Left-Amygdala 1,488.1 1,725.9
> 1,588.6 1,531.3 1,638.0
> CSF 1,440.9 1,321.0 1,551.0
> 1,910.1 1,394.7
> Left-Accumbens-area 295.0 273.2
> 327.7 274.9 380.0
> Left-VentralDC 5,086.1 4,935.5
> 5,429.6 5,106.9 5,041.7
> Left-vessel- 16.6
> 7.7- 48.5
> Left-choroid-plexus 221.6 419.4
> 271.7 429.5 734.2
> Right-Lateral-Ventricle 6,465.1 9,581.5
> 8,695.5 6,893.5 9,713.9
> Right-Inf-Lat-Vent 360.0 333.7
> 408.5 400.5 518.0
> 

Re: [Freesurfer] Longitudinal data from different sequences and scanners

[James Gullickson]

All,

Just wanted to follow up on this message. Does anyone have recommendations
for the best way to compare this data longitudinally?

Thanks very much,

James

On Thu, Mar 29, 2018 at 1:19 PM, James Gullickson <jgull...@umn.edu> wrote:

> All,
>
> Our lab is working with longitudinal (two time points) data from a cohort
> of around 130 individuals. We are attempting to make comparisons between
> time points 1 and 2 on measures like total brain volume, ventricular
> volume, ventricle-brain-ratio, and cortical thickness. However, we have run
> into an issue--we are seeing net increases in cortical thickness and total
> brain volume over time (which seems biologically implausible, given our age
> range of ~30-60 yrs old). We think it may have something to do with the
> fact that the T1w data from each time point was acquired with slightly
> different parameters and on different scanners, possibly leading to a
> rounding error in quantification of volumes/thickness. Timepoint 1 data are
> 1.0mm isotropic and were acquired on a 3T Siemens Tim Trio with 12ch
> headcoil. Timepoint 2 data are 0.8mm isotropic and were acquired on the
> same scanner, but which went through an upgrade to a Prisma Fit between
> timepoints, using a 32ch coil.
>
> Currently we have been comparing data from the cross sectional stream. We
> would like to use the longitudinal stream if that would improve results,
> but we saw this post that cautioned against it: https://www.mail-archive.c
> om/freesurfer@nmr.mgh.harvard.edu/msg52992.html
>
> What would be your recommendation for comparing this data longitudinally?
> Any thoughts on why we are seeing net increases in volume/thickness, and
> how to avoid that? One idea we had was perhaps degrading each image by
> rigid co-registration and then bringing each image into the halfway space
> between the two (as FSL's SIENA does).
>
> Thanks,
>
> James
>
>
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[Freesurfer] Longitudinal data from different sequences and scanners

[James Gullickson]

 All,

Our lab is working with longitudinal (two time points) data from a cohort
of around 130 individuals. We are attempting to make comparisons between
time points 1 and 2 on measures like total brain volume, ventricular
volume, ventricle-brain-ratio, and cortical thickness. However, we have run
into an issue--we are seeing net increases in cortical thickness and total
brain volume over time (which seems biologically implausible, given our age
range of ~30-60 yrs old). We think it may have something to do with the
fact that the T1w data from each time point was acquired with slightly
different parameters and on different scanners, possibly leading to a
rounding error in quantification of volumes/thickness. Timepoint 1 data are
1.0mm isotropic and were acquired on a 3T Siemens Tim Trio with 12ch
headcoil. Timepoint 2 data are 0.8mm isotropic and were acquired on the
same scanner, but which went through an upgrade to a Prisma Fit between
timepoints, using a 32ch coil.

Currently we have been comparing data from the cross sectional stream. We
would like to use the longitudinal stream if that would improve results,
but we saw this post that cautioned against it: https://www.mail-archive.c
om/freesurfer@nmr.mgh.harvard.edu/msg52992.html

What would be your recommendation for comparing this data longitudinally?
Any thoughts on why we are seeing net increases in volume/thickness, and
how to avoid that? One idea we had was perhaps degrading each image by
rigid co-registration and then bringing each image into the halfway space
between the two (as FSL's SIENA does).

Thanks,

James
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