Re: [Freesurfer] Cortical thickness values extraction issue

[Giuliana Klencklen]

External Email - Use Caution

THank you so much for your help!

Le lun. 18 mars 2019 à 16:32, Greve, Douglas N.,Ph.D. <
dgr...@mgh.harvard.edu> a écrit :

> The vertices chosen for the cluster depend only on the cluster forming
> threshold (CFT). Once that is chosen, the vertices in the cluster are
> fixed. The permutation judges how likely the cluster as a whole would be
> seen by chance. There are other ways to do permutation, but this is the way
> we do it.
>
>
> On 3/18/19 11:25 AM, Giuliana Klencklen wrote:
>
> External Email - Use Caution
> Ok, well then I do not understand the purpose of the permutation testing
> if the number of permutation would not change the size of the clusters.
> What is it doing if not refining what vertices are significant? Perhaps I
> am just missing a key idea.
>
> Best,
>
>
> On Thu, Mar 14, 2019 at 5:11 PM Greve, Douglas N.,Ph.D. <
> dgr...@mgh.harvard.edu> wrote:
>
>> The size of the cluster is not going to be affected by the number of
>> iterations (only by the threshold). Why would you think that the cluster
>> size is affected by the number of iterations?
>>
>> On 3/14/19 5:45 AM, Giuliana Klencklen wrote:
>> >
>> > External Email - Use Caution
>> >
>> > Hi Douglas,
>> >
>> > According to your suggestion, I used the permutation simulation
>> > approach. I chose a cluster forming threshold set at 0.05 and explored
>> > how the number of iterations effects the data. For example, I used
>> > this command for 1,000 iterations:
>> > mri_glmfit-sim \
>> >  --glmdir lh.longMRI.glmdir \
>> >  --sim perm 1000 1.3 perm.abs.13 \
>> >  --sim-sign abs\
>> >  --cwpvalthresh 0.05
>> >
>> > I did not observe any difference on the size of the cluster between
>> > 1,000 vs 5,000 vs 10,000 iterations. As I thought the results were
>> > pretty odd, I tried running the command with 5 permutations just to
>> > make sure it's not also the same and I did not find any significant
>> > clusters.
>> >
>> > Is the absence of cluster size difference between different number of
>> > iterations expected? Just wanted to make sure with you that this
>> > approach is working as it should.
>> >
>> > Thanks!
>> > Regards,
>> >
>> > On Fri, Feb 22, 2019 at 6:50 PM Greve, Douglas N.,Ph.D.
>> > mailto:dgr...@mgh.harvard.edu>> wrote:
>> >
>> > You can make the bonferroni correction from of mri_glmfit-sim the
>> > same as qdec by not including --2spaces (the bonferroni correction
>> > in qdec is actually 1, not 0). Also, if you want to use such a low
>> > cluster forming threshold (1.3=p<.05), then you should use
>> > permutation and not MC (MC is not valid at such low thresholds).
>> >
>> > On 2/22/19 7:49 AM, Giuliana Klencklen wrote:
>> >>
>> >> External Email - Use Caution
>> >>
>> >> Thanks Douglas, that makes sense. I have run mri_glmfit-sim and
>> >> have that table file.
>> >> However, the cortical thickness values for each cluster displayed
>> >> in that table can’t be used because the clusters
>> >> (something.sig.cluster.mgh opened with tksurfer) do not match
>> >> exactly (but are very similar to) those previously generated with
>> >> QDEC. I do not understand the cause of this issue because all the
>> >> stats I used, i.e., threshold, statistical correction, level of
>> >> smooting, seem to be the same between both qdec and mri_glmfit-sim.
>> >>
>> >> I send you here a typical example of the problematic clusters, as
>> >> well as the summary for both qdec and mri_glmfit-sim. They appear
>> >> similar except for the Bonferroni correction that is set at 2 for
>> >> the mri_glmfit-sim while it is set at 0 for the qdec version? If
>> >> it is the source of the current issue, how can I configure the
>> >> Bonferroni correction? If not, do you have any idea how I can
>> >> resolve the problem?
>> >>
>> >> Many thanks in advance.
>> >>
>> >> Regards,
>> >> Giuliana Klencklen
>> >> QdecGroupComparison.jpg
>> >>
>> >> On Fri, Feb 15, 2019 at 5:24 PM Greve, Douglas N.,Ph.D.
>> >> mailto:dgr...@mgh.harvard.edu>> wrote:
>> >>
>> >> This can happen if the label is small and/or you've used a
>> >> lot of smoothing. It is better to do this kind of thing in
>> >> fsaverage space rather than moving the labels back to the
>> >> individual space. If you've run mri_glmfit-sim, then it
>> >> should have created a table file  (something.y.ocn.dat). This
>> >> file will have a row for each subject and a column for each
>> >> cluster. The value will be the mean for that subject in that
>> >> cluster.
>> >>
>> >> On 2/15/19 6:23 AM, Giuliana Klencklen wrote:
>> >>>
>> >>> External Email - Use Caution
>> >>>
>> >>> Hi FS experts,
>> >>>
>> >>> I did group-level, surface-based, vertex-wise analysis for
>> >>> baseline and longitudinal data. I used Qdec and d

Re: [Freesurfer] Cortical thickness values extraction issue

[Greve, Douglas N.,Ph.D.]

The vertices chosen for the cluster depend only on the cluster forming 
threshold (CFT). Once that is chosen, the vertices in the cluster are fixed. 
The permutation judges how likely the cluster as a whole would be seen by 
chance. There are other ways to do permutation, but this is the way we do it.

On 3/18/19 11:25 AM, Giuliana Klencklen wrote:

External Email - Use Caution

Ok, well then I do not understand the purpose of the permutation testing if the 
number of permutation would not change the size of the clusters. What is it 
doing if not refining what vertices are significant? Perhaps I am just missing 
a key idea.

Best,


On Thu, Mar 14, 2019 at 5:11 PM Greve, Douglas N.,Ph.D. 
mailto:dgr...@mgh.harvard.edu>> wrote:
The size of the cluster is not going to be affected by the number of
iterations (only by the threshold). Why would you think that the cluster
size is affected by the number of iterations?

On 3/14/19 5:45 AM, Giuliana Klencklen wrote:
>
> External Email - Use Caution
>
> Hi Douglas,
>
> According to your suggestion, I used the permutation simulation
> approach. I chose a cluster forming threshold set at 0.05 and explored
> how the number of iterations effects the data. For example, I used
> this command for 1,000 iterations:
> mri_glmfit-sim \
>  --glmdir lh.longMRI.glmdir \
>  --sim perm 1000 1.3 perm.abs.13 \
>  --sim-sign abs\
>  --cwpvalthresh 0.05
>
> I did not observe any difference on the size of the cluster between
> 1,000 vs 5,000 vs 10,000 iterations. As I thought the results were
> pretty odd, I tried running the command with 5 permutations just to
> make sure it's not also the same and I did not find any significant
> clusters.
>
> Is the absence of cluster size difference between different number of
> iterations expected? Just wanted to make sure with you that this
> approach is working as it should.
>
> Thanks!
> Regards,
>
> On Fri, Feb 22, 2019 at 6:50 PM Greve, Douglas N.,Ph.D.
> mailto:dgr...@mgh.harvard.edu> 
> >> wrote:
>
> You can make the bonferroni correction from of mri_glmfit-sim the
> same as qdec by not including --2spaces (the bonferroni correction
> in qdec is actually 1, not 0). Also, if you want to use such a low
> cluster forming threshold (1.3=p<.05), then you should use
> permutation and not MC (MC is not valid at such low thresholds).
>
> On 2/22/19 7:49 AM, Giuliana Klencklen wrote:
>>
>> External Email - Use Caution
>>
>> Thanks Douglas, that makes sense. I have run mri_glmfit-sim and
>> have that table file.
>> However, the cortical thickness values for each cluster displayed
>> in that table can’t be used because the clusters
>> (something.sig.cluster.mgh opened with tksurfer) do not match
>> exactly (but are very similar to) those previously generated with
>> QDEC. I do not understand the cause of this issue because all the
>> stats I used, i.e., threshold, statistical correction, level of
>> smooting, seem to be the same between both qdec and mri_glmfit-sim.
>>
>> I send you here a typical example of the problematic clusters, as
>> well as the summary for both qdec and mri_glmfit-sim. They appear
>> similar except for the Bonferroni correction that is set at 2 for
>> the mri_glmfit-sim while it is set at 0 for the qdec version? If
>> it is the source of the current issue, how can I configure the
>> Bonferroni correction? If not, do you have any idea how I can
>> resolve the problem?
>>
>> Many thanks in advance.
>>
>> Regards,
>> Giuliana Klencklen
>> QdecGroupComparison.jpg
>>
>> On Fri, Feb 15, 2019 at 5:24 PM Greve, Douglas N.,Ph.D.
>> mailto:dgr...@mgh.harvard.edu> 
>> >> wrote:
>>
>> This can happen if the label is small and/or you've used a
>> lot of smoothing. It is better to do this kind of thing in
>> fsaverage space rather than moving the labels back to the
>> individual space. If you've run mri_glmfit-sim, then it
>> should have created a table file  (something.y.ocn.dat). This
>> file will have a row for each subject and a column for each
>> cluster. The value will be the mean for that subject in that
>> cluster.
>>
>> On 2/15/19 6:23 AM, Giuliana Klencklen wrote:
>>>
>>> External Email - Use Caution
>>>
>>> Hi FS experts,
>>>
>>> I did group-level, surface-based, vertex-wise analysis for
>>> baseline and longitudinal data. I used Qdec and do the same
>>> work with the fsgd version (mri_glmfit-sim command) to
>>> double-check the data.
>>>
>>> I created label files with tksurfer for each of the clusters
>>> showing a significant between-group difference. Then, I used
>>> the following command stream to extract the cortical
>>

Re: [Freesurfer] Cortical thickness values extraction issue

[Giuliana Klencklen]

External Email - Use Caution

Ok, well then I do not understand the purpose of the permutation testing if
the number of permutation would not change the size of the clusters. What
is it doing if not refining what vertices are significant? Perhaps I am
just missing a key idea.

Best,


On Thu, Mar 14, 2019 at 5:11 PM Greve, Douglas N.,Ph.D. <
dgr...@mgh.harvard.edu> wrote:

> The size of the cluster is not going to be affected by the number of
> iterations (only by the threshold). Why would you think that the cluster
> size is affected by the number of iterations?
>
> On 3/14/19 5:45 AM, Giuliana Klencklen wrote:
> >
> > External Email - Use Caution
> >
> > Hi Douglas,
> >
> > According to your suggestion, I used the permutation simulation
> > approach. I chose a cluster forming threshold set at 0.05 and explored
> > how the number of iterations effects the data. For example, I used
> > this command for 1,000 iterations:
> > mri_glmfit-sim \
> >  --glmdir lh.longMRI.glmdir \
> >  --sim perm 1000 1.3 perm.abs.13 \
> >  --sim-sign abs\
> >  --cwpvalthresh 0.05
> >
> > I did not observe any difference on the size of the cluster between
> > 1,000 vs 5,000 vs 10,000 iterations. As I thought the results were
> > pretty odd, I tried running the command with 5 permutations just to
> > make sure it's not also the same and I did not find any significant
> > clusters.
> >
> > Is the absence of cluster size difference between different number of
> > iterations expected? Just wanted to make sure with you that this
> > approach is working as it should.
> >
> > Thanks!
> > Regards,
> >
> > On Fri, Feb 22, 2019 at 6:50 PM Greve, Douglas N.,Ph.D.
> > mailto:dgr...@mgh.harvard.edu>> wrote:
> >
> > You can make the bonferroni correction from of mri_glmfit-sim the
> > same as qdec by not including --2spaces (the bonferroni correction
> > in qdec is actually 1, not 0). Also, if you want to use such a low
> > cluster forming threshold (1.3=p<.05), then you should use
> > permutation and not MC (MC is not valid at such low thresholds).
> >
> > On 2/22/19 7:49 AM, Giuliana Klencklen wrote:
> >>
> >> External Email - Use Caution
> >>
> >> Thanks Douglas, that makes sense. I have run mri_glmfit-sim and
> >> have that table file.
> >> However, the cortical thickness values for each cluster displayed
> >> in that table can’t be used because the clusters
> >> (something.sig.cluster.mgh opened with tksurfer) do not match
> >> exactly (but are very similar to) those previously generated with
> >> QDEC. I do not understand the cause of this issue because all the
> >> stats I used, i.e., threshold, statistical correction, level of
> >> smooting, seem to be the same between both qdec and mri_glmfit-sim.
> >>
> >> I send you here a typical example of the problematic clusters, as
> >> well as the summary for both qdec and mri_glmfit-sim. They appear
> >> similar except for the Bonferroni correction that is set at 2 for
> >> the mri_glmfit-sim while it is set at 0 for the qdec version? If
> >> it is the source of the current issue, how can I configure the
> >> Bonferroni correction? If not, do you have any idea how I can
> >> resolve the problem?
> >>
> >> Many thanks in advance.
> >>
> >> Regards,
> >> Giuliana Klencklen
> >> QdecGroupComparison.jpg
> >>
> >> On Fri, Feb 15, 2019 at 5:24 PM Greve, Douglas N.,Ph.D.
> >> mailto:dgr...@mgh.harvard.edu>> wrote:
> >>
> >> This can happen if the label is small and/or you've used a
> >> lot of smoothing. It is better to do this kind of thing in
> >> fsaverage space rather than moving the labels back to the
> >> individual space. If you've run mri_glmfit-sim, then it
> >> should have created a table file  (something.y.ocn.dat). This
> >> file will have a row for each subject and a column for each
> >> cluster. The value will be the mean for that subject in that
> >> cluster.
> >>
> >> On 2/15/19 6:23 AM, Giuliana Klencklen wrote:
> >>>
> >>> External Email - Use Caution
> >>>
> >>> Hi FS experts,
> >>>
> >>> I did group-level, surface-based, vertex-wise analysis for
> >>> baseline and longitudinal data. I used Qdec and do the same
> >>> work with the fsgd version (mri_glmfit-sim command) to
> >>> double-check the data.
> >>>
> >>> I created label files with tksurfer for each of the clusters
> >>> showing a significant between-group difference. Then, I used
> >>> the following command stream to extract the cortical
> >>> thickness values for each subject and cluster:
> >>> mri_label2label, mris_anatomical_stats, and aparcstats2table.
> >>> E.g.,
> >>> mri_label2label --srcsubject fsaverage --srclabel
> >>>
>  /home/jagust/gklenck/Long_MRI/lh.ac-baseline-rostralmiddlefrontal.l

Re: [Freesurfer] Cortical thickness values extraction issue

[Greve, Douglas N.,Ph.D.]

The size of the cluster is not going to be affected by the number of 
iterations (only by the threshold). Why would you think that the cluster 
size is affected by the number of iterations?

On 3/14/19 5:45 AM, Giuliana Klencklen wrote:
>
> External Email - Use Caution
>
> Hi Douglas,
>
> According to your suggestion, I used the permutation simulation 
> approach. I chose a cluster forming threshold set at 0.05 and explored 
> how the number of iterations effects the data. For example, I used 
> this command for 1,000 iterations:
> mri_glmfit-sim \
>  --glmdir lh.longMRI.glmdir \
>  --sim perm 1000 1.3 perm.abs.13 \
>  --sim-sign abs\
>  --cwpvalthresh 0.05
>
> I did not observe any difference on the size of the cluster between 
> 1,000 vs 5,000 vs 10,000 iterations. As I thought the results were 
> pretty odd, I tried running the command with 5 permutations just to 
> make sure it's not also the same and I did not find any significant 
> clusters.
>
> Is the absence of cluster size difference between different number of 
> iterations expected? Just wanted to make sure with you that this 
> approach is working as it should.
>
> Thanks!
> Regards,
>
> On Fri, Feb 22, 2019 at 6:50 PM Greve, Douglas N.,Ph.D. 
> mailto:dgr...@mgh.harvard.edu>> wrote:
>
> You can make the bonferroni correction from of mri_glmfit-sim the
> same as qdec by not including --2spaces (the bonferroni correction
> in qdec is actually 1, not 0). Also, if you want to use such a low
> cluster forming threshold (1.3=p<.05), then you should use
> permutation and not MC (MC is not valid at such low thresholds).
>
> On 2/22/19 7:49 AM, Giuliana Klencklen wrote:
>>
>> External Email - Use Caution
>>
>> Thanks Douglas, that makes sense. I have run mri_glmfit-sim and
>> have that table file.
>> However, the cortical thickness values for each cluster displayed
>> in that table can’t be used because the clusters
>> (something.sig.cluster.mgh opened with tksurfer) do not match
>> exactly (but are very similar to) those previously generated with
>> QDEC. I do not understand the cause of this issue because all the
>> stats I used, i.e., threshold, statistical correction, level of
>> smooting, seem to be the same between both qdec and mri_glmfit-sim.
>>
>> I send you here a typical example of the problematic clusters, as
>> well as the summary for both qdec and mri_glmfit-sim. They appear
>> similar except for the Bonferroni correction that is set at 2 for
>> the mri_glmfit-sim while it is set at 0 for the qdec version? If
>> it is the source of the current issue, how can I configure the
>> Bonferroni correction? If not, do you have any idea how I can
>> resolve the problem?
>>
>> Many thanks in advance.
>>
>> Regards,
>> Giuliana Klencklen
>> QdecGroupComparison.jpg
>>
>> On Fri, Feb 15, 2019 at 5:24 PM Greve, Douglas N.,Ph.D.
>> mailto:dgr...@mgh.harvard.edu>> wrote:
>>
>> This can happen if the label is small and/or you've used a
>> lot of smoothing. It is better to do this kind of thing in
>> fsaverage space rather than moving the labels back to the
>> individual space. If you've run mri_glmfit-sim, then it
>> should have created a table file  (something.y.ocn.dat). This
>> file will have a row for each subject and a column for each
>> cluster. The value will be the mean for that subject in that
>> cluster.
>>
>> On 2/15/19 6:23 AM, Giuliana Klencklen wrote:
>>>
>>> External Email - Use Caution
>>>
>>> Hi FS experts,
>>>
>>> I did group-level, surface-based, vertex-wise analysis for
>>> baseline and longitudinal data. I used Qdec and do the same
>>> work with the fsgd version (mri_glmfit-sim command) to
>>> double-check the data.
>>>
>>> I created label files with tksurfer for each of the clusters
>>> showing a significant between-group difference. Then, I used
>>> the following command stream to extract the cortical
>>> thickness values for each subject and cluster:
>>> mri_label2label, mris_anatomical_stats, and aparcstats2table.
>>> E.g.,
>>> mri_label2label --srcsubject fsaverage --srclabel
>>> 
>>> /home/jagust/gklenck/Long_MRI/lh.ac-baseline-rostralmiddlefrontal.label
>>> --trgsubject ${s}_tp1 --trglabel
>>> ${s}_tp1/label/lh.ac-baseline-rostralmiddlefrontal.label
>>> --hemi lh --regmethod surface
>>>
>>> mris_anatomical_stats -l
>>> lh.ac-baseline-rostralmiddlefrontal.label -t lh.thickness -b
>>> -f ${s}_tp1/stats/lh.ac-baseline-rostralmiddlefrontal.stats
>>> ${s}_tp1 lh
>>>
>>> aparcstats2table --subjects ${s}_tp1 --hemi lh --parc
>>> ac-baseline-rostralmiddlefrontal --meas thickness
>>> --tablefile
>>> ${out_dir}/lh.ac-baseline-rostra

Re: [Freesurfer] Cortical thickness values extraction issue

[Giuliana Klencklen]

External Email - Use Caution

 Hi Douglas,

According to your suggestion, I used the permutation simulation approach. I
chose a cluster forming threshold set at 0.05 and explored how the number
of iterations effects the data. For example, I used this command for 1,000
iterations:
mri_glmfit-sim \
 --glmdir lh.longMRI.glmdir \
 --sim perm 1000 1.3 perm.abs.13 \
 --sim-sign abs\
 --cwpvalthresh 0.05

I did not observe any difference on the size of the cluster between 1,000
vs 5,000 vs 10,000 iterations. As I thought the results were pretty odd, I
tried running the command with 5 permutations just to make sure it's not
also the same and I did not find any significant clusters.

Is the absence of cluster size difference between different number of
iterations expected? Just wanted to make sure with you that this approach
is working as it should.

Thanks!
Regards,

On Fri, Feb 22, 2019 at 6:50 PM Greve, Douglas N.,Ph.D. <
dgr...@mgh.harvard.edu> wrote:

> You can make the bonferroni correction from of mri_glmfit-sim the same as
> qdec by not including --2spaces (the bonferroni correction in qdec is
> actually 1, not 0). Also, if you want to use such a low cluster forming
> threshold (1.3=p<.05), then you should use permutation and not MC (MC is
> not valid at such low thresholds).
>
> On 2/22/19 7:49 AM, Giuliana Klencklen wrote:
>
> External Email - Use Caution
> Thanks Douglas, that makes sense. I have run mri_glmfit-sim and have that
> table file.
> However, the cortical thickness values for each cluster displayed in that
> table can’t be used because the clusters (something.sig.cluster.mgh opened
> with tksurfer) do not match exactly (but are very similar to) those
> previously generated with QDEC. I do not understand the cause of this issue
> because all the stats I used, i.e., threshold, statistical correction,
> level of smooting, seem to be the same between both qdec and
> mri_glmfit-sim.
>
> I send you here a typical example of the problematic clusters, as well as
> the summary for both qdec and mri_glmfit-sim. They appear similar except
> for the Bonferroni correction that is set at 2 for the mri_glmfit-sim while
> it is set at 0 for the qdec version? If it is the source of the current
> issue, how can I configure the Bonferroni correction? If not, do you have
> any idea how I can resolve the problem?
>
> Many thanks in advance.
>
> Regards,
> Giuliana Klencklen
> [image: QdecGroupComparison.jpg]
>
> On Fri, Feb 15, 2019 at 5:24 PM Greve, Douglas N.,Ph.D. <
> dgr...@mgh.harvard.edu> wrote:
>
>> This can happen if the label is small and/or you've used a lot of
>> smoothing. It is better to do this kind of thing in fsaverage space rather
>> than moving the labels back to the individual space. If you've run
>> mri_glmfit-sim, then it should have created a table file
>> (something.y.ocn.dat). This file will have a row for each subject and a
>> column for each cluster. The value will be the mean for that subject in
>> that cluster.
>>
>> On 2/15/19 6:23 AM, Giuliana Klencklen wrote:
>>
>> External Email - Use Caution
>> Hi FS experts,
>>
>> I did group-level, surface-based, vertex-wise analysis for baseline and
>> longitudinal data. I used Qdec and do the same work with the fsgd version
>> (mri_glmfit-sim command) to double-check the data.
>>
>> I created label files with tksurfer for each of the clusters showing a
>> significant between-group difference. Then, I used the following command
>> stream to extract the cortical thickness values for each subject and
>> cluster: mri_label2label, mris_anatomical_stats, and aparcstats2table.
>> E.g.,
>> mri_label2label --srcsubject fsaverage --srclabel
>> /home/jagust/gklenck/Long_MRI/lh.ac-baseline-rostralmiddlefrontal.label
>> --trgsubject ${s}_tp1 --trglabel
>> ${s}_tp1/label/lh.ac-baseline-rostralmiddlefrontal.label --hemi lh
>> --regmethod surface
>>
>> mris_anatomical_stats -l lh.ac-baseline-rostralmiddlefrontal.label -t
>> lh.thickness -b -f ${s}_tp1/stats/lh.ac-baseline-rostralmiddlefrontal.stats
>> ${s}_tp1 lh
>>
>> aparcstats2table --subjects ${s}_tp1 --hemi lh --parc
>> ac-baseline-rostralmiddlefrontal --meas thickness --tablefile
>> ${out_dir}/lh.ac-baseline-rostralmiddlefrontal.aparc_stats.txt
>>
>> Subsequently, when I conducted between-group comparisons for each
>> cluster, a couple of clusters (7 out of 16) do not show a significant
>> difference - which does not make sense. This problem seems to appear
>> randomly.
>>
>> Help to the FS archives, I tried to use mri_segstats command but was not
>> able to find a correct combination of arguments. If this is the right track
>> to solve this problem, what combination of arguments should I use? And if
>> this is not, do you have any idea how I can solve this problem?
>>
>> Many thanks in advance.
>>
>> Regards,
>> Giuliana
>>
>>
>> --
>> Giuliana Klencklen, Ph.D.
>>
>> Helen Wills Neuroscience Institute
>> University of California, Berkeley
>> 118 Barker Hall
>> Berkeley, C

Re: [Freesurfer] Cortical thickness values extraction issue

[Greve, Douglas N.,Ph.D.]

You can make the bonferroni correction from of mri_glmfit-sim the same as qdec 
by not including --2spaces (the bonferroni correction in qdec is actually 1, 
not 0). Also, if you want to use such a low cluster forming threshold 
(1.3=p<.05), then you should use permutation and not MC (MC is not valid at 
such low thresholds).

On 2/22/19 7:49 AM, Giuliana Klencklen wrote:

External Email - Use Caution

Thanks Douglas, that makes sense. I have run mri_glmfit-sim and have that table 
file.
However, the cortical thickness values for each cluster displayed in that table 
can’t be used because the clusters (something.sig.cluster.mgh opened with 
tksurfer) do not match exactly (but are very similar to) those previously 
generated with QDEC. I do not understand the cause of this issue because all 
the stats I used, i.e., threshold, statistical correction, level of smooting, 
seem to be the same between both qdec and mri_glmfit-sim.

I send you here a typical example of the problematic clusters, as well as the 
summary for both qdec and mri_glmfit-sim. They appear similar except for the 
Bonferroni correction that is set at 2 for the mri_glmfit-sim while it is set 
at 0 for the qdec version? If it is the source of the current issue, how can I 
configure the Bonferroni correction? If not, do you have any idea how I can 
resolve the problem?

Many thanks in advance.

Regards,
Giuliana Klencklen
[QdecGroupComparison.jpg]

On Fri, Feb 15, 2019 at 5:24 PM Greve, Douglas N.,Ph.D. 
mailto:dgr...@mgh.harvard.edu>> wrote:
This can happen if the label is small and/or you've used a lot of smoothing. It 
is better to do this kind of thing in fsaverage space rather than moving the 
labels back to the individual space. If you've run mri_glmfit-sim, then it 
should have created a table file  (something.y.ocn.dat). This file will have a 
row for each subject and a column for each cluster. The value will be the mean 
for that subject in that cluster.

On 2/15/19 6:23 AM, Giuliana Klencklen wrote:

External Email - Use Caution

Hi FS experts,

I did group-level, surface-based, vertex-wise analysis for baseline and 
longitudinal data. I used Qdec and do the same work with the fsgd version 
(mri_glmfit-sim command) to double-check the data.

I created label files with tksurfer for each of the clusters showing a 
significant between-group difference. Then, I used the following command stream 
to extract the cortical thickness values for each subject and cluster: 
mri_label2label, mris_anatomical_stats, and aparcstats2table.
E.g.,
mri_label2label --srcsubject fsaverage --srclabel 
/home/jagust/gklenck/Long_MRI/lh.ac-baseline-rostralmiddlefrontal.label 
--trgsubject ${s}_tp1 --trglabel 
${s}_tp1/label/lh.ac-baseline-rostralmiddlefrontal.label --hemi lh --regmethod 
surface

mris_anatomical_stats -l lh.ac-baseline-rostralmiddlefrontal.label -t 
lh.thickness -b -f ${s}_tp1/stats/lh.ac-baseline-rostralmiddlefrontal.stats 
${s}_tp1 lh

aparcstats2table --subjects ${s}_tp1 --hemi lh --parc 
ac-baseline-rostralmiddlefrontal --meas thickness --tablefile 
${out_dir}/lh.ac-baseline-rostralmiddlefrontal.aparc_stats.txt

Subsequently, when I conducted between-group comparisons for each cluster, a 
couple of clusters (7 out of 16) do not show a significant difference - which 
does not make sense. This problem seems to appear randomly.

Help to the FS archives, I tried to use mri_segstats command but was not able 
to find a correct combination of arguments. If this is the right track to solve 
this problem, what combination of arguments should I use? And if this is not, 
do you have any idea how I can solve this problem?

Many thanks in advance.

Regards,
Giuliana


--
Giuliana Klencklen, Ph.D.

Helen Wills Neuroscience Institute
University of California, Berkeley
118 Barker Hall
Berkeley, CA 94720-3190
510-395-0040
giuliana.klenck...@berkeley.edu



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--
Giuliana Klencklen, Ph.D.

Helen Wills Neuroscience Institute
University of California, Berkeley
118 Barker Hall
Berkeley, CA 94720-3190
510-395-0040
giuliana.klenck...@berkeley.edu



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Re: [Freesurfer] Cortical thickness values extraction issue

[Greve, Douglas N.,Ph.D.]

This can happen if the label is small and/or you've used a lot of smoothing. It 
is better to do this kind of thing in fsaverage space rather than moving the 
labels back to the individual space. If you've run mri_glmfit-sim, then it 
should have created a table file  (something.y.ocn.dat). This file will have a 
row for each subject and a column for each cluster. The value will be the mean 
for that subject in that cluster.

On 2/15/19 6:23 AM, Giuliana Klencklen wrote:

External Email - Use Caution

Hi FS experts,

I did group-level, surface-based, vertex-wise analysis for baseline and 
longitudinal data. I used Qdec and do the same work with the fsgd version 
(mri_glmfit-sim command) to double-check the data.

I created label files with tksurfer for each of the clusters showing a 
significant between-group difference. Then, I used the following command stream 
to extract the cortical thickness values for each subject and cluster: 
mri_label2label, mris_anatomical_stats, and aparcstats2table.
E.g.,
mri_label2label --srcsubject fsaverage --srclabel 
/home/jagust/gklenck/Long_MRI/lh.ac-baseline-rostralmiddlefrontal.label 
--trgsubject ${s}_tp1 --trglabel 
${s}_tp1/label/lh.ac-baseline-rostralmiddlefrontal.label --hemi lh --regmethod 
surface

mris_anatomical_stats -l lh.ac-baseline-rostralmiddlefrontal.label -t 
lh.thickness -b -f ${s}_tp1/stats/lh.ac-baseline-rostralmiddlefrontal.stats 
${s}_tp1 lh

aparcstats2table --subjects ${s}_tp1 --hemi lh --parc 
ac-baseline-rostralmiddlefrontal --meas thickness --tablefile 
${out_dir}/lh.ac-baseline-rostralmiddlefrontal.aparc_stats.txt

Subsequently, when I conducted between-group comparisons for each cluster, a 
couple of clusters (7 out of 16) do not show a significant difference - which 
does not make sense. This problem seems to appear randomly.

Help to the FS archives, I tried to use mri_segstats command but was not able 
to find a correct combination of arguments. If this is the right track to solve 
this problem, what combination of arguments should I use? And if this is not, 
do you have any idea how I can solve this problem?

Many thanks in advance.

Regards,
Giuliana


--
Giuliana Klencklen, Ph.D.

Helen Wills Neuroscience Institute
University of California, Berkeley
118 Barker Hall
Berkeley, CA 94720-3190
510-395-0040
giuliana.klenck...@berkeley.edu



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