[Freesurfer] How to interpret vertex-wise findings?

[Julian Macoveanu]

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Dear Freesurfers,

I have a simple design with patients and controls and would like to
find regions showing group differences in cortical volume and
thickness. I also remove the effect of age, sex and ICV. Running the
GLM group analysis by the book I get a few clusters showing
significant differences in intercept.

1. I interpret this like patients have smaller “predicted” regional
volume at age 0, or at mean age of the group (if demeaned age
covariate) compared to controls. How can this finding be described in
a more clinically meaningful way?

2. Related to the above, how do I visualize what is going on in the
significant clusters? The extracted values from these clusters
(ocn.dat) show no group differences in volume when displaying the
group means in SPSS (using either age/sex/ICV adjusted or non-adjusted
values). Maybe I am misunderstanding something, how can there be
differences in intercept but no differences between group means?
(slopes not significantly different). How do I best describe and
visualize this group difference in a paper?

Going through the studies I found on Pubmed that used Freesurfer with
vertex-wise GLM, so far, I only found e.g. the “lower thickness”
interpretation in one group vs the other and not “lower intercept”. In
my case at least I cannot say this since the extracted values between
groups show no group difference.

Julian

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[Freesurfer] Visualizing and interpreting vertex-wise GLM analysis

[Julian Macoveanu]

External Email - Use Caution



Dear Freesurfers,

I have a simple design with patients and controls and would like to
find regions showing group differences in cortical volume and
thickness. I also remove the effect of age, sex and ICV. Running the
GLM group analysis by the book I get a few clusters showing
significant differences in intercept.

1. I interpret this like patients have smaller “predicted” regional
volume at age 0, or at mean age of the group (if demeaned age
covariate) compared to controls. How can this finding be described in
a more clinically meaningful way?

2. Related to the above, how do I visualize what is going on in the
significant clusters? The extracted values from these clusters
(ocn.dat) show no group differences in volume when displaying the
group means in SPSS (using either age/sex/ICV adjusted or non-adjusted
values). Maybe I am misunderstanding something, how can there be
differences in intercept but no differences between group means?
(slopes not significantly different). How do I best describe and
visualize this group difference in a paper?

Going through the studies I found on Pubmed that used Freesurfer with
vertex-wise GLM, so far, I only found e.g. the “lower thickness”
interpretation in one group vs the other and not “lower intercept”. In
my case at least, I cannot say this since the extracted values between
groups show no group difference.

Julian

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[Freesurfer] Visualizing and interpreting vertex-wise GLM analysis

[Julian Macoveanu]

External Email - Use Caution

Dear Freesurfers,

I have a simple design with patients and controls and would like to
find regions showing group differences in cortical volume and
thickness. I also remove the effect of age, sex and ICV. Running the
GLM group analysis by the book I get a few clusters showing
significant differences in intercept.

1. I interpret this like patients have smaller “predicted” regional
volume at age 0, or at mean age of the group (if demeaned age
covariate) compared to controls. How can this finding be described in
a more clinically meaningful way?

2. Related to the above, how do I visualize what is going on in the
significant clusters? The extracted values from these clusters
(ocn.dat) show no group differences in volume when displaying the
group means in SPSS (using either age/sex/ICV adjusted or non-adjusted
values). Maybe I am misunderstanding something, how can there be
differences in intercept but no differences between group means?
(slopes not significantly different). How do I best describe and
visualize this group difference in a paper?

Julian

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[Freesurfer] Linear mixed model for longitudinal data

[Julian Macoveanu]

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Dear Kersten,

Thank you for your answer, I have some follow-up questions to the
first two points before I can move on.

1) Regarding family-relation, since three random effects may not work,
did you mean that a simpler model with 2 random effects (intercept and
family ID, thus leaving out slope which I assume is time from
baseline) would work? While forgetting family ID is an option, it
would be a model that may attract criticism.

2) Regarding multiple comparison correction, I understand how to
perform the procedure you suggested (second option) to simply
calculate the corrected p threshold using pcor = -log10(pth) and then
use pcor as threshold for the sig.mgh. However after running FDR2:
[detvtx,sided_pval,pth] =
lme_mass_FDR2(F_lhstats.pval,F_lhstats.sgn,lhcortex,0.05,0);

While detvtx is not empty (a few vertexes survived) and the pth value
is calculated to 1.0e-30 and therefore pcor to 30. This doesn’t
seem right. What is also strange is that I saw a few times a more
reasonable pth in the range of e-5 using the exact same data and
procedure. I cannot replicate this in the same analysis in R2021, now
I only get the e-30 (I only tried left cortex thickness so far).

Best,
Julian

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[Freesurfer] Linear mixed model for longitudinal data

[Julian Macoveanu]

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(Apologies for reposting due to lack of answer)

Dear all,

This is my first attempt to analyze longitudinal structural data with
freesurfer, and I just do my best to follow the tutorial. I have some
things not clear, so any help would be appreciated.

We have a longitudinal study design with 2 groups, healthy and
patients who were scanned at baseline a second time following a
variable time interval (6 to 36 months). We want to investigate
whether the patient’s cortical thickness/volume progresses differently
compared to controls (group-by-time interaction effects). I chose to
analyze the data with LME due to variable follow-up time, having only
50% of the patients with a second scan, and also having family members
within and between groups.

Questions
1) To account for familial relationship, should I add a family ID as
covariate that stays in the design matrix X and count this covariate
as random effect when the model is fitted? Like this:
lhstats = lme_mass_fit_Rgw(X,[1 2 3],Y,ni,lhTh0,lhRgs,lhsphere);

Where the first 3 covariates in X are counted as random effects: 1 the
intercept, 2 follow-up time and 3 family ID.

The X would further include group, age, sex, TIV, and group*time, the
last one being the interaction term we want to assess using CM.C = [0
0 0 0 0 0 0 1].

Does the X and CM look good to address our research question?

2) It is unclear to me how to perform correction for multiple
comparisons and assess what is significant. I follow this procedure
after constructing X:

[lhTh0,lhRe] = lme_mass_fit_EMinit(X,[1 2 3],Y,ni,lhcortex,3);
[lhRgs,lhRgMeans] = lme_mass_RgGrow(lhsphere,lhRe,lhTh0,lhcortex,2,95);
lhstats = lme_mass_fit_Rgw(X,[1 2 3],Y,ni,lhTh0,lhRgs,lhsphere);
F_lhstats = lme_mass_F(lhstats,CM);
dvtx = lme_mass_FDR2(F_lhstats.pval,F_lhstats.sgn,lhcortex,0.05,0):

The dvtx is not empty but has 1 value. What I don’t understand, when
constructing the sig.mgh map it appears the FDR2 correction info is
not used:
fs_write_fstats(F_lhstats,mri,'sig.mgh','sig');

How can I be sure when I import sig.mgh in Freeview I only look at the
vertexes surviving multiple comparisons? The sig.mgh file shows a lot
of regions when thresholded at min 1.3

3) Given a few clusters showing an interaction effect which are saved
as sig.mgh and overlaid on surface in freeview, how do I know
(visualize) what is going on in these clusters, like which of the
group means go up or down over time? (I use FS71 and tksurfer not
working). Is it here that printing out and plotting the betas (of the
interaction term) are useful for?

4) I have a hypothesis to find differences in prefrontal cortex, at
which step would I apply a PFC mask? I have constructed a PFC.label
file by adding corresponding label files.

Thank you,
Julian

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addressed.  If you believe this e-mail was sent to you in error and the e-mail 
contains patient information, please contact the Mass General Brigham 
Compliance HelpLine at https://www.massgeneralbrigham.org/complianceline 
 .
Please note that this e-mail is not secure (encrypted).  If you do not wish to 
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this message immediately.  Continuing to send or respond to e-mail after 
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[Freesurfer] Linear mixed model for longitudinal data

[Julian Macoveanu]

External Email - Use Caution

Dear all,

This is my first attempt to analyze longitudinal structural data with
freesurfer, and I just do my best to follow the tutorial. I have some
things not clear, so any help would be appreciated.

We have a longitudinal study design with 2 groups, healthy and
patients who were scanned at baseline a second time following a
variable time interval (6 to 36 months). We want to investigate
whether the patient’s cortical thickness/volume progresses differently
compared to controls (group-by-time interaction effects). I chose to
analyze the data with LME due to variable follow-up time, having only
50% of the patients with a second scan, and also having family members
within and between groups.

Questions
1) To account for familial relationship, should I add a family ID as
covariate that stays in the design matrix X and count this covariate
as random effect when the model is fitted? Like this:
lhstats = lme_mass_fit_Rgw(X,[1 2 3],Y,ni,lhTh0,lhRgs,lhsphere);

Where the first 3 covariates in X are counted as random effects: 1 the
intercept, 2 follow-up time and 3 family ID.

The X would further include group, age, sex, TIV, and group*time, the
last one being the interaction term we want to assess using CM.C = [0
0 0 0 0 0 0 1].

Does the X and CM look good to address our research question?

2) It is unclear to me how to perform correction for multiple
comparisons and assess what is significant. I follow this procedure
after constructing X:

[lhTh0,lhRe] = lme_mass_fit_EMinit(X,[1 2 3],Y,ni,lhcortex,3);
[lhRgs,lhRgMeans] = lme_mass_RgGrow(lhsphere,lhRe,lhTh0,lhcortex,2,95);
lhstats = lme_mass_fit_Rgw(X,[1 2 3],Y,ni,lhTh0,lhRgs,lhsphere);
F_lhstats = lme_mass_F(lhstats,CM);
dvtx = lme_mass_FDR2(F_lhstats.pval,F_lhstats.sgn,lhcortex,0.05,0):

The dvtx is not empty but has 130 vertexes that survive. What I don’t
understand, when constructing the sig.mgh map it appears the dvtx is
not used:
fs_write_fstats(F_lhstats,mri,'sig.mgh','sig');
How can I be sure when I import sig.mgh in Freeview I only look at the
vertexes surviving multiple comparison?

3) Given a few clusters showing an interaction effect which are saved
as sig.mgh and overlaid on surface in freeview, how do I know
(visualize) what is going on in these clusters, like which of the
group means go up or down? (I use FS71 and tksurfer not working). Is
it here that printing out and plotting the betas (of the interaction
term) are useful for?

4) I have a hypothesis to find differences in prefrontal cortex, at
which step would I apply a PFC mask? I have constructed a PFC.label
file by adding corresponding label files.

Thank you,
Julian

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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 Mass General Brigham 
Compliance HelpLine at https://www.massgeneralbrigham.org/complianceline 
 .
Please note that this e-mail is not secure (encrypted).  If you do not wish to 
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[Freesurfer] Subject cluster mean values from ocn.dat do not show the expected group difference

[Julian Macoveanu]

External Email - Use Caution

Hi,
I am using FS 7.1.0. After running the surface thickness GLM analysis
I get one cluster after correction for multiple comparison showing a
significant group difference. I want to make a plot illustrating this
group difference. However, when using the mean values from:

cache.th30.abs.y.ocn.dat, I don’t see the expected group difference in
these values.

Question 1)
Are the subjects cluster mean values in y.ocn.dat adjusted for the
covariates I used in the fsgd file? E.g. age, sex and TIV?

Question 2)
I inserted these mean cluster values from y.ocn.dat in SPSS and tried
verify the statistics both with and without adjustment for covariates
and did not get the expected significant group region which I expected
in either case. Why is this?

Question 3)
I moved on to extract the thickness values from peak vertex in my
significant cluster. For this I used mris_convert which I just wanted
to be confirmed that is correct:

mris_convert -c lh.thickness.fwhm15.fsaverage.mgh
$freesurferdir/fsaverage/surf/lh.orig  output.asc

After extracting the thickness value for all subjects from the peak
vertex as reported by the sig.cluster.summary, I still have the same
issue as before, I find no expected group difference.

Interestingly, the significant cluster I found is part of the
superior_frontal region in aparc2009. When I extract these values it
almost shows significant group differences in the entire region, in
line with the surface GLM findings.

Thanks,
Julian

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[Freesurfer] Issue with extracted thickness value from significant cluster or peak vertex

[Julian Macoveanu]

External Email - Use Caution

Sorry I had to re-posted this since since it quickly moved down the
list without an answer

Julian Macoveanu Thu, 22 Oct 2020 00:59:50 -0700

Hi,
I am using FS 7.1.0. After running the surface thickness GLM analysis
I get one cluster after correction for multiple comparison showing a
significant group difference. I want to make a plot illustrating this
group difference. However, when using the mean values from:

cache.th30.abs.y.ocn.dat, I don’t see the expected group difference in
these values.

Question 1)
Are the subjects cluster mean values in y.ocn.dat adjusted for the
covariates I used in the fsgd file? E.g. age, sex and TIV?

Question 2)
I inserted these mean cluster values from y.ocn.dat in SPSS and tried
verify the statistics both with and without adjustment for covariates
and did not get the expected significant group region which I expected
in either case. Why is this?

Question 3)
I moved on to extract the thickness values from peak vertex in my
significant cluster. For this I used mris_convert which I just wanted
to be confirmed that is correct:

mris_convert -c lh.thickness.fwhm15.fsaverage.mgh
$freesurferdir/fsaverage/surf/lh.orig  output.asc

After extracting the thickness value for all subjects from the peak
vertex as reported by the sig.cluster.summary, I still have the same
issue as before, I find no expected group difference.

Interestingly, the significant cluster I found is part of the
superior_frontal region in aparc2009. When I extract these values it
almost shows significant group differences in the entire region, in
line with the surface GLM findings.

Thanks,
Julian

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[Freesurfer] Issue with extracted thickness value from significant cluster or peak vertex

[Julian Macoveanu]

External Email - Use Caution

Hi,
I am using FS 7.1.0. After running the surface thickness GLM analysis
I get one cluster after correction for multiple comparison showing a
significant group difference. I want to make a plot illustrating this
group difference. However, when using the mean values from:

cache.th30.abs.y.ocn.dat, I don’t see the expected group difference in
these values.

Question 1)
Are the subjects cluster mean values in y.ocn.dat adjusted for the
covariates I used in the fsgd file? E.g. age, sex and TIV?

Question 2)
I inserted these mean cluster values from y.ocn.dat in SPSS and tried
verify the statistics both with and without adjustment for covariates
and did not get the expected significant group region which I expected
in either case. Why is this?

Question 3)
I moved on to extract the thickness values from peak vertex in my
significant cluster. For this I used mris_convert which I just wanted
to be confirmed that is correct:

mris_convert -c lh.thickness.fwhm15.fsaverage.mgh
$freesurferdir/fsaverage/surf/lh.orig  output.asc

After extracting the thickness value for all subjects from the peak
vertex as reported by the sig.cluster.summary , I still have the same
issue as before, I find no group expected group difference.

Interestingly, the significant cluster I found is part of the
superior_frontal region in aparc2009. When I extract these values it
almost shows significant group differences in the entire region, in
line with the surface GLM findings.

Thanks,
Julian

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[Freesurfer] Accounting for family relation in FS

[Julian Macoveanu]

External Email - Use Caution

Dear FS List,

(apologies for reposting under different title)

I wanted to check if my GLM design for a surface based analysis is sound. I
am new to FS so please don’t skip over details.

I have 3 groups of monozygotic twins: low-risk, high-risk, ill. Twin pairs
may be present in the same group (e.g both are in the low-risk) or between
groups (one of them in high-risk one in affected).  Not all twins have
their co-twin in the analysis though.

In order to account for with-pair variance correlation I was wondering if
this FSGD example with 9 subjects might do it or if there might be better
options.

GroupDescriptorFile 1
Title …
Class LR
Class HR
Class ILL
Variablesage sex TIV  Pair_no1 Pair_no2 Pair_no3
Input subject1 LR  30 M 123456 1 0 0
Input subject2 LR  30 M 123456 1 0 0
Input subject3 LR  23 F 123456 0 0 0
Input subject4 HR  20 F 123456 0 1 0
Input subject5 HR  21 F 123456 0 0 1
Input subject6 HR  22 M 123456 0 0 0
Input subject7 ILL 20 F 123456 0 1 0
Input subject8 ILL 21 F 123456 0 0 1
Input subject9 ILL 35 F 123456 0 0 0

I do have 60+ pairs so it will be a lot of variables. I know that PALM has
an options to provide an exchangeability file, but FsPalm seems to perform
corrections for multiple comparisons only.

Julian
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[Freesurfer] GLM twin design

[Julian Macoveanu]

External Email - Use Caution

Dear FS List,

I wanted to check if my GLM design for a surface based analysis is sound. I
am new to FS so please don’t skip over details.

I have 3 groups of monozygotic twins: low-risk, high-risk, ill. Twin pairs
may be present in the same group (e.g both are in the low-risk) or between
groups (one of them in high-risk one in affected).  Not all twins have
their co-twin in the analysis though.

In order to account for with-pair variance correlation I was wondering if
this FSGD example with 9 subjects might do it or if there might be better
options.

GroupDescriptorFile 1
Title …
Class LR
Class HR
Class ILL
Variables  age sex TIV  Pair_no1 Pair_no2 Pair_no3
Input subject1 LR 30 M 123456 1 0 0
Input subject2 LR 30 M 123456 1 0 0
Input subject3 LR 23 F 123456 0 0 0
Input subject4 HR 20 F 123456 0 1 0
Input subject5 HR 21 F 123456 0 0 1
Input subject6 HR 22 M 123456 0 0 0
Input subject7 ILL 20 F 123456 0 1 0
Input subject8 ILL 21 F 123456 0 0 1
Input subject9 ILL 35 F 123456 0 0 0

I do have 60+ pairs so it will be a lot of variables. I know that PALM has
an options to provide an exchangeability file, but FsPalm seems to perform
corrections for multiple comparisons only.

Julian
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