[Freesurfer] strange output from lme analysis - Max r value=inf

[Valsasina Paola]

Dear all

I am using the lme toolbox to perform a longitudinal comparison of cortical 
thickness among 4 patient groups. Following the lme tutorial, I concatenated 
all files and I built a design matrix with following columns:

-one column for intercept (all ones)
-one column for time
-four columns for each group (1 for subjects belonging to the group, zero 
otherwise)
-four columns for group by time interaction

I then built appropriate contrasts to test the effects of time, group and time 
x group effect; e.g., to test time x group interaction between group 1 and 
group 2, I built the following contrast:

CM.C=[0 0 0 0 0 0 1 -1 0 0]

However, I obtain quite strange results, because I get a lot of very small, 
very significant clusters of difference (having max t value = inf or -inf).
Do you have any idea of the reason why I am getting this strange output? In 
your opinion, is there anything wrong in my design matrix or in the way I 
defined the contrasts?
This is the first time I am trying to compare four groups in the same LME 
models, I previously ran LME statistics including just one or two groups, and 
everything ran smoothly.

Please see a snapshot of my output statistic below:


# ClusterNo  Max   VtxMax   Size(mm^2)  TalX   TalY   TalZNVtxs   Annot
   1 -inf  112586  0.96-19.0  -95.28.3 1  
lateraloccipital
   2 -inf   93693  0.89-22.0   54.4   16.4 1  
rostralmiddlefrontal
   3  inf   29207  0.86-35.9   46.6   -9.9 1  parsorbitalis
   4  inf  158564  0.78-13.7  -98.82.3 1  
lateraloccipital
   5 -inf   67614  0.69 -8.3   35.1   25.4 1  
superiorfrontal
   6 -inf  123144  0.69-40.3   19.5   29.8 1  
caudalmiddlefrontal
   7 -inf2273  0.68-55.6  -30.9   24.2 1  supramarginal
   8 -inf  140663  0.67 -6.2   39.1   -4.4 1  
rostralanteriorcingulate
   9 -inf5857  0.64-35.58.6   50.8 1  
caudalmiddlefrontal
  10  inf   87756  0.61-14.1  -98.21.7 1  
lateraloccipital
  11  inf   95576  0.56-43.9   24.5   27.5 1  
rostralmiddlefrontal
  12 -inf  112122  0.56-29.0  -62.8   35.9 1  
inferiorparietal
  13  inf   54800  0.54-18.7  -59.32.6 1  lingual
  14 -inf  140378  0.53-19.0  -42.5   62.6 1  
superiorparietal
  15  inf   99044  0.49-43.7  -20.0   52.6 1  postcentral
  16  inf  119372  0.47-52.1   -2.5   25.5 1  precentral
  17 -inf  125951  0.47-44.3  -55.1   12.7 1  
inferiorparietal
  18  inf8776  0.46-57.0   -2.0   11.7 1  precentral
  19  inf   10137  0.44-27.8  -49.8   -1.9 1  lingual
  20  inf   20153  0.42 -6.6   13.8   29.5 1  
caudalanteriorcingulate
  21 -inf   19380  0.40-61.6   -8.6   13.7 1  postcentral
  22 -inf   38802  0.39-23.9  -56.1   49.0 1  
superiorparietal
  23  inf   53936  0.38-45.3  -25.9   46.7 1  postcentral
  24  inf   45406  0.35-38.8  -36.8   36.7 1  supramarginal
  25 -inf   46213  0.32 -6.4  -47.6   15.7 1  
isthmuscingulate
  26  inf   41829  0.26-61.1   -8.7   10.5 1  postcentral
  27  -13.032   76609  0.40-14.4  -15.8   64.7 1  precentral
  28   12.272   78924  0.27 -6.0   -6.5   30.3 1  
posteriorcingulate
  29  -10.960 822  0.63-51.8  -30.1  -17.2 1  
inferiortemporal
  30   10.326   96572  0.48-31.7   11.60.5 1  insula
  31   -8.509   43473  0.54-62.2  -26.36.7 1  
superiortemporal
.. continued with a lot of significant clusters, all 1 vertex in size ..


Thank you for any suggestion
Best regards

Paola




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[Freesurfer] leaving out one timepoint from a longitudinal analysis

[Valsasina Paola]

Dear all

I already ran the Freesurfer longitudinal stream on 4 timepoints from a set of 
subjects (dataset1).
I would like to perform a new analysis, merging these subjects with another 
subjects' dataset (dataset2), which has only 3 timepoints. For this second 
analysis, I will use just the first 3 timepoints of dataset1. Should I re-run 
the longitudinal stream on the first 3 timepoints of dataset1, or is it fine if 
I use the output I already have (obtained using a template with 4 timepoints)?
Thanks for any suggestion
Best regards
Paola



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[Freesurfer] I: Demeaning of "group" covariate in LME models

[Valsasina Paola]

Dear Doug

this is a snapshot of the website 
(https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels)  where the 
categorical variable "group" is coded as a number (see point 4)


**
Example data analyses

Here are two example analyses using ADNI data processed with Freesurfer.

a)Univariate

In this case the interest was in determining the differences in mean 
hippocampal volume change over time among four groups of individuals: healthy 
controls (HC), stable mild cognitive impairment patients (sMCI), MCI patients 
who converted to Alzheimer's disease (cMCI) during the follow-up period and 
patients with Alzheimer's disease (AD) at baseline. A Qdec table file, 
qdec.table.dat, was used to process in Freesurfer all time point scans from all 
subjects included in the study. It contains the following columns:

1) Freesurfer's fsid (fsid)

2) subject ID (fsid-base)

3) time from baseline (time)

4) group (HC=1, sMCI=2, cMCI=3, AD=4)

5) Apoe4 (E4:carriers=1, non-carriers=0)

6) Gender (female=1, male=0)

7) Age at baseline (BslAge)

8) Education (in years).

***

In the case reported on the website, there were 4 groups, and groups were 
encoded with the following numbers: HC=1, sMCI=2, cMCI=3, AD=4.

In my analysis, I have just two groups (HC and Patients). If I put HC=1, 
Patients=2 (following the example of the website), I obtain some results from 
the LME model. However, if I center around 0 the "group" covariate, (i.e., 
HC=1, Patients=-1), I obtain different results from LME estimation. Therefore, 
I was wondering whether it is more correct to de-mean the "group" covariate or 
not.

Thank you for any suggestion
Kind regards
Paola



-Messaggio originale-
Da: freesurfer-boun...@nmr.mgh.harvard.edu 
[mailto:freesurfer-boun...@nmr.mgh.harvard.edu] Per conto di Douglas N Greve
Inviato: 29 September 2016 18:37
A: freesurfer@nmr.mgh.harvard.edu
Oggetto: Re: [Freesurfer] Demeaning of "group" covariate in LME models

sorry, not sure I understand. Are you coding a catagorical variable as a 
continuous covariate? What tutorial are you referring to?


On 09/29/2016 10:39 AM, Valsasina Paola wrote:
>
> Dear all,
>
> I am trying to perform a longitudinal statistic by using the LME mass
> univariate toolbox. I have two groups of subjects (each acquired 3 or
> 4 times over the follow-up), and I want to test whether atrophy
> progresses differently over time in these two groups.
>
> I followed the tutorial on the Website, and I used the following
> design matrix to test the time x group interaction: X=[ones(length
> (M),1) M M(:,1).*M(:,2)]
>
> I first tried to code the categorical variable “group” assigning
> values 1 and 2 for subjects belonging to group 1 and 2, respectively
> (as it is suggested also in the tutorial). Then, I tried to re-run the
> same model by using mean-centered values for “group” (i.e., assigning
> -1 to subjects of group 1, and 1 to subjects of group 2).
>
> When I run the two statistical models, I obtain different results.
> Therefore, I would like to ask which is the correct way to model the
> group covariate? Should it be zero-centered or not? How de-meaning is
> affecting the estimate?
>
> Thank you so much for any suggestion
>
> Kind regards
>
> Paola
>
>
>
> /Rispetta l’ambiente: non stampare questa mail se non è necessario.
> Respect the environment: if it's not necessary, don't print this mail./
>
>
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> Devolvi il tuo 5 per mille all’Ospedale San Raffaele: perché al centro
> della Ricerca ci sei TU. CODICE FISCALE: 07636600962, nel riquadro
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>
>
>
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--
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MGH-NMR Center
gr...@nmr.mgh.harvard.edu
Phone Number: 617-724-2358
Fax: 617-726-7422

Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2
www.nmr.mgh.harvard.edu/facility/filedrop/index.html
Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/

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[Freesurfer] Demeaning of "group" covariate in LME models

[Valsasina Paola]

Dear all,

I am trying to perform a longitudinal statistic by using the LME mass 
univariate toolbox. I have two groups of subjects (each acquired 3 or 4 times 
over the follow-up), and I want to test whether atrophy progresses differently 
over time in these two groups.

I followed the tutorial on the Website, and I used the following design matrix 
to test the time x group interaction: X=[ones(length (M),1) M M(:,1).*M(:,2)]

I first tried to code the categorical variable "group" assigning values 1 and 2 
for subjects belonging to group 1 and 2, respectively (as it is suggested also 
in the tutorial). Then, I tried to re-run the same model by using mean-centered 
values for "group" (i.e., assigning -1 to subjects of group 1, and 1 to 
subjects of group 2).

When I run the two statistical models, I obtain different results. Therefore, I 
would like to ask which is the correct way to model the group covariate? Should 
it be zero-centered or not? How de-meaning is affecting the estimate?

Thank you so much for any suggestion
Kind regards
Paola




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[Freesurfer] R: R: problems by running longitudinal mass univariate analysis

[Valsasina Paola]

Dear Martin

thank you for your answer. Yes, it was a collinearity problem in the design 
matrix. I modified it and now everything works fine
Best
paola



Da: freesurfer-boun...@nmr.mgh.harvard.edu 
[mailto:freesurfer-boun...@nmr.mgh.harvard.edu] Per conto di Martin Reuter
Inviato: 27 July 2016 17:16
A: Freesurfer support list
Oggetto: Re: [Freesurfer] R: problems by running longitudinal mass univariate 
analysis

Hi Paola,

there is probably a problem with the design matrix. What exactly are the 
columns of M ?

Also how many subjects (and time points) do you have?

I am not sure, but I think you can open a thickness stack on top of the 
corresponding surface (from fsaverag) in freeview. It will think it is a time 
course, but that is OK. Not sure how to switch between the thicknesses, but 
there is a way to click a vertex and see the time course (all the thickness 
values at that location).  Also Qdec may do that.

Best, Martin
On 07/20/2016 10:35 AM, Valsasina Paola wrote:
Dear Martin

to follow-up mu previous question, I was finally able to run the 
lme_mass_fit_vw script till the end. Unfortunately, it came out that converge 
of the algorithm was failed in 99% of the locations.
I have only one study group (with a variable number of follow-up , and I used 
as design matrix exactly the same proposed in the online guide:
X=[ones(length(M),1) M M(:,1).*M(:,2)]

so, I don’t see any specific region of failure associated with the design 
matrix

Therefore, I suppose that this is due to the fact that input images are wrong 
(empty or with some problems?).
How can I investigate further the correctness of my input data? The 
longitudinal pipeline was performed correctly, and mris_preproc command to 
generate the mass univariate file seems to work without errors. Can I 
eventually load somewhere my image file (i.e., the concatenated 
lh.thickness.mgh including all scans), so that someone chan check that input 
images for lme estimation are OK?
Thank you so much in advance
Kind regards
Paola


Da: 
freesurfer-boun...@nmr.mgh.harvard.edu<mailto:freesurfer-boun...@nmr.mgh.harvard.edu>
 [mailto:freesurfer-boun...@nmr.mgh.harvard.edu] Per conto di Martin Reuter
Inviato: 14 July 2016 12:12
A: Freesurfer support list
Oggetto: Re: [Freesurfer] problems by running longitudinal mass univariate 
analysis


Hi Paola,

1 . It really takes a long time, though I think you probably are missing the 
parallel toolbox, and running this sequentially will be really slow.

2. You need to wait till the end and look at the percent of places where it did 
not converge. It is normal that it does not converge in some places. Also make 
sure the design matrix is not (close to) singular, which can happen if you have 
nearly collinear columns.

Best Martin
On Jul 14, 2016 10:43 AM, Valsasina Paola 
<valsasina.pa...@hsr.it><mailto:valsasina.pa...@hsr.it> wrote:

Dear FS experts



I am trying to run the commands for performing the longitudinal mixed effect 
statistics with the LME Matlab toolbox.

I am following the instructions on the web page; however, I have two questions:



1) when I run the command lme_mass_fit_vw, the script is running for a very 
long time, after 3 hours the estimation was just at 10% (unfortunately, my 
laptop is equipped with Windows and is not particularly powerful). I had to 
stop the script at that point; before restarting it again I would like to be 
sure: is this behavior normal?  Is it correct that lme_mass_fit_vw is running 
for such a long time?



2) when the script was running, there were several warnings on the Matlab 
screen, saying that the algorithm was not converging (at most of locations). Is 
it also normal? How can I check the correctness of my input image? When I open 
with freeview the “lh.thickness_sm10.mgh” volume, I just see a black screen 
with valid timeseries values in the minority of voxels (I imagine that these 
timeseries are concatenated thickness values of my study subjects). Is there 
another way to better visualize and check a concatenated 4d mgh file? Failure 
of convergence is referring to empty/zero voxels of this image?



Thank you for any advice on these two topics

Kind regards

Paola






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[Freesurfer] R: problems by running longitudinal mass univariate analysis

[Valsasina Paola]

Dear Martin

to follow-up mu previous question, I was finally able to run the 
lme_mass_fit_vw script till the end. Unfortunately, it came out that converge 
of the algorithm was failed in 99% of the locations.
I have only one study group (with a variable number of follow-up , and I used 
as design matrix exactly the same proposed in the online guide:
X=[ones(length(M),1) M M(:,1).*M(:,2)]

so, I don’t see any specific region of failure associated with the design matrix

Therefore, I suppose that this is due to the fact that input images are wrong 
(empty or with some problems?).
How can I investigate further the correctness of my input data? The 
longitudinal pipeline was performed correctly, and mris_preproc command to 
generate the mass univariate file seems to work without errors. Can I 
eventually load somewhere my image file (i.e., the concatenated 
lh.thickness.mgh including all scans), so that someone chan check that input 
images for lme estimation are OK?
Thank you so much in advance
Kind regards
Paola


Da: freesurfer-boun...@nmr.mgh.harvard.edu 
[mailto:freesurfer-boun...@nmr.mgh.harvard.edu] Per conto di Martin Reuter
Inviato: 14 July 2016 12:12
A: Freesurfer support list
Oggetto: Re: [Freesurfer] problems by running longitudinal mass univariate 
analysis


Hi Paola,

1 . It really takes a long time, though I think you probably are missing the 
parallel toolbox, and running this sequentially will be really slow.

2. You need to wait till the end and look at the percent of places where it did 
not converge. It is normal that it does not converge in some places. Also make 
sure the design matrix is not (close to) singular, which can happen if you have 
nearly collinear columns.

Best Martin
On Jul 14, 2016 10:43 AM, Valsasina Paola <valsasina.pa...@hsr.it> wrote:

Dear FS experts



I am trying to run the commands for performing the longitudinal mixed effect 
statistics with the LME Matlab toolbox.

I am following the instructions on the web page; however, I have two questions:



1) when I run the command lme_mass_fit_vw, the script is running for a very 
long time, after 3 hours the estimation was just at 10% (unfortunately, my 
laptop is equipped with Windows and is not particularly powerful). I had to 
stop the script at that point; before restarting it again I would like to be 
sure: is this behavior normal?  Is it correct that lme_mass_fit_vw is running 
for such a long time?



2) when the script was running, there were several warnings on the Matlab 
screen, saying that the algorithm was not converging (at most of locations). Is 
it also normal? How can I check the correctness of my input image? When I open 
with freeview the “lh.thickness_sm10.mgh” volume, I just see a black screen 
with valid timeseries values in the minority of voxels (I imagine that these 
timeseries are concatenated thickness values of my study subjects). Is there 
another way to better visualize and check a concatenated 4d mgh file? Failure 
of convergence is referring to empty/zero voxels of this image?



Thank you for any advice on these two topics

Kind regards

Paola






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IL TUO 5XMILLE AL SAN RAFFAELE DI MILANO

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Non c’è cura, senza ricerca. Non c’è ricerca, senza il tuo 5xmille. Scopri come 
su http://www.5xmille.org
[Immagine rimossa dal mittente.]


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[Freesurfer] R: problems by running longitudinal mass univariate analysis

[Valsasina Paola]

Dear Martin

thank you so much for your answer. Actually, I tried to run the script with the 
parallel toolbox, but I got some errors and I had to run the version without 
parallel computing.
Anyway, I will run the script till the end and let you know how results look 
like. The design matrix is really similar to that reported in the example Web 
page, since I have only one group, in which I want to estimate the effect of 
time and time x group interaction
Best
Paola


Da: freesurfer-boun...@nmr.mgh.harvard.edu 
[mailto:freesurfer-boun...@nmr.mgh.harvard.edu] Per conto di Martin Reuter
Inviato: 14 July 2016 12:12
A: Freesurfer support list
Oggetto: Re: [Freesurfer] problems by running longitudinal mass univariate 
analysis


Hi Paola,

1 . It really takes a long time, though I think you probably are missing the 
parallel toolbox, and running this sequentially will be really slow.

2. You need to wait till the end and look at the percent of places where it did 
not converge. It is normal that it does not converge in some places. Also make 
sure the design matrix is not (close to) singular, which can happen if you have 
nearly collinear columns.

Best Martin
On Jul 14, 2016 10:43 AM, Valsasina Paola <valsasina.pa...@hsr.it> wrote:

Dear FS experts



I am trying to run the commands for performing the longitudinal mixed effect 
statistics with the LME Matlab toolbox.

I am following the instructions on the web page; however, I have two questions:



1) when I run the command lme_mass_fit_vw, the script is running for a very 
long time, after 3 hours the estimation was just at 10% (unfortunately, my 
laptop is equipped with Windows and is not particularly powerful). I had to 
stop the script at that point; before restarting it again I would like to be 
sure: is this behavior normal?  Is it correct that lme_mass_fit_vw is running 
for such a long time?



2) when the script was running, there were several warnings on the Matlab 
screen, saying that the algorithm was not converging (at most of locations). Is 
it also normal? How can I check the correctness of my input image? When I open 
with freeview the “lh.thickness_sm10.mgh” volume, I just see a black screen 
with valid timeseries values in the minority of voxels (I imagine that these 
timeseries are concatenated thickness values of my study subjects). Is there 
another way to better visualize and check a concatenated 4d mgh file? Failure 
of convergence is referring to empty/zero voxels of this image?



Thank you for any advice on these two topics

Kind regards

Paola






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[Freesurfer] problems by running longitudinal mass univariate analysis

[Valsasina Paola]

Dear FS experts

I am trying to run the commands for performing the longitudinal mixed effect 
statistics with the LME Matlab toolbox.
I am following the instructions on the web page; however, I have two questions:

1) when I run the command lme_mass_fit_vw, the script is running for a very 
long time, after 3 hours the estimation was just at 10% (unfortunately, my 
laptop is equipped with Windows and is not particularly powerful). I had to 
stop the script at that point; before restarting it again I would like to be 
sure: is this behavior normal?  Is it correct that lme_mass_fit_vw is running 
for such a long time?

2) when the script was running, there were several warnings on the Matlab 
screen, saying that the algorithm was not converging (at most of locations). Is 
it also normal? How can I check the correctness of my input image? When I open 
with freeview the "lh.thickness_sm10.mgh" volume, I just see a black screen 
with valid timeseries values in the minority of voxels (I imagine that these 
timeseries are concatenated thickness values of my study subjects). Is there 
another way to better visualize and check a concatenated 4d mgh file? Failure 
of convergence is referring to empty/zero voxels of this image?

Thank you for any advice on these two topics
Kind regards
Paola




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