I created the contrast:
CM.C = [1 1 0 0 0 0; 1 1 0 0 0 0]
Then I ran the following:
F_lhstats = lme_mass_F(lhstats,CM);
I received the following error message, which kept on repeating in loop.
Warning: Matrix is singular to working precision.
> In matlab.internal.math.mpower.viaMtimes (line 35)
In parallel_function>make_general_channel/channel_general (line 914)
In remoteParallelFunction (line 38)
Finally, I had to terminate the operation Ctrl + C, which displayed the
following:
Operation terminated by user during matlab.internal.math.mpower.viaMtimes (line
35)
In parallel_function>make_general_channel/channel_general (line 914)
O = F(C{:});
In remoteParallelFunction (line 38)
out = parallel.internal.pool.serialize(feval(channel, channelArgs{:}));
Operation terminated by user during distcomp.remoteparfor/getCompleteIntervals
(line 127)
In parallel_function>distributed_execution (line 820)
[tags, out] = P.getCompleteIntervals(chunkSize);
In parallel_function (line 587)
R = distributed_execution(...
In lme_mass_F (line 92)
parfor i=1:nv
Mayank
On Mar 8, 2018, at 6:36 AM, Diers, Kersten /DZNE
<kersten.di...@dzne.de<mailto:kersten.di...@dzne.de>> wrote:
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On Do, 2018-03-08 at 03:56 +0100, Kaushal, Mayank wrote:
Your inputs have been very helpful.
Looking at the examples on LME webpage, the contrast for both
univariate and mass-variate creates 3 rows with 14 columns for
univariate and 17 for mass-variate analysis. My understanding is that
3 rows have been created because there are 4 groups being analyzed
and applying that rationale to my data would mean that the contrast
for my analysis would require 2 rows since I comparing 3 groups. Is
my logic correct here?
Yes, that's correct.
As a side note, it only applies when you want to include all three
groups in a contrast. If you compare only two groups against each
other, then a single line could be sufficient also in your design.
So, for my analysis if I want to include two random effects, namely,
intercept and time_from_base_scan, which are given in Column 1 and 2,
respectively, the contrasts based on M having 6 columns would as
follows:
CM.C = [1 1 ; 1 1] zeros(2,4)];
or
CM.C = [1 1 0 0 0 0; 1 1 0 0 0 0]
This may be more tricky. It is not necessarily / exclusively the random
effects that are of interest in a longitudinal study, and in general
there are multiple valid contrasts that each test for different
effects. In the above example, it would be the combined effect of the
overall intercept and the time-from-baseline - I have to admit that I
do not know what the interpretation of that effect would be.
So the contrast specification really depends on which question you want
to ask to the data. In designs like yours, this could be the effect of
group, the effect of time, group differences in the effect of time, or
the effect of covariates. You'll then typically set the corresponding
entries in the contrast vector to either '1' or '-1', and zero
otherwise.
Hope this helps,
Kersten
Mayank
On Mar 7, 2018, at 3:33 PM, Diers, Kersten /DZNE <Kersten.Diers@dzne.
de<mailto:kersten.di...@dzne.de>> wrote:
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Hi,
let me just mention that my previous comments were directed at the
design matrix X and not necessarily at the QDEC table. In that sense,
it is possible, but not necessary to modify the QDEC table prior to
loading it into Matlab. Alternatively, one might do these
modifications
within Matlab, although this would require some additional manual
coding.
For the present analysis, however, the current qdec table looks fine.
About question 1:
If I understood correctly, you have so far loaded the (modified) QDEC
table and stored the extracted information within the matlab variable
'M'.
In addition to that you'd also need to sort the data within each
subject according to time, and load 'Y', 'lhcortex' and possibly
'lhsphere'. For a description of all of these steps please see the
"mass-univariate" section of the example data analysis on the
tutorial
page.
About question 2:
Just to be precise, I assume that for this particular case the matlab
variable 'M' contains the following four columns: time-from-baseline,
age-at-baseline, group2, group3; where 'group2' and 'group3' indicate
memberships in groups 2 and 3, respectively.
Then, a design matrix can be created as follows:
X = [ones(length(M),1) M M(:,1).*M(:,3) M(:,1).*M(:,4)];
Then the order of the columns of X is: intercept, time-from-baseline,
age-at-baseline, group2, group3, time-from-baseline*group2, time-
from-
baseline*group3.
Let's keep in mind that the order of columns in X is important for
the
formulation of contrasts at a later stage (and is different from the
tutorial example).
Again, this procedure and the matrices M and X are specific for this
particular analysis scenario and cannot necessarily be generalized to
others.
Best regards,
Kersten
-----Original Message-----
From: "Kaushal, Mayank"
<mkaus...@mcw.edu<mailto:mkaus...@mcw.edu><mailto:mkaus...@mcw.edu>>
Reply-to: Freesurfer support list
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To: Freesurfer support list
<freesurfer@nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>>
Subject: Re: [Freesurfer] Running Linear Mixed Effects (LME) Model
Date: Wed, 7 Mar 2018 15:48:36 +0100
I have made the suggested edits and would appreciate if you could
take
a look at the qdec.table.dat file attached to this conversation.
After going over the univariate example on the Freesurfer LME
webpage,
I had a couple of questions.
1. How will data be loaded into Matlab? More specifically, what would
be the step(s) after the line M = Qdec2num(Qdec);?
2. Given that column named time_base_scan would be multiplied with
column named group2 as well as group3, what will be the equation for
creating the design matrix?
X = [ones(length(M),1) M M(:,).*M(:,)];
Mayank
On Mar 7, 2018, at 7:14 AM, Diers, Kersten /DZNE <Kersten.Diers@dzne.
de> wrote:
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________________________________
Hi Mayank,
I may have a few more general considerations before commenting on the
number and kind of random effects.
In the current design matrix, column 2 (time from baseline) and
column
5 (visit) will be quite redundant. It is one of the advantages of the
LME model to be able to deal with unevenly spaced observation
intervals
and also with possibly missing visits. In that sense, all temporal
information is already contained within column 2, and I cannot see a
good reason to additionally include column 5. It may even complicate
things, due to the redundancy.
If you have 3 groups, you will need two columns in the design matrix
to
indicate group membership (assuming that an intercept column is also
present). Choose one group (say, group 1) as the reference group,
which
will not explicitly be coded, and then use one of the two group
columns
to indicate if a person belongs to group 2 (1=yes, 0=no), and the
other
to indicate if a person belongs to group 3 (1=yes, 0=no).
Please don't code group membership numerically as 1, 2, 3 in a single
column, as this will impose a linear relation between group
membership
and outcome measure (similar to linear regression).
I am not sure about the current meaning of column 6, which multiplies
age and time-from-baseline. It would be more important, in my eyes,
to
multiply time-from-baseline and group membership. Considering my
second
comment, this will also result in two colums. These will indicate
whether or not the effect of time will differ between groups, and
this
(=different longitudinal trajectory) is often the primary interest in
longitudinal studies.
As far as I can see, your overall design is somewhat similar to the
univariate example on the Freesurfer LME webpage, except that you
have
fewer groups and fewer covariates. As it does not matter for the
design
matrix whether or not the overall study is univariate or mass-
univariate (as in your case), one suggestion would be that you
construct your design in a similar way as in the given example.
I hope that I have understood your design correctly, otherwise let me
know.
Best regards,
Kersten
-----Original Message-----
From: "Kaushal, Mayank" <mkaus...@mcw.edu<mailto:mkaus...@mcw.edu>>
Reply-to: Freesurfer support list
<freesurfer@nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>>
To: Freesurfer support list
<freesurfer@nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>>
Subject: Re: [Freesurfer] Running Linear Mixed Effects (LME) Model
Date: Tue, 6 Mar 2018 19:22:57 +0100
I have tabulated the columns for easier comprehension.
Column 1
Column 2
Column 3
Column 4
Column 5
Column 6
intercept
time of each scan from base scan for a given subject
age at first scan for a given subject
group (1, 2, or 3)
visit (1,2 3, or 4)
Column 2 * Column 3
Thanks again.
Mayank
On Mar 6, 2018, at 12:05 PM, Kaushal, Mayank
<mkaus...@mcw.edu<mailto:mkaus...@mcw.edu><mailto
:m
kaus...@mcw.edu<mailto:kaus...@mcw.edu>>> wrote:
Changing the Matlab directory worked.
Thanks for all your inputs, Kersten.
Sorry to prolong the conversation but if possible, I would appreciate
input on whether my thought process regarding the LME analysis is on
the right track.
I am looking to compare cortical thickness between 3 groups over
multiple visits.
The design matrix created by me X = [ones(length(M),1) M
M(:,1).*M(:,2)];
Column 1 Column 2 Column 3 Column 4 Column 5 Column 6
intercept time of each scan from base scan age at first scan group
(1,
2, or 3) visit (1,2 3, or 4) Column 2 * Column 3
Presently, I am running lhstats = lme_mass_fit_vw(X,[1
2],Y,ni,lhcortex,[],1) as a test run.
However, I would like to include Columns 1, 4, and 5 as random
effects
for LME analysis and if possible even include Column 2 as a random
effect to have 4 random effects. lhstats =
lme_mass_fit_vw(X,[1
4 5],Y,ni,lhcortex,[],1) or lhstats = lme_mass_fit_vw(X,[1 2 4
5],Y,ni,lhcortex,[],1)
Now, I would like to know if having 3 or possibly 4 random effects
would be too many random effects.
Mayank
On Mar 6, 2018, at 8:57 AM, Diers, Kersten /DZNE <Kersten.Diers@dzne.
de
<mailto:kersten.di...@dzne.de><mailto:kersten.di...@dzne.de>> wrote:
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________________________________
The LME toolbox will create a text file ('parfor_progress1.txt') in
Matlab's present working directory to indicate its progress in
processing each vertex.
To create this file it needs to have write permissions in Matlab's
present working directory.
I speculate that your present working directory in Matlab is the root
directory '/' where the toolbox cannot create a file.
The solution would be to not run the scripts from '/', but to set the
Matlab working directory to another directory where you have write
permissions.
Best regards
Kersten
-----Original Message-----
From: "Kaushal, Mayank"
<mkaus...@mcw.edu<mailto:mkaus...@mcw.edu><mailto:mkaus...@mcw.edu><ma
il
to:mkaus...@mcw.edu<mailto:mkaus...@mcw.edu>>>
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Subject: Re: [Freesurfer] Running Linear Mixed Effects (LME) Model
Date: Tue, 6 Mar 2018 15:17:31 +0100
After downloading version 6.0. and running lhstats =
lme_mass_fit_vw(X,[1 2],Y,ni,lhcortex,[],1), I received the following
errors.
Error using parfor_progress (line 17)
Do you have write permissions for /?
Error in lme_mass_fit (line 155)
fn = parfor_progress('init',nv);
Error in lme_mass_fit_vw (line 73)
[stats1,st1] = lme_mass_fit(X,[],Xrows,Zcols,Y,ni,prs,e);
Thoughts?
Mayank
On Mar 6, 2018, at 7:08 AM, Diers, Kersten /DZNE <Kersten.Diers@dzne.
de> wrote:
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________________________________
Yes,
my understanding is the same.
The LME toolbox can in a way be regarded as a post-processing add-on
to
Freesurfer. The only interface with Freesurfer are the read/write
functions, everything else is to the best of my knowledge independent
of Freesurfer. So I cannot see any problems in using the newer
version
of the LME toolbox with data from Freesurfer 5.3.
Needless to say, this does not affect the usual recommendation, which
is that Freesurfer versions should not be mixed in general.
Best,
Kersten
------Original Message-----
From: "Kaushal, Mayank"
<mkaus...@mcw.edu<mailto:mkaus...@mcw.edu><mailto:mkaus...@mcw.edu>>
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Subject: Re: [Freesurfer] Running Linear Mixed Effects (LME) Model
Date: Mon, 5 Mar 2018 20:47:36 +0100
The version string I have been using:
$Revision: 1.1.1.1 $ $Date: 2012/02/02 11:25:52 $
So, it appears that I do indeed have the older version.
I am running Matlab 2017a, which is a relatively newer version.
Before I proceed with using LME toolbox from a freesurfer 6.0, I
would
appreciate your input on the following.
My understanding is that LME toolbox is used for statistical analysis
and is independent of the freesurfer version performing the actual
cortical thickness analysis. So, in theory, if I import cortical
thickness data processed in version 5.3, I should still be able to
run
the LME toolbox from a version 6.0.
So, can I input the files processed by freesurfer 5.3 (files that
have
been processed till mris_preproc and mri_surf2surf) and simply use
the
LME toolbox from a version 6.0 to perform the statistical analysis?
Mayank
On Mar 5, 2018, at 1:29 PM, Diers, Kersten /DZNE <Kersten.Diers@dzn
e.
de> wrote:
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________________________________
Hi,
thanks for the additional information.
I believe that we are dealing with a version issue now.
Specifically, I have so far been testing with an LME toolbox from a
Freesurfer 6.0 version on Matlab R2016a. In that sense, there is no
need for a separate download of the LME scripts, since they are
already
included within the freesurfer directory (e.g.
'freesurfer/matlab/lme').
It is primarily the 'lme_mass_fit.m' script, which throws the
current
error. Could you please check your version of this script (type
'help
lme_mass_fit.m')?
Within my 5.3 installation, the version string is:
% $Revision: 1.1.2.2 $ $Date: 2013/02/23 21:08:10 $
Within my 6.0 installation, the version string is:
$Revision: 1.3 $ $Date: 2016/04/08 19:39:24 $
Unfortunately, the version string of the 'lme_mass_fit.m' script
within
a toolbox that I just downloaded from the LME website is the most
ancient one:
% $Revision: 1.1.1.1 $ $Date: 2012/02/02 11:25:52 $
When I use the 1.1.1.1 version instead of the one that is included
within Freesurfer 6.0, I can replicate the problem reported in your
current mail. With the 1.3 version I have no issues.
As far as I know, the 'lme_mass_fit.m' script was updated to
accommodate the changes in Matlab's way of doing the parallel
processing. In older Matlab versions this was done using the
'matlabpool' command, but this was replaced by 'parpool', which is
-
to
my knowledge - the only way of running parallel processing with
recent
Matlab versions. The LME toolbox was originally only tailored to
the
'matlabpool' command, but has later been adapted to also include
the
'parpool' command.
Assuming that you are using a relatively recent Matlab version, the
use
of earlier vs. later versions of the toolbox would explain our
respective observations.
All in all, I currently see no way of getting an older version of
the
LME toolbox to run with a current Matlab version - except from
modifying the code itself. So my suggestion would be to just use
the
newer LME toolbox that is shipped with Freesurfer 6.0.
Let us know if I am mistaken or if there are further issues.
Best regards,
Kersten
-----Original Message-----
From: "Kaushal, Mayank"
<mkaus...@mcw.edu<mailto:mkaus...@mcw.edu><mailto:mkaus...@mcw.edu>>
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Subject: Re: [Freesurfer] Running Linear Mixed Effects (LME) Model
Date: Mon, 5 Mar 2018 16:34:14 +0100
Thanks for the quick response Kersten.
I tried all the potential suggestions mentioned in your post but
still
no luck.
Here’s the summary:
lhstats = lme_mass_fit_vw(X,[1 2],Y,ni,lhcortex,[],1)
Undefined function or variable 'matlabpool'.
Error in lme_mass_fit (line 124)
if (matlabpool('size') > 0)
Error in lme_mass_fit_vw (line 73)
[stats1,st1] = lme_mass_fit(X,[],Xrows,Zcols,Y,ni,prs,e);
lhstats = lme_mass_fit_vw(X,[1 2],Y,ni,lhcortex,[],2)
Undefined function or variable 'matlabpool'.
Error in lme_mass_fit (line 123)
if (prs==1) || (matlabpool('size') ~= prs)
Error in lme_mass_fit_vw (line 73)
[stats1,st1] = lme_mass_fit(X,[],Xrows,Zcols,Y,ni,prs,e);
The "automatically create a parallel pool" was already deselected
in
my
case.
Also, I am using freesurfer 5.3 with LME toolbox downloaded from
the
freesurfer website.
Mayank
On Mar 5, 2018, at 9:12 AM, Diers, Kersten /DZNE <Kersten.Diers@d
zn
e.
de> wrote:
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Use
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________________________________
Hi,
you might try supplying an additional command-line argument while
calling the 'lme_mass_fit_vw' function:
lhstats = lme_mass_fit_vw(X,[1 2],Y,ni,lhcortex,[],1)
This will tell Matlab to use only one worker. Further
documentation
on
this additional argument can be obtained by typing 'help
lme_mass_fit_vw' at the Matlab prompt.
To get this working with my Matlab installation, I additionally
had
to deselect the "Automatically create parallel pool (if one
doesn't
already exist) when parallel keywords (e.g., parfor) are
executed."
option. This can be found in Matlab's preferences menu (--> see
the
Parallel Computing toolbox section).
As an alternative, using two workers might also be an option in
your
case:
lhstats = lme_mass_fit_vw(X,[1 2],Y,ni,lhcortex,[],2)
To get this alternative approach working, I did not have to
change
the
Matlab preferences.
Hope this helps,
Kersten
-----Original Message-----
From: "Kaushal, Mayank"
<mkaus...@mcw.edu<mailto:mkaus...@mcw.edu><mailto:mkaus...@mcw.edu>>
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To:
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ed
u>
<freesur...@nmr.mgh.harvard.ed<mailto:freesur...@nmr.mgh.harvard.ed><mailto:freesur...@nmr.mgh.harvard.e
d>
u>
Subject: [Freesurfer] Running Linear Mixed Effects (LME) Model
Date: Sun, 4 Mar 2018 23:56:03 +0100
Dear Freesurfer Community,
Running lhstats = lme_mass_fit_vw(X,[1 2],Y,ni,lhcortex); gives
the
following error,
Starting parallel pool (parpool) using the 'local' profile ...
Error using parpool (line 104)
You requested a minimum of 8 workers, but the cluster "local" has
the
NumWorkers property set to allow a maximum of 2 workers. To run a
communicating job on more workers than this (up to a maximum of
512
for
the Local cluster), increase the value of the NumWorkers
property for the cluster. The default value of NumWorkers for a
Local
cluster is the number of cores on the local machine.
Error in lme_mass_fit (line 137)
parpool(prs);
Error in lme_mass_fit_vw (line 73)
[stats1,st1] = lme_mass_fit(X,[],Xrows,Zcols,Y,ni,prs,e);
I am running Matlab on a personal MacBook Pro, 2.4 GHz Intel Core
i5,
8
GB 1600 MHz DDR3.
I have researched the forum on how to stop the function from
trying
to
initiate parallel processing but so far haven’t been able figure
out
how to overcome this.
Any help would be much appreciated.
Mayank Kaushal MD MBA
Postdoctoral Fellow
Department of Neurosurgery
Medical College of Wisconsin
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