Hi Jon
We recommend toorder the columns of your design matrix in the following way:
First,the intercept term (which is a column of ones); second, the
timecovariate; third, any time-varying covariates (eg. cannabis use);fourth,
the group covariates of interest (eg. a binary variableindicating whether the
subject is a patient or control) and theirinteractions with the time-varying
covariates; finally any othernuisance time-invariant covariate (eg. gender). So
your designmatrix is comprised by the following columns:
1. Intercept (acolumn of ones)2. Time sincebaseline3. cannabis use(time-varying
if varies over time for each subject during thefollow-up time)4. alcohol use
(time-varying if varies over time for each subject during the follow-uptime)5.
drug use overtime (time-varying if varies over time for each subject during
thefollow-up time)6. gender7. age at baseline
There is no GUI forsetting up the models. Here is an outline of the basic steps
(with only three time points you shouldn't need more than two random effects):
1-Read your labeleg.: lhcortex =
fs_read_label('freesurfer/subjects/fsaverage/label/lh.cortex.label');
2-Read the data file eg.:
[lhY, lhmri] = fs_read_Y('lh.thickness.mgh');
3-Fit a vertex-wise lme model with two random effects for the intercept term
and time eg.: lhstats1 = lme_mass_fit_vw(X, [1 2], lhY, ni, lhcortex);
4-Fita vertex-wise lme model with two random effects for the interceptterm and
cannabis use eg.:lhstats2 = lme_mass_fit_vw(X, [1 3], lhY, ni, lhcortex);
And so on withother time-variying covariates...
Now see which modelfit produces the best lreml values across vertices in
general and then:
4-Performvertex-wise inferences using the winner model eg.: CM.C = [your
contrast matrix];F_lhstats = lme_mass_F(lhstats_winner, CM);
5-Saveresults eg.:
fs_write_fstats(F_lhstats, lhmri,' sig.mgh', 'sig');
-Jorge
De: Jon Alan Wieser <wie...@uwm.edu>
Para: jorge luis <jbernal0...@yahoo.es>; Freesurfer support list
<freesurfer@nmr.mgh.harvard.edu>
CC: Krista Lisdahl Medina <krista.med...@gmail.com>;
"alicia.thomas....@gmail.com" <alicia.thomas....@gmail.com>
Enviado: Martes 16 de diciembre de 2014 15:24
Asunto: Re: [Freesurfer] longitudinal statistics LGI
#yiv6533942457 #yiv6533942457 -- p
{margin-top:0px;margin-bottom:0px;}#yiv6533942457 p
{margin-bottom:0.1in;line-height:120%;}#yiv6533942457 p
{margin-top:0px;margin-bottom:0px;}#yiv6533942457 Jorge, We are interested in
examining the impact of cannabis exposure (time-varying continuous variable) on
local gyrification index over 3 time points (baseline, 18 month, 36 month)- so
this is a time-varying random effect. I apologize in advance if these are
student questions… we are novices here… From what you said previously, we
would want to model intercept+time, vs intercept+cannabis use, vs
intercept+time+ cannabis use. Vs. intercept+time+ cannabis use.+covariates
(alcohol use over time, gender, age, drug use over time). We are trying to
figure out how to do this in Freesurfer/Matlab using the Wiki
(https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels). There is
a great example in there with AD/MCI groups, but none outlining an example
focused on time-varying continuous variables. So with OUR question, would we
organize the data as follows:InterceptTimeCannabis total (changes over
time)Alcohol total (changes over time)Other Drug total (changes over
time)GenderAge at baseline So, a few questions:1) We need help writing the
syntax for Matlab for testing the models (assuming linear trend was
significant):a. intercept+time, vs b. intercept+cannabis use, vs c.
intercept+time+ cannabis use. Vs. d. intercept+time+ cannabis
use.+covariates (alcohol use over time, gender, age, drug use over time)…
i. For example, this is the linear
model for cortical thickness over time for the groups example: Yij = ß1 +
ß2*tij + ß3*t²ij + ß4*sMCIi + ß5*sMCIi*tij + ß6*sMCIi*t²ij + ß7*cMCIi +
ß8*cMCIi*tij + ß9*cMCIi*t²ij + ß10*ADi + ß11*ADi*tij + ß12*ADi*t²ij + ß13*E4i +
ß14*E4i*tij + ß15*Genderi + ß16*BslAgei + ß17*Educationi + b1i + b2i*tij+ eij
ii. This is the design
matrix:[lhTh0,lhRe] = lme_mass_fit_EMinit(X,[1 2],Y,ni,lhcortex,3);
2) Is there a GUI available for setting up these models? (We are assuming
there isn’t and that it is all matlab based.)3) Once we test these models,
is it correct that we open the spheres representing the liklihood ratio test
results (corrected for multiple comparisons) and pick the “best” model based on
the greatest #/size of significant clusters? Jon
Jon Wieser
Research Specialist
UW-Milwaukee
Psychology Department, Pearse Hall Rm 375
2441 East Hartford Ave
Milwaukee, WI 53211
Phone: 414-229-7145
Fax: 414-229-5219
From: freesurfer-boun...@nmr.mgh.harvard.edu
<freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of jorge luis
<jbernal0...@yahoo.es>
Sent: Tuesday, December 2, 2014 12:34 PM
To: Freesurfer support list
Subject: Re: [Freesurfer] longitudinal statistics LGI #yiv6533942457
#yiv6533942457 --p {margin-bottom:0.1in;line-height:120%;}#yiv6533942457 Hi Jon
I guess that when you say “we have continuous data as to the amount of drug
usage” you actually mean that the amount-of-drug-usage is a continuous variable
that changes over time for each subject. So yes you can keep this variable as a
continuous variable. In fact it can even be a random effect in your statistical
model. You will need to select the model with the best combination of random
effects : intercept+time vs intercept+amount-of-drug-usage vs
intercept+time+amount-of-drug-usage.
Actually one nice feature of the LME model implemented in freesurfer vs
commonly used two-levels random effects models in neuroimaging is that you can
include this type of longitudinal continuous variables in the model for the
mean without requiring it be included in the model for the covariance (i.e
included as a random effect). You just select the best subset of random effects
as explained above.
-Jorge
De: Jon Alan Wieser <wie...@uwm.edu>
Para: "freesurfer (freesurfer@nmr.mgh.harvard.edu)"
<freesurfer@nmr.mgh.harvard.edu>
Enviado: Martes 2 de diciembre de 2014 12:29
Asunto: [Freesurfer] longitudinal statistics LGI
#yiv6533942457 #yiv6533942457 -- p
{margin-top:0px;margin-bottom:0px;}#yiv6533942457 HI freesurfer experts
I have a question about the statistical analysis of longitudinal data. we have
run our data through the longtudinal data processing stream.
We are looking at the longitudinal effect on the LGI data
We are looking at doing a Mixed effects analysis. our main Model Factor
(Independent Variable) of interest is drug usage. we have continuous data as
to the amount of drug usage. Can this variable be continous variable, or do we
have to break it up into discrete levels of usage ( example, low, middle,
high)
Thanks
Jon
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contains patient information, please contact the Partners Compliance HelpLine at
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