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I'm glad that cleared things up a little. Sorry about the confusion,
we will try to include warnings for deprecated function in the next
release of TRENTOOL.<br>
<br>
You don't need to run TEprepare before running TEgroup_prepare. You
just run the four functions <br>
- TEgroup_prepare<br>
- InteractionDelayReconstruction_calculate <br>
- InteractionDelayReconstruction_analyze <br>
- TEgroup_stats <br>
in that order. <br>
<br>
The graph analysis is an optional step that can be run on the
individual outputs of InteractionDelayReconstruction_analyze. The
function will partially correct the output for multivariate effects.
Since you are looking at one connection only, you don't need to
include the graph analysis step into your pipeline. (I also sent you
an example script directly, because I think it is not a good idea to
send attachments via the mailing list.)<br>
<br>
Best, Patricia<br>
<br>
<br>
<br>
<div class="moz-cite-prefix">On 09/09/2014 03:19 PM, Max Cantor
wrote:<br>
</div>
<blockquote
cite="mid:CAFTjRaUXzwca7rR3sm8iPLLyUxqHO9kkm++=zAU4LUW=r7NRBw@mail.gmail.com"
type="cite">
<div dir="ltr">
<div>
<div>This is immensely helpful, thank you. I was very confused
about why some versions of the pipeline I saw were using
group calculate and others were using interaction delay
reconstruction and what that meant, and I think I have a
more clear idea of what the different steps of the pipeline
are doing. There are still a few things I'm a bit confused
about though in terms of the pipeline. For instance, whether
or not I need to do TEprepare before group prepare, and if I
need to do graph analysis (which I'm not sure I fully
understand but also haven't looked deeply into) before group
stats. <br>
<br>
</div>
If you don't mind me taking you up on your offer, I think
seeing your example script might help clarify some of these
issues.<br>
<br>
</div>
Thank you!<br>
</div>
<div class="gmail_extra"><br>
<div class="gmail_quote">On Tue, Sep 9, 2014 at 8:16 AM,
Patricia Wollstadt <span dir="ltr"><<a
moz-do-not-send="true"
href="mailto:Patricia.Wollstadt@gmx.de" target="_blank">Patricia.Wollstadt@gmx.de</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div bgcolor="#FFFFFF" text="#000000"> Hello Max,<br>
<br>
I added a few comments to the questions regarding
individual parameters below. To address the general
problem of TRENTOOL telling you, that there are not enough
sample points in your data: From what I can see in your
script, you probably don't have enough data points in each
time series to robustly estimate TE. You analyze 800 ms of
data sampled at 300 Hz, which gives you 240 samples per
time series. Can you maybe avoid downsampling to 300 Hz
and downsample to 600 Hz instead? Or could you analyze a
longer time window of interest?<br>
Note that you also 'lose' data to embedding and the
interaction delay: The first point that can be used for TE
estimation is at max. embedding length + max. interaction
delay in samples. For example: max. embedding length = dim
* tau_factor * ACT = 10 * 0.4 * 5 = 20 samples plus the
max interaction delay of 30 ms = 9 samples. In this
example, you would be left with 240 - 29 samples for TE
estimation per trial. There is also the possibility to
estimate time resolved TE/TE for shorter time windows of
interest (see section 4.4 in the manual); however, this
method requires the use of a GPU for TE estimation.<br>
<br>
I would further recommend to use the new pipeline for
group statistics described in the manual in section 4.5
(the function 'TEgroup_calculate' is deprecated). The new
pipeline allows you to reconstruct the interaction delay
and uses the following functions (see also comments in the
script):<br>
<br>
TEgroup_prepare -> prepares all data sets (all
subjects/all conditions) for group analysis (this means
finding common embedding parameters such that estimates
are not biased between groups)<br>
InteractionDelayReconstruction_calculate ->
estimates TE for individual data sets and all assumed
interaction delays u<br>
InteractionDelayReconstruction_analyze ->
reconstructs the interaction delay by selecting the u that
maximizes TE for each channel<br>
TEgroup_stats -> calculate group statistics
using a permutation test<br>
<br>
I can send you an example script for group TE analysis
using this pipeline to get you started. I hope this helps
you to get the group analysis running. Just write again if
you're having trouble setting up the pipeline or something
is not clear about the parameters/my comments.<br>
<br>
Best,<br>
Patricia<span class=""><br>
<br>
<br>
<br>
<br>
On 09/04/2014 08:30 PM, Max Cantor wrote:<br>
<blockquote type="cite">
<div dir="ltr">
<div>
<div>
<div>Hi fieldtrippers,<br>
<br>
</div>
I know trentool is not produced by the Donders
Institute, so I'm not 100% sure if it is
appropriate to ask questions about it here, but
to the best of my knowledge they do not have a
mailing list and I saw a few trentool questions
in the archives, so I'm going to assume it's
ok...<br>
<br>
</div>
In any case, below is my current pipeline
(slightly modified for comprehensibility):<br>
<br>
</div>
(notes in bold are comments/questions made in this
email, not present in the pipeline. Sorry in advance
for the long post! Any help would be greatly
appreciated as I'm a bit over my head on this but I
think I'm close!)<br>
<div><br>
*****<br>
<br>
% Prepare group TE data<br>
<br>
cfgP = [];<br>
cfgP.Path2TSTOOL = <b>TSTOOLPATH</b> <br>
cfgP.TEcalctype = 'VW_ds';<br>
cfgP.channel = {'ctfdip_LAC'
'ctfdip_RAC'}; <br>
<br>
<b>I'm trying to find the transfer entropy between
the left and right auditory cortices in my
experiment. The input is virtual sensor data
that was produced using SAM in fieldtrip on real
MEG data. </b><br>
<br>
% specify u to be scanned <br>
<br>
cfgP.predicttime_u = 30; <br>
cfgP.toi = [-0.4 0.4]; <br>
<br>
</div>
</div>
</blockquote>
</span>
<blockquote type="cite">
<div dir="ltr">
<div><b>For clarification, the predicttime_u is in
seconds but the toi is in milliseconds. If I
understand correctly, the predicttime_u must fit
within the toi, but beyond that are there any
benefits to it being earlier or later?</b> PW: The
predictiontime_u is in milliseconds and the toi is
in seconds. The prediction time is the assumed
interaction delay between your two sources and
should fit within your toi. In general it is
preferable to use the method for interaction delay
reconstruction for TE estimation, because it allows
you to reconstruct the actual delay between your
source and target times series. A non-optimal
u/interaction delay may cause an underestimation of
TE, so it is recommended to use the pipeline for
interaction delay reconstruction whenever estimating
TE for unknown delays. <br>
If you use the methods for interaction delay
reconstruction 'predicttime_u' is replaced by<br>
cfgTEP.predicttimemin_u % minimum u to be scanned<br>
cfgTEP.predicttimemax_u % maximum u to be scanned<br>
cfgTEP.predicttimestepsize % time steps between u to
be scanned <br>
A large range for u values to be scanned increases
computing time a lot, so it is best to limit the u
range to values that are physiologically plausible.
<br>
<br>
</div>
</div>
</blockquote>
<blockquote type="cite">
<div dir="ltr"><span class="">
<div>% ACT (Autocorrelation Time) estimation and
constraints<br>
<br>
cfgP.maxlag = 150; <br>
cfgP.actthrvalue = 7.5; <br>
cfgP.minnrtrials = 5; <br>
<br>
</div>
</span>
<div><b>My understanding is maxlag should be 1/2 the
sampling rate, so since the data are downsampled
to 300hz, it should be 150. I know that the sample
rate and filters are used to determine the
actthrvalue, but I don't actually know the
calculation. 7.5 was a rough guess just to test
the pipeline. I'm also uncertain of what
minnrtrials should be.</b> PW: You can set the
actthrvalue based on the filtering you did prior to
TE analysis. If you for example highpass filtered at
10 Hz, you shouldn't find an ACT higher than 30
samples, because you filtered out any components of
the signal slower than 10 Hz/30 samples (given your
sampling frequency of 300 Hz). So in this scenario
the actthrvalue would be 30.<br>
A good value for cfgP.minnrtrials is 12 (a minimum
number of trials is needed to realize the
permutation test for estimated TE values). </div>
</div>
</blockquote>
<blockquote type="cite">
<div dir="ltr"><span class="">
<div> </div>
<div> <br>
% Optimization<br>
<br>
cfgP.optimizemethod = 'ragwitz';<br>
cfgP.ragdim = 4:8;<br>
cfgP.ragtaurange = [0.2 0.4];<br>
cfgP.ragtausteps = 15;<br>
cfgP.repPred = 100;<br>
<b><br>
</b></div>
</span>
<div><b>I am completely at a loss for this. I've done
some reading into transfer entropy, mutual
information, etc., cited in trentool, but I'm yet
to understand how exactly this optimization works
and what the configuration should be, given my
data and experimental intentions.</b> PW: The
Ragwitz criterion tries to find optimal embedding
parameters dim and tau for the data. To do that, the
method iteratively takes all possible combinations
of dim and tau values that are provided in
cfgP.ragdim and cfgP.ragtaurange/.ragtausteps and
tests how well these combinations embed the data. To
test an embedding, the method builds the embedding
vectors from the data; it then tests for each point
how well the next point in time can be predicted
from the reference point's nearest neighbours. So
for each embedded point, the method searches for the
nearest neighbours and calculates the average of
those nearest neighbours. The difference between the
averaged/predicted point and the actual next point
is the error of the local predictor. The Ragwitz
criterion will then return the parameter combination
for which this error over all points is minimal. <br>
The parameters set the following: 'ragdim' are
dimensions to be tested by the method (I would
reccomend to start with 2:10), 'ragtaurange'
together with 'ragtausteps' specifies the tau values
to be tested (TRENTOOL will build a vector from 0.2
to 0.4 in 15 steps). Note, that the values here are
factors that are later multiplied with the ACT to
obtain the actual tau. 'repPred' is the number of
points that will be used for the local prediction,
i.e. the Ragwitz criterion will test the local
prediction and calculate the error for the first 100
points in your time series. The two parameters
'flagNei' ans 'sizeNei' below specify the type of
neighbour search conducted by the Ragwitz criterion:
'flagNei' tells the method to either conduct a kNN
or range search; 'sizeNei' specifies the number of
neighbours or the radius to be searched by a range
search.<br>
<br>
</div>
</div>
</blockquote>
<blockquote type="cite">
<div>
<div class="h5">
<div dir="ltr">
<div> <br>
% Kernel-based TE estimation<br>
<br>
cfgP.flagNei = 'Mass';<br>
cfgP.sizeNei = 4; % Default<br>
<br>
cfgP.ensemblemethod = 'no';<br>
cfgP.outputpath = <b>OUTPUT PATH</b>;
<br>
<br>
if ~exist(<b>Path for TEprepare data object</b>)<br>
load VSdat;<br>
TE_Wrd = {}; <br>
for i = 1:nConds<br>
for j = 1:Nsub<br>
TE_Wrd{i}{j} =
TEprepare(cfgP, VSdat{i}{j});<br>
end<br>
end<br>
clear VSdat;<br>
save('TE_Wrd', 'TE_Wrd');<br>
end<br>
<b><br>
</b></div>
<div><b>The configuration and virtual sensor data,
organized in a 3 x 15 cell of structures
(condition by subject) are the input. The
TEprepare substructure is added to each
individual condition x subject .mat files'
data structure which are stored on disk
independently.</b> <br>
</div>
<div><br>
% Use object_to_mat_conversion.m to replace
individual condition x subject virtual sensor
data<br>
% .mat files with their TE_Wrd equivalent<br>
<b><br>
</b></div>
<div><b>I'm using a separate script to make some
manipulations to the objects from disk; this
will all eventually be integrated into the
main pipeline</b>.<b> TRENTOOL seems to handle
data output very differently from fieldtrip
and I've had trouble thinking through the most
logical way to handle the data so it's a bit
haphazard right now.</b><br>
</div>
<div><br>
load cond080sub01.mat<br>
<br>
cfgG = [];<br>
cfgG.dim =
cond080sub01.TEprepare.optdim;<br>
cfgG.tau =
cond080sub01.TEprepare.opttau;<br>
<br>
if isfield(cond080sub01, 'TEprepare')<br>
TEgroup_prepare(cfgG, fileCell);<br>
else<br>
error('Need to run TEprepare before
TEgroup_prepare');<br>
end<br>
<b><br>
</b></div>
<div><b>For clarification, fileCell is a cell with
the name of each condition x subject .mat
file, which as I said before is collectively
the same as the 3 x 15 VSdat structure
(condition x subject).</b><br>
</div>
<div><br>
% Replace .mat files with
'_for_TEgroup_calculate' version in<br>
% object_to_mat_conversion.m<br>
<br>
% TE Group Calculate<br>
<br>
load cond080sub01.mat<br>
if isfield(cond080sub01, 'TEgroupprepare')<br>
for i = 1:length(fileCell)<br>
TEgroup_calculate(fileCell{i});<br>
end<br>
else<br>
error('Need to run TEgroup_prepare before
TEgroup_calculate');<br>
end<br>
<b><br>
</b></div>
<div><b>At this step I get the following error:<br>
<br>
Error using transferentropy (line 337)<br>
\nTRENTOOL ERROR: not enough data points left
after embedding<br>
<br>
Error in TEgroup_calculate (line 133)<br>
[TEresult] = transferentropy(cfg,data);</b><br>
</div>
<div><br>
% TE Group Stats<br>
<br>
cfgGSTAT = [];<br>
cfgGSTAT.design(1,1:2*Nsub) = [ones(1,Nsub)
2*ones(1,Nsub)];<br>
cfgGSTAT.design(2,1:2*Nsub) = [1:Nsub 1:Nsub];<br>
<br>
cfgGSTAT.uvar = 1;<br>
cfgGSTAT.ivar = 2;<br>
cfgGSTAT.fileidout =
'test_groupstats';<br>
<br>
TEgroup_stats(cfgGSTAT, fileCell);<br>
<br>
</div>
<div><b>Given the error above, I am yet to get to
this step, but it does not seem fundamentally
different from normal fieldtrip stats.</b><br
clear="all">
</div>
<div>
<div>
<div>
<div><br>
*****<br>
<br>
</div>
<div>In case my notes were not clear or you
skipped to the bottom, <b>my primary
concern is whether the error I'm getting
in TEgroup_calculate is a pipeline issue</b>
(I noticed the example pipeline in
trentool, the manual, and published
methods articles all seem to have slightly
or significantly different pipeline
compositions), <b>or if the error is</b>
due to ACT, ragwitz optimization, or some
other faulty parameterization <b>on my
part due to a lack of understanding of
how transfer entropy works on a more
theoretical/mathematical level</b>. If
the latter is the case, is there any
relatively straightforward way to
conceptualize this, or is this something
where I'm just going to have to keep
reading and rereading until it eventually
makes sense? I've already done quite a bit
of that and it hasn't pierced my thick
skull yet but I'm sure it will eventually!<br>
<br>
</div>
<div>Thank you so much,<br>
<br>
</div>
<div>Max Cantor<br>
</div>
<div><br>
<br>
-- <br>
<div dir="ltr">Max Cantor
<div>Lab Manager</div>
<div>Computational Neurolinguistics Lab</div>
<div>University of Michigan</div>
</div>
</div>
</div>
</div>
</div>
</div>
<br>
<fieldset></fieldset>
<br>
</div>
</div>
<pre>_______________________________________________
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<span class="HOEnZb"><font color="#888888"> </font></span></blockquote>
<span class="HOEnZb"><font color="#888888"> <br>
<div>-- <br>
<address><font color="#666666"><small><font
face="Helvetica, Arial, sans-serif">------------------------------------------------------------<br>
<br>
</font></small></font></address>
<address><font color="#666666"><small><font
face="Helvetica, Arial, sans-serif">Patricia
Wollstadt, PhD Student<br>
<br>
</font></small></font></address>
<address><font color="#666666"><small> </small></font></address>
<address><font color="#666666"><small><font
face="Helvetica, Arial, sans-serif"> MEG
Unit, Brain Imaging Center</font></small></font></address>
<address><font color="#666666"><small><font
face="Helvetica, Arial, sans-serif"> Goethe
University, Frankfurt, Germany<br>
<br>
</font></small></font></address>
<address><font color="#666666"><small><font
face="Helvetica, Arial, sans-serif">Heinrich
Hoffmann Strasse 10, Haus 93 B</font></small></font></address>
<address><font color="#666666"><small><font
face="Helvetica, Arial, sans-serif">D -
60528 Frankfurt am Main</font></small></font></address>
</div>
</font></span></div>
<br>
_______________________________________________<br>
fieldtrip mailing list<br>
<a moz-do-not-send="true"
href="mailto:fieldtrip@donders.ru.nl">fieldtrip@donders.ru.nl</a><br>
<a moz-do-not-send="true"
href="http://mailman.science.ru.nl/mailman/listinfo/fieldtrip"
target="_blank">http://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a><br>
</blockquote>
</div>
<br>
<br clear="all">
<br>
-- <br>
<div dir="ltr">Max Cantor
<div>Lab Manager</div>
<div>Computational Neurolinguistics Lab</div>
<div>University of Michigan</div>
</div>
</div>
<br>
<fieldset class="mimeAttachmentHeader"></fieldset>
<br>
<pre wrap="">_______________________________________________
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<address><font color="#666666"><small><font face="Helvetica,
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<address><font color="#666666"><small><font face="Helvetica,
Arial, sans-serif">Patricia Wollstadt, PhD Student<br>
<br>
</font></small></font></address>
<address><font color="#666666"><small> </small></font></address>
<address><font color="#666666"><small><font face="Helvetica,
Arial, sans-serif"> MEG Unit, Brain Imaging Center</font></small></font></address>
<address><font color="#666666"><small><font face="Helvetica,
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