Re: [HCP-Users] Structural space diffusion data

[Glasser, Matthew]

That’s right and that transformation is already provided 
${StudyFolder}/${Subject}/MNINonLinear/xfms/acpc_dc2standard.nii.gz.

Peace,

Matt.

From: 
>
 on behalf of Xinyang Liu >
Date: Monday, November 20, 2017 at 9:33 PM
To: HCP 讨论组 
>
Subject: [HCP-Users] Structural space diffusion data

Dear HCP experts,

When reading tutorials about the HCP preprocessing pipeline, I noticed that 
there is a registration processing of diffusion data to the 1.25 mm structural 
space. So does that mean all the diffusion data are already in the structural 
space now after preprocessing, together with the 
T1w_acpc_dc_restore_1.25.nii.gz native structural image? And if so, does that 
mean if we want to further do data registration to standard templates, we only 
need to transform between structural and standard space? Is my understanding 
correct?
Any guidance would be very appreciated. Thanks.

Best regards,
Xinyang Liu






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[HCP-Users] Structural space diffusion data

[Xinyang Liu]

Dear HCP experts,


When reading tutorials about the HCP preprocessing pipeline, I noticed that 
there is a registration processing of diffusion data to the 1.25 mm structural 
space. So does that mean all the diffusion data are already in the structural 
space now after preprocessing, together with the 
T1w_acpc_dc_restore_1.25.nii.gz native structural image? And if so, does that 
mean if we want to further do data registration to standard templates, we only 
need to transform between structural and standard space? Is my understanding 
correct? 
Any guidance would be very appreciated. Thanks.


Best regards,
Xinyang Liu


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Re: [HCP-Users] R: MEG resting state

[Georgios Michalareas]

Hi Adoney (and Francesco),

Francesco is right. The ICA was done independently per session. But this 
should not cause a problem to your analysis if you are using the 
preprocessed resting state data i.e.


177746_MEG_3-Restin_rmegpreproc.mat

177746_MEG_4-Restin_rmegpreproc.mat

177746_MEG_5-Restin_rmegpreproc.mat

Because in these files only the independent components corresponding to 
eye movements and heart artifacts have been removed. This artifacts have 
quite standard topology, quite similar across the different scans.
So under the assumption that there are small head movements between 
scans , if you concatanate the datasets and then you do ICA then you 
should be able to capture the same brain Independent components across 
all three scans. The effect of the removed eye and heart artifacts from 
each scan separately on the ICA on the concatenated data should be 
negligible as long as the head movement is small.


I hope this helps
Best
Giorgos


On 11/20/2017 2:09 PM, Francesco Di Pompeo wrote:


Hi,

ICA has been performed on the three sessions separately so I think 
it’s better to use a different strategy.


Best,

Francesco

*Da:*hcp-users-boun...@humanconnectome.org 
[mailto:hcp-users-boun...@humanconnectome.org] *Per conto di 
*Michalareas, Georgios

*Inviato:* venerdì 17 novembre 2017 14:15
*A:* A Nunes ; HCP-Users@humanconnectome.org
*Oggetto:* Re: [HCP-Users] MEG resting state

Hi Adoney,

The released data has been only 7.5 mm maximum movement within scan.

So the sensor positions should be quite close in all three resting 
state scans.


ICA was done with all three sessions concatenated together.

So I believe it should be one to concatenate them for your analysis 
too and assume sensors are more or less on same location.


If you want to treat each different scan separately then you have to 
follow different strategies like:


-Perform beamforming for each session separately and project each 
session separately into source space and then concatenate data in 
source space and do further analysis there.


-Make a virtual gradiometer array as the mean of the three gradiometer 
positions in the three scans and then interpolate the data of each 
session to this virtual MEG sensors using Fieltrip’s 
function ft_megrealign and then combine all data in this virtual 
sensor space. Keep in mind that this projects the data into source 
space and projects it back to the virtual sensor space.


I hope this helps

Best

Giorgos

*From: *> on behalf of A Nunes 
>

*Date: *Thursday, 16. November 2017 at 21:56
*To: *"hcp-users@humanconnectome.org 
" >

*Subject: *[HCP-Users] MEG resting state

Hi,

The MEG resting state data is split in three sessions, is it possible 
to append the data before computing the covariance matrix?


I have some doubts because the sensor position might change between 
the recordings and if ICA was done separately, then the rank would 
change between sessions and I don't know how would this affect 
beamforming.


Any suggestions?

Thanks

Adonay

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Neuroscience Department
Max Planck Institute for Empirical Aesthetics

email: g...@aesthetics.mpg.de
phone: +49 69 8300479-325



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Re: [HCP-Users] MEG resting state

[Georgios Michalareas]

Hi Adoney,

you can just get the channel positions of the same sensors in order to 
compare them.


I am pasting here some code that loads the channel positions from the 
three different resting state scans in a common order and plots the 
histogram of the position differences between runs 1-2,1-3 and 2-3.


At first it does this for all the sensors including the reference sensors.

Then it does the same only for the MEG sensors , with the reference 
sensors removed, because the reference sensors are far from the  head 
and a small head movement can be translated into larger position 
differences further away from the head as compared to the MEG sensors 
which are near the head.


In the code below you need to change the data directory and Fieltrip 
directory to what you have.


I hope it helps

Best

Giorgos

%-
%fieldtripdir='/home/michalareasg/toolboxes/matlab/fieldtrip/fieldtrip_svn';
fieldtripdir='/home/georgios.michalareas/workspace/toolboxes/matlab/fieldtrip';
addpath(fieldtripdir);
ft_defaults;
%-

datadir='/home/georgios.michalareas/workspace/projects/HCP/data/connectomedb/177746/MEG/Restin/rmegpreproc/';

rsfile1=[datadir,'177746_MEG_3-Restin_rmegpreproc.mat'];
rsfile2=[datadir,'177746_MEG_4-Restin_rmegpreproc.mat'];
rsfile3=[datadir,'177746_MEG_5-Restin_rmegpreproc.mat'];

%=
load(rsfile1,'data');
data1=data; clear data;
%
load(rsfile2,'data');
data2=data; clear data;
%
load(rsfile3,'data');
data3=data; clear data;
%=

%%


[C12,IA12,IB12]=intersect(data1.grad.label,data2.grad.label);
[C13,IA13,IB13]=intersect(data1.grad.label,data3.grad.label);
if ~isequal(C12,C13)
    error('common grad labels between resting state recordins 1-2 and 
recordings 1-3 appear to be different')

else
    C=C12;
    IA1=IA12;
    IA2=IB12;
    IA3=IB13;
end

gradlabels=C;
gradpos1=data1.grad.chanpos(IA1,:);
gradpos2=data2.grad.chanpos(IA2,:);
gradpos3=data3.grad.chanpos(IA3,:);

posdif12=sqrt(sum((gradpos1-gradpos2).^2,2));
posdif13=sqrt(sum((gradpos1-gradpos3).^2,2));
posdif23=sqrt(sum((gradpos2-gradpos3).^2,2));

figure;
subplot(1,3,1);
hist(posdif12);
subplot(1,3,2);
hist(posdif13);
subplot(1,3,3);
hist(posdif23);



%% 
% Now do the same for only the MEG sensors without the REFERENCE sensors
% because they are too far from the head an a small head movement can be
% translated into bigger position differences of the reference sensors as
% compared to the MEG sensors which are close to the head

[chansMEG] = ft_channelselection('MEG',data1.grad.label );


[C1,IA1,IB1]=intersect(chansMEG, data1.grad.label);
[C2,IA2,IB2]=intersect(chansMEG, data2.grad.label);
[C3,IA3,IB3]=intersect(chansMEG, data3.grad.label);


if (~isequal(C1,C2))|(~isequal(C1,C3))|(~isequal(C2,C3))
    error('common grad labels between resting state recordins 1-2 and 
recordings 1-3 appear to be different')

else
    C=C1;
end

gradlabels=C;
gradpos1=data1.grad.chanpos(IB1,:);
gradpos2=data2.grad.chanpos(IB2,:);
gradpos3=data3.grad.chanpos(IB3,:);

posdif12=sqrt(sum((gradpos1-gradpos2).^2,2));
posdif13=sqrt(sum((gradpos1-gradpos3).^2,2));
posdif23=sqrt(sum((gradpos2-gradpos3).^2,2));

figure;
subplot(1,3,1);
hist(posdif12);
subplot(1,3,2);
hist(posdif13);
subplot(1,3,3);
hist(posdif23);









On 11/19/2017 9:40 PM, A Nunes wrote:

Hi Giorgos,

Thanks, I did not know about ft_megrealign. Idially I would like to 
just concatenate the data in the sensor level. I recently realized 
that between recordings the sensor position varies up to 0.2 meters in 
data.grad.chanpos. This is because the order of the sensors are 
different between recordings, I don't know why though. In 
data.grad.labelorg it shows the right order, however I have the 
impression that the order of the sensors in the covariance matrix is 
based in chanpos because separate recording have smaller rank than 
concatenating which has full rank. When reading the data file FT gives 
me the warning 'Your data and configuration allow for multiple sensor 
definitions.' maybe this is related.


Do you know how to approach this? Maybe I should somehow specify that 
the right order is in data.grad.labelorg rather than data.grad.label?


Thanks
Adonay

On Fri, Nov 17, 2017 at 5:15 AM, Michalareas, Georgios > wrote:


Hi Adoney,
The released data has been only 7.5 mm maximum movement within scan.
So the sensor positions should be quite close in all three resting
state scans.
ICA was done with all three sessions concatenated together.
So I believe it should be one to concatenate them for your
analysis too and assume sensors are more or less on same location.
If you want to treat each different scan separately then you have
to follow different strategies like:
-Perform beamforming for each session separately and project 

[HCP-Users] R: MEG resting state

[Francesco Di Pompeo]

Hi,

 

ICA has been performed on the three sessions separately so I think it’s
better to use a different strategy.

 

Best,

Francesco

 

Da: hcp-users-boun...@humanconnectome.org
[mailto:hcp-users-boun...@humanconnectome.org] Per conto di Michalareas,
Georgios
Inviato: venerdì 17 novembre 2017 14:15
A: A Nunes ; HCP-Users@humanconnectome.org
Oggetto: Re: [HCP-Users] MEG resting state

 

Hi Adoney,

The released data has been only 7.5 mm maximum movement within scan.

So the sensor positions should be quite close in all three resting state
scans.

ICA was done with all three sessions concatenated together.

So I believe it should be one to concatenate them for your analysis too and
assume sensors are more or less on same location.

If you want to treat each different scan separately then you have to follow
different strategies like:

-Perform beamforming for each session separately and project each session
separately into source space and then concatenate data in source space and
do further analysis there.

-Make a virtual gradiometer array as the mean of the three gradiometer
positions in the three scans and then interpolate the data of each session
to this virtual MEG sensors using Fieltrip’s function ft_megrealign and then
combine all data in this virtual sensor space. Keep in mind that this
projects the data into source space and projects it back to the virtual
sensor space.

 

I hope this helps

Best

Giorgos

 

From:  > on behalf of A Nunes
 >
Date: Thursday, 16. November 2017 at 21:56
To: "hcp-users@humanconnectome.org  "
 >
Subject: [HCP-Users] MEG resting state

 

Hi, 

 

The MEG resting state data is split in three sessions, is it possible to
append the data before computing the covariance matrix? 

I have some doubts because the sensor position might change between the
recordings and if ICA was done separately, then the rank would change
between sessions and I don't know how would this affect beamforming.

 

Any suggestions?

 

Thanks

Adonay

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