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

2017-11-22 Thread A Nunes 
Hi Georgios and Francesco

Thanks for your help and the script! My doubts are solved.

Cheers,
Adoney

On Mon, Nov 20, 2017 at 10:22 AM, Georgios Michalareas 
wrote:

> 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-bounces@
> 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 <
> adonay.s.nu...@gmail.com>
> *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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> <#m_-6511468309016046648_DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>
>
>
> --
> 
> Dr. Georgios Michalareas
> Neuroscience Department
> Max Planck Institute for Empirical Aesthetics
>
> email: g...@aesthetics.mpg.de
> phone: +49 69 8300479-325 <+49%2069%208300479325>
> 
>
>

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

2017-11-20 Thread 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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<#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>


--

Dr. Georgios Michalareas
Neuroscience Department
Max Planck Institute for Empirical Aesthetics

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



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[HCP-Users] R: MEG resting state

2017-11-20 Thread 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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