Dear Rasha,

One way you can do it is flip your images using the attached function and then use ImCalc to compute your laterality index from the original and flipped images. You can then display the resulting image and read out the values voxel by voxel.  If you want to get an average over ROI from an image there are definitely some SPM extensions allowing you to do that but I'm not well familiar with them. Other people on the list might have a better idea.

Best,

Vladimir

On Wed, Oct 22, 2014 at 12:42 PM, Rasha Haider <[log in to unmask]> wrote:
Dear Vladimir,
thank you again for your quick reply, this email is long and I want to ask you to give me 10 mints from your time please to read it and see if you can help me in this.

I know that GS is the superior in terms of Log- evidence, but the problem is that when I compare the different inversion results with the simulated data it does it look that GS is reconstructing the sources I simulated in the different time windows better that BEE or COH, this is making me confused, so I want to analyse the language data in different way.

If fact I have an idea and I would be very grateful if you can tell me how to apply it using SPM12,

In Language studies they use a Laterality index to analysis the results, it has this form:

LI= QLH-QRH / QLH+QRH,

LH, RH means left and right hemisphere.

The measure Q could be: the number of active sources in ROI or the magnitude of activation in a ROI.

My questions for you are:
1- How to define a ROI?
2- How to extract the number and the magnitude of activation from the data after inversion?

Dear Vladimir, I know I'm asking too much but here in my department no one is working on such this topic and I see that your answers are always helping me. 
I'm so sorry if I'm talking from your time.
Thank you again and Regards
Rasha

From: Vladimir Litvak <[log in to unmask]>
To: Rasha Haider <[log in to unmask]>
Cc: "[log in to unmask]" <[log in to unmask]>
Sent: Wednesday, October 22, 2014 1:04 PM

Subject: Re: [SPM] Concatenating several simulated Meg data sets

Dear Rasha,

It doesn't matter whether the values are negative or positive, all that matters is the differences. A difference of more than 3 is considered strong evidence so it looks like you are consistently getting GS as the best method by far. 

Best,

Vladimir



On Wed, Oct 22, 2014 at 11:36 AM, Rasha Haider <[log in to unmask]> wrote:
Dear Vladimir,
thank you very much, your answers are always helpful for me, it worked, but now I have another question I would appreciate if you could explain it for me.

After I merged the data I applied source reconstruction using the four techniques available now in SPM12 (EBB, IID, COH, GS) to see which one is giving me the most accurate results in order to use it for source reconstruction for real MEG data, but unfortunately the results are almost the same, and the problem is I got negative values for Log-evidence and when I compared between these values for all inversion methods I didn't get any significant difference! (please see the attached photo).
 
My question is what is the meaning of the negative values of  Log-evidence and is there another way to compare the performance of inversion techniques instead of Log-evidence?

I hope you can help me in this matter also.
Best Regards
Rasha

From: Vladimir Litvak <[log in to unmask]>
To: Rasha Haider <[log in to unmask]>
Cc: "[log in to unmask]" <[log in to unmask]>
Sent: Sunday, October 19, 2014 6:44 PM

Subject: Re: [SPM] Concatenating several simulated Meg data sets

Dear Rasha,

If all your epochs are the same length you can merge them and then re-convert your merged file forcing your data to be continuous (set 'Check trial boundaries' to 'No'). Otherwise you'll have to use the tools for converting arbitrary data (see the manual).

Best,

Vladimir



On Sun, Oct 19, 2014 at 11:03 AM, Rasha Haider <[log in to unmask]> wrote:
Dear Gareth,

thank you for answering my second question, but any help regarding the main question about how to generate concatenating simulated data sets??.
I would appreciate if you or any one can help me in this matter because this simulation is very important in my work.

Regards
Rasha

From: "Barnes, Gareth" <[log in to unmask]>
To: SPM (Statistical Parametric Mapping) <[log in to unmask]>; Rasha Haider <[log in to unmask]>
Sent: Sunday, October 19, 2014 5:55 PM
Subject: Re: [SPM] Concatenating several simulated Meg data sets

Dear Rasha
That option generates random white signals (not noise) the reason for this is that sometimes you may wish to have a lot of signals that are not correlated (and this is difficult to do with sinusoids).
best
Gareth

From: SPM (Statistical Parametric Mapping) <[log in to unmask]> on behalf of Rasha Haider <[log in to unmask]>
Sent: 19 October 2014 09:22
To: [log in to unmask]
Subject: [SPM] Concatenating several simulated Meg data sets
 

Dear SPMer experts,

 I want to simulate  MEG data set over a time window ( 0 ms - 600 ms), but I want to divide the total time window into four sub-windows, where for each sub-time window I simulate different sources, for this I have simulated four MEG data sets with concatenated time windows;  and I want to Concatenate these data sets into one data set, I tried to use the merge function but it didn't work since it requires the merged data sets to have the same time points, and in my case the data sets have concatenated time windows, how can I do this ? 

 One more question regarding the following option in the batch editor:
  • * "Bandwidth of Gaussian orthogonal white signals in (Hz) " 
can any one explain to me what is meaning of this choice, I have used it with the default options but I didn't get the shape of the simulated signal.

Any help will be very appreciated.
Regards
Rasha