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<body class='hmmessage'><div dir='ltr'>Hi David,<div><br></div><div>In your case with contrasts defined as 1000, 0100, etc, the PEs and the corresponding COPEs should be the same, so it should not make any difference either using PEs or COPEs. But I don't really understand why you say the PEs would not be independent. Can you explain it a bit more?</div><div><br></div><div>Best,</div><div>Meng<br><br><div><hr id="stopSpelling">Date: Tue, 5 Aug 2014 16:40:39 +0100<br>From: d.soto.b@gmail.com<br>To: pkg-exppsy-pymvpa@lists.alioth.debian.org<br>Subject: Re: [pymvpa] classification based on individual parameter estimates from FSL<br><br><div dir="ltr"><div class="ecxgmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0);">Hi Michael (and all), just a quick clarification on your previous response to my query relating classification based on individual parameter estimates (PEs) - you mentioned I could use the PEs associated with the temporal derivative or even the PEs associated with a set of basis functions....however I wonder that this PEs would not be independent (as would be PEs obtained from different runs)<br>
</div><div class="ecxgmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0);">....would it be okay to use those PEs anyways?<br><br></div><div class="ecxgmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0);">
A second related thing is that I have not been using the PEs exactly but the Contrast of PEs (i.e. COPES in FSL)<br></div><div class="ecxgmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0);">associated with each EV- I have 16 EVs (8 per class) and hence obtained COPES such that<br>
1000<br>0100<br>0010<br>0001<br></div><div class="ecxgmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0);">etc <br><br></div><div class="ecxgmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0);">
I dont see why it would make any difference to work wit COPEs rather than PEs, except that only with the later I could boost my dataset by using the temporal derivatives or basis functions....<br><br></div><div class="ecxgmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0);">
cheers<br>ds<br></div><div class="ecxgmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0);"><br></div></div><div class="ecxgmail_extra"><br><br><div class="ecxgmail_quote">On Fri, Jul 4, 2014 at 2:33 PM, Michael Hanke <span dir="ltr"><<a href="mailto:mih@debian.org" target="_blank">mih@debian.org</a>></span> wrote:<br>
<blockquote class="ecxgmail_quote" style="border-left:1px #ccc solid;padding-left:1ex;">Hi,<br>
<div><br>
On Tue, Jul 01, 2014 at 12:25:40AM +0100, David Soto wrote:<br>
> Hi Michael, indeed ..well done for germany today! :).<br>
> Thanks for the reply and the suggestion on KNN<br>
> I should have been more clear that for each subject I have the<br>
</div>> following *block<br>
> *sequences<br>
<div>> ababbaabbaabbaba in TASK 1<br>
> ababbaabbaabbaba in TASK 2<br>
><br>
> this explains that I have 8 a-betas and 8 b-betas for each task<br>
> AND for each subject..so if i concatenate & normalize all the beta data<br>
> across subjects I will have 8 x 19 (subjects)= 152 beta images for class a<br>
> and the same for class b<br>
<br>
</div>Ah, I guess you model each task with two regressors (hrf + derivative?).<br>
You can also use a basis function set and get even more betas...<br>
<div>><br>
> then could I use SVM searchlight trained to discriminate a from b in task1<br>
> betas and tested in the task2 betas?<br>
<br>
</div>yes, no problem.<br>
<br>
Cheers,<br>
<br>
Michael<br>
<br>
PS: Off to enjoy the quarter finals ... ;-)<br>
<span class="ecxHOEnZb"><font color="#888888"><br>
<br>
--<br>
Michael Hanke<br>
<a href="http://mih.voxindeserto.de" target="_blank">http://mih.voxindeserto.de</a><br>
<br>
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</font></span></blockquote></div><br><br clear="all"><br>-- <br><div dir="ltr"><a href="http://www1.imperial.ac.uk/medicine/people/d.soto/" style="color:rgb(17,85,204);" target="_blank">http://www1.imperial.ac.uk/medicine/people/d.soto/</a><br>
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