Thanks for this clarification!<br><br><div class="gmail_quote">On Thu, May 21, 2009 at 11:21 AM, Jesper Andersson <span dir="ltr">&lt;<a href="mailto:[log in to unmask]"><a href="/cgi-bin/wa-jisc.exe?LOGON=A3%3Dind0905%26L%3DFSL%26E%3Dquoted-printable%26P%3D2380337%26B%3D--0016e646058c5f7d4e046a843bac%26T%3Dtext%252Fhtml%3B%2520charset%3DISO-8859-1%26pending%3D" target="_parent" >[log in to unmask]</a></a>&gt;</span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">

Dear Zarrar,<div class="im"><br>
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I was interested in doing something just like this and was glad to see that Klara asked this question. I have another clarification question regarding the use of fsl_motion_outliers. When you add the confound file produced by fsl_motion_outliers to your FEAT model, does this impact the inclusion of the motion parameters as covariates? So if you previously included the motion parameters as covariates, will they no longer be necessary with the inclusion of the confound matrix from fsl_motion_outliers?<br>


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No, they are pretty much complementary. Including the motion parameters removes (as a first order approximation) effects that depend ~linearly on subject position, such as e.g. distortion-by-position and dropout-by-position interactions.<br>


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The covariates you get from motion_outliers on the other hand removes effects related to &quot;subject velocity&quot; (again as a first order approximation). For example if someone makes a sudden movement in the middle of the acquisition of a volume the rigid-body transformation is no longer valid, and such a volume would be picked up by motion_outliers.<br>


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Good Luck Jesper<br>
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