Hello, <br><br>I want to get some help regarding GLM. <br><br>The GLM analysis that needs to be performed to get the beta
estimates is a multiple regression analysis. In other words, I need to
specify a model that includes predictors (one predictor for each
condition, a confound-mean predictor (all-one vector), and optionally
head-motion and trend predictors), and then fit this model to the data
to obtain the beta-estimates.<br>
<br><br><span style="color:rgb(51,204,255)"><b>TR (scan repeat time) = 3000 ms. Functional measurement consisted of 220 scans.</b></span><br><br>Attached
with this email the table of Onsets and Durations for both conditions.
There are also indices of the conditions but for me they are not logical
as they don't really correspond to the timings. Anyway, I have an
important question. What about the number of scans for each condition
and also for the rest condition? The durations are real numbers and if i
try to divid the number by 3 (which is TR) i get real number of scans,
so i'm not exactly sure of the number of 1s and 0s in the model.<br>
<br>It is needed to convolve the model with gamma function, how this
could be done? What about the parameters of gamma function? I also don't
know how to do the additional and confound-mean predictors.<br><br>Thanks in advance, <br>Abdul<br><br><div class="gmail_quote">On Wed, Sep 19, 2012 at 5:52 PM, Mike E. Klein <span dir="ltr"><<a href="mailto:michaeleklein@gmail.com" target="_blank">michaeleklein@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><span style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:left;font-style:normal;display:inline!important;font-weight:normal;float:none;line-height:12px;color:rgb(0,153,51);text-transform:none;font-size:small;white-space:normal;font-family:arial,sans-serif;word-spacing:0px"><a href="http://www.princeton.edu/%7Ematthewb/Publications/" target="_blank">www.princeton.edu/~matthewb/Publications/</a></span><b style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:left;font-style:normal;line-height:12px;color:rgb(0,153,51);text-transform:none;font-size:small;white-space:normal;font-family:arial,sans-serif;word-spacing:0px">tutorial</b><span style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:left;font-style:normal;display:inline!important;font-weight:normal;float:none;line-height:12px;color:rgb(0,153,51);text-transform:none;font-size:small;white-space:normal;font-family:arial,sans-serif;word-spacing:0px">.pdf</span><div>
<div style="text-align:left"><font color="#009933" face="arial, sans-serif"><span style="line-height:12px"><br></span></font></div><div style="text-align:left"><font color="#009933" face="arial, sans-serif"><span style="line-height:12px"><a href="http://scan.oxfordjournals.org/content/4/1/101.full" target="_blank">http://scan.oxfordjournals.org/content/4/1/101.full</a></span></font></div>
<div style="text-align:left"><font color="#009933" face="arial, sans-serif"><br></font></div><div style="text-align:left"><font color="#009933" face="arial, sans-serif"><span style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:left;font-style:normal;display:inline!important;font-weight:normal;float:none;line-height:12px;color:rgb(0,153,51);text-transform:none;font-size:small;white-space:normal;font-family:arial,sans-serif;word-spacing:0px"><a href="http://www.bcn-nic.nl/txt/people/publications/" target="_blank">www.bcn-nic.nl/txt/people/publications/</a></span><b style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:left;font-style:normal;line-height:12px;color:rgb(0,153,51);text-transform:none;font-size:small;white-space:normal;font-family:arial,sans-serif;word-spacing:0px">Etzel</b><span style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:left;font-style:normal;display:inline!important;font-weight:normal;float:none;line-height:12px;color:rgb(0,153,51);text-transform:none;font-size:small;white-space:normal;font-family:arial,sans-serif;word-spacing:0px">2009.pdf</span></font></div>
<div><div class="h5">
<br><div class="gmail_quote">On Wed, Sep 19, 2012 at 11:22 AM, Abduljalil Sireis <span dir="ltr"><<a href="mailto:kentman234@gmail.com" target="_blank">kentman234@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<br><br><div class="gmail_quote"><div>On Wed, Sep 19, 2012 at 4:56 PM, J.A. Etzel <span dir="ltr"><<a href="mailto:jetzel@artsci.wustl.edu" target="_blank">jetzel@artsci.wustl.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
This is far too general of a question: which steps are necessary depends upon the precise hypothesis you're testing with the analysis. I can easily think of a half dozen analysis schemes appropriate in various situations.<br>
<br>
You should start by making your hypothesis as concrete as possible. For example, are you interested in widespread activity, or activity in particular brain areas only? Are you interested in activity during particular times during the clips, or some sort of temporally averaged activity?<br>
</blockquote><div><br></div></div><div>- I'm interested in particular brain areas or regions of interest which should be activated during the experiment. Also, a temporally averaged activity would be fine. </div><div>
<div> </div>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<br>
There are many useful tutorials; the ones by Pereira, Mur, and (if I say so myself) Etzel are good starting points.<br></blockquote><div><br></div></div><div>- Which tutorial do you mean? Could you please write me its name or the link or it? Thank you!</div>
<div><div>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<br>
good luck,<br>
Jo<div><div><br>
<br>
<br>
On 9/19/2012 7:19 AM, Abduljalil Sireis wrote:<br>
</div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div>
Hi,<br>
<br>
I need help in classification project of subjects see two types of<br>
video clips. One kind is dangerous car accidents and the other is normal<br>
car advertisements. We have number of subjects perform this task with<br>
one session per subject. Each run consists of 6 clips from each type,the<br>
two successive spots are separated by black screen and a yellow cross to<br>
fix the subjects eyes. There was not a specific order of the spots but<br>
more than two successive ones of the same type were not allowed. All<br>
videos were accompanied with voice, so we have visual and audio<br>
stimulation.<br>
<br>
I think that i need to do features selection with respect to the active<br>
regions corresponding to the stimulations, then a cross-validation leave<br>
one-run-out can be applied. Could you please guide me through this<br>
project? Which concrete steps should i need to do?<br>
<br>
Thank you,<br>
Abdul<br>
<br>
<br></div></div>
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<br><span><font color="#888888">
</font></span></blockquote><span><font color="#888888">
<br>
-- <br>
Joset A. Etzel, Ph.D.<br>
Research Analyst<br>
Cognitive Control & Psychopathology Lab<br>
Washington University in St. Louis<br>
<a href="http://mvpa.blogspot.com/" target="_blank">http://mvpa.blogspot.com/</a><br>
<br>
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