Dear Aleksandra,<div><br></div><div>That sounds like a limitation of this approach, so no perfect answer.</div><div><br></div><div>Alternatively, you can make a relatively big clusters and show dipole density difference. I'll give you my plugin (which is alpha version now) for that purpose. Email me if you are interested in.</div>
<div><br></div><div>You may also want to try Measure Projection to see what it returns. It does not generate too many domains (compared to k-means clustering where we use 10-25) so you'll have no choice there and solve the problem.</div>
<div><br></div><div>Makoto</div><div><br></div><div>Sent from my Brain<br><br><div class="gmail_quote">2013/2/27 Aleksandra Vuckovic <span dir="ltr"><<a href="mailto:Aleksandra.Vuckovic@glasgow.ac.uk" target="_blank">Aleksandra.Vuckovic@glasgow.ac.uk</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div bgcolor="#FFFFFF"><div>Dear Makoto,</div><div>I do expect different spatial distribution of some dipoles in different groups, and some clusters existing dominantly in one group only. If I use smaller number of clusters I might miss it. Which clustering method would you recommend?</div>
<div>Many thanks</div><div>Aleksandra</div><div><br>Sent from my iPhone</div><div><div class="h5"><div><br>On 27 Feb 2013, at 19:02, "Makoto Miyakoshi" <<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>> wrote:<br>
<br></div><div></div><blockquote type="cite"><div>Dear Aleksandra,<div><br></div><div><span style="font-family:tahoma;font-size:x-small">> I can hardly get the same number of subjects from each group,</span><br><br>Why don't you use smaller number of clusters? I don't see any necessity to create separate STUDY in your case.<br>
<br>Makoto<br><br><div class="gmail_quote">2013/2/27 Aleksandra Vuckovic <span dir="ltr"><<a href="mailto:Aleksandra.Vuckovic@glasgow.ac.uk" target="_blank"></a><a href="mailto:Aleksandra.Vuckovic@glasgow.ac.uk" target="_blank">Aleksandra.Vuckovic@glasgow.ac.uk</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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<div dir="ltr"><font color="#000000" face="Tahoma">Hi</font></div>
<div dir="ltr"><font face="tahoma">I have three groups (patients) and three conditions, 9 variables in total. I am trying to cluster their ICA components and wonder if it is a good idea to create only one study. Different groups have (as expected) slightly<a></a>
different areas of cortical activation, so when I create a common cluster I can hardly get the same number of subjects from each group, so further comparison between conditions may be affected by different number of subjects in each group.</font></div>
<div dir="ltr"><font face="tahoma"></font> </div>
<div dir="ltr"><font face="tahoma">Has anybody faced a similar problem? Should<a></a> I create separate studies for separate groups and then compare coordinates<a></a> of similar clusters (does not seem like an optimal solution)</font></div>
<div dir="ltr"><font face="tahoma"></font> </div>
<div dir="ltr"><font face="tahoma">Many thanks,</font></div>
<div dir="ltr"><font face="tahoma">Aleksandra</font></div>
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-- <br>Makoto Miyakoshi<br>JSPS Postdoctral Fellow for Research Abroad<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br>
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</div></blockquote></div></div></div></blockquote></div><br><br clear="all"><div><br></div>-- <br>Makoto Miyakoshi<br>JSPS Postdoctral Fellow for Research Abroad<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br>
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