<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">An alternative would be to interpolate the cortical sheet to a 3D grid (where the grid is defined for each subject based on a warped template grid defined in a standard space), and then do clustering using a regular 3D spatial neighbourhood structure. The rationale being that two vertices on the sheet may appear as disconnected (e.g. being on two sides of a sulcus) whereas, given the poor spatial resolution, they belong to the same spatial blob.<div><br></div><div>Best,</div><div>Jan-Mathijs</div><div><br><div><div>On Jun 12, 2013, at 5:44 PM, <a href="mailto:smoratti@psi.ucm.es">smoratti@psi.ucm.es</a> wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear Nicolai,<div><br></div><div>Indeed I have used ft_timelockstatistics for minimum norm source data. The trick is to put the source level data into a ERF structure. Determining the neighbors of a source surface with vertices is not trivial. However I used tess_vertconn.m from the BrainStorm toolbox to get the connectivity matrix that tells you who is a neighbor. This you can feed into timelockstats.</div><div><br></div><div>Hope that helps,</div><div><br></div><div>Stephan</div><div><br><div>
<span class="Apple-style-span" style="border-collapse: separate; font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div><div>________________________________________________________</div><div>Stephan Moratti, PhD<br><br></div><div>see also: <a href="http://web.me.com/smoratti/">http://web.me.com/smoratti/</a><br><br></div><div>Universidad Complutense de Madrid</div><div>Facultad de Psicología</div><div>Departamento de Psicología Básica I</div><div>Campus de Somosaguas</div><div>28223 Pozuelo de Alarcón (Madrid)</div><div>Spain</div><div><br></div><div>and</div><div><br></div><div>Center for Biomedical Technology</div><div>Laboratory for Cognitive and Computational Neuroscience</div><div>Parque Científico y Tecnológico de la Universidad Politecnica de Madrid<br>Campus Montegancedo</div><div>28223 Pozuelo de Alarcón (Madrid)</div><div>Spain<br><br><br>email: <a href="mailto:smoratti@psi.ucm.es">smoratti@psi.ucm.es</a><br>Tel.: +34 679219982</div></div></span>
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<br><div><div>El 12/06/2013, a las 15:44, Nicolai Mersebak escribió:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div dir="ltr">Dear all,<div><br></div><div>I have a question concerning <span style="color:rgb(0,0,0)">the usage of ft_sourcegrandaverage and ft_sourcestatistics. </span></div><div><font color="#000000"><br></font></div><div>
<font color="#000000">After using ft_sourceanalysis (method: MNE), I get spatio-temporal source reconstructed data in source.avg.pow (4050 x 897): 4050 sources and 897 time points. </font></div><div><font color="#000000"><br>
</font></div><div><font color="#000000">Now I would like to use the cluster-based permutation test on my source reconstructed data. However it seems like ft_sourcegrandaverage and</font><span style="color:rgb(0,0,0)"> ft_sourcestatistics don't support source level time courses. E.g when I am using </span><span style="color:rgb(0,0,0)">ft_sourcegrandaverage I am getting the following error:</span></div>
<div><font color="#000000"><br></font></div><div><div>Error in ft_sourcegrandaverage (line 158)</div><div> dat(:,i) = tmp(:);</div><div><br></div><div style="">Looking into the code:</div><div style=""><br></div><div style=""><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font-size: 10px; font-family: Courier; ">
<span style="color:rgb(4,51,255)">for</span> i=1:Nsubject</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font-size: 10px; font-family: Courier; "> tmp = getsubfield(varargin{i}, parameterselection(cfg.parameter, varargin{i}));</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font-size: 10px; font-family: Courier; "> dat(:,i) = tmp(:);</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font-size: 10px; font-family: Courier; "> tmp = getsubfield(varargin{i}, <span style="color:rgb(178,69,243)">'inside'</span>);</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font-size: 10px; font-family: Courier; "> inside(tmp,i) = 1;</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font-size: 10px; font-family: Courier; color: rgb(4, 51, 255); "><span style="color:rgb(0,0,0)"> </span>end</div><div><br></div></div><div style="">I see that "tmp" are getting the structure [N_sources x timepoints] from source.avg.pow for one subject, where "dat" requires the structure [N_sources x 1]. <br>
</div><div style=""><br></div><div style="">I seached the mailing list for similar issues and found this thread:</div><div style=""><br></div><div style=""><a href="http://mailman.science.ru.nl/pipermail/fieldtrip/2010-September/003122.html">http://mailman.science.ru.nl/pipermail/fieldtrip/2010-September/003122.html</a><br>
</div><div style=""><br></div><div style="">Since I am interested in using the temporal dimension in my statistics, I would like to know if it is still not possible to use spatio-temporal source reconstructed data in ft_sourcestatistics and ft_sourcegrandaverage ?</div>
<div style=""><br></div><div style="">Or if any have succeeded in using the cluster-based permutation test on source level also including the temporal dimension ? </div><div style=""><br></div><div style="">Alternative I was thinking that I might could use ft_timelockstatistics, where I substituted the channels with sources, e.g instead of having 64 channels, I would now have 4050 "channels". </div>
<div style="">If so I need to calculate a label structure and an appropriate neighbor structure, which I guess is possible as I have all the 3D coordinates for each source, e.g in leadfield.pos ?</div><div style="">I know this is a work around solution, but have anyone tried or have any experience using such an approach ? </div>
<div style=""><br></div><div style="">Best,</div><div style=""><br></div><div style="">Nicolai</div><div><br></div></div></div>
_______________________________________________<br>fieldtrip mailing list<br><a href="mailto:fieldtrip@donders.ru.nl">fieldtrip@donders.ru.nl</a><br><a href="http://mailman.science.ru.nl/mailman/listinfo/fieldtrip">http://mailman.science.ru.nl/mailman/listinfo/fieldtrip</a></blockquote></div><br></div></div>_______________________________________________<br>fieldtrip mailing list<br><a href="mailto:fieldtrip@donders.ru.nl">fieldtrip@donders.ru.nl</a><br>http://mailman.science.ru.nl/mailman/listinfo/fieldtrip</blockquote></div><br><div>
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text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div>Jan-Mathijs Schoffelen, MD PhD </div><div><br></div><div>Donders Institute for Brain, Cognition and Behaviour, <br>Centre for Cognitive Neuroimaging,<br>Radboud University Nijmegen, The Netherlands</div><div><br></div><div>Max Planck Institute for Psycholinguistics,</div><div>Nijmegen, The Netherlands</div><div><br></div><div><a href="mailto:J.Schoffelen@donders.ru.nl">J.Schoffelen@donders.ru.nl</a></div><div>Telephone: +31-24-3614793</div><div><br></div><div><a href="http://www.hettaligebrein.nl">http://www.hettaligebrein.nl</a></div></div></span></div></span></div></span></div></span></div></span></div></span></div></span></div></span></span>
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