<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">Dear Eduardo,<div class=""><br class=""></div><div class="">@1: Do you mean normalize across the group? What you could do is interpolate the source-level data to an MRI and then use ft_volumenormalise to normalize all images to a common standard MRI. Check the FT site here: <a href="http://www.fieldtriptoolbox.org/tutorial/sourcemodel?s%5B%5D=warp" class="">http://www.fieldtriptoolbox.org/tutorial/sourcemodel?s[]=warp</a></div><div class="">Alternatively, you could take care that a common source grid is warped to the individual headmodel prior to the source analysis. In this case, you don’t need to normalize, as all subjects have the same number of sources. Check the tutorials on the FT-website for hints (e.g. <a href="http://www.fieldtriptoolbox.org/tutorial/salzburg?s%5B%5D=warp" class="">http://www.fieldtriptoolbox.org/tutorial/salzburg?s[]=warp</a> or <a href="http://www.fieldtriptoolbox.org/tutorial/beamformingextended?s%5B%5D=warp" class="">http://www.fieldtriptoolbox.org/tutorial/beamformingextended?s[]=warp</a>)</div><div class=""><br class=""></div><div class="">@2: Did you build your own headmodel? It might be that the elements of the headmodel intersect. I would go back and double check all steps leading up to the leadfield computation for errors.</div><div class=""><br class=""></div><div class="">@3: I have no idea.</div><div class=""><br class=""></div><div class="">@4: When does this happen?</div><div class=""><br class=""></div><div class="">Good luck,</div><div class=""><br class=""></div><div class="">Julian<br class=""><div><br class=""><blockquote type="cite" class=""><div class="">Am 12.12.2017 um 14:38 schrieb Uri Eduardo Ramírez Pasos <<a href="mailto:urieduardo@gmail.com" class="">urieduardo@gmail.com</a>>:</div><br class="Apple-interchange-newline"><div class=""><div dir="ltr" class="">Dear fieldtrippers,<div class=""><br class=""></div><div class="">I have a couple questions regarding source reconstruction that I hope you can help me with.</div><div class=""><br class=""></div><div class="">1. What is the best way to 'normalize' the position values in each forward model across my subjects so that I can run ft_sourcestatistics with cfg.statistic = 'ft_statfun_depsamplesT' ? </div><div class=""><br class=""></div><div class="">2. For one of my subjects, their leadfield keeps containing only NaNs. What could be the source (no pun intended) of the problem?</div><div class=""><br class=""></div><div class="">3. If my experiment's design has three 'levels' (say A1, A2, A3) for one 'factor', is it valid to subtract source values (obtained using method 'dics') for a comparison (e.g. A1-A3 vs A2-A3)? </div><div class=""><br class=""></div><div class="">4. What could have gone wrong when i get the warning “matrix is singular, close to singular or badly scaled. Results may be inaccurate.” How does one go about solving this? </div><div class=""><br class=""></div><div class="">Best regards,</div><div class="">Eduardo Ramírez, PhD candidate</div><div class="">University of Würzburg</div></div>
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