<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Hi Naran,<div><br></div><div>Usually the statistical contrast takes care of the depth bias. </div><div>Alternatively, you could test the difference of the log10 of the power (i.e. take the log10 of srcX.avg.pow); this is equivalent to testing the log10 of the ratio.</div><div><br></div><div>Indeed you can correct for multiple comparisons using the cluster-based test statistic.</div><div><br></div><div>Best,</div><div>Jan-Mathijs</div><div><br></div><div><br><div><div>On Jan 19, 2013, at 10:17 AM, Narayanan Kutty wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite">Hi Jan,<div><br>Thank you for the quick reply. I thought the normalization (srcA-srcB)/srcA was necessary to get rid of the bias towards the center of the head. So if I do it as you suggest </div><div><br></div><div>1. would the bias be still there, or will it be taken care of?</div>
<div><br></div><div>2. if I wanted to "correct" for multiple comparisons do I follow your suggestion with a few extras to do the montecarlo as given here</div><div> <a href="http://fieldtrip.fcdonders.nl/example/source_statistics#group_level_statistics_over_subjects">http://fieldtrip.fcdonders.nl/example/source_statistics#group_level_statistics_over_subjects</a></div>
<div><br></div><div>Thank you again!</div><div><br></div><div>best,</div><div>Naran</div><div><br></div><div><div class="gmail_quote">On Sat, Jan 19, 2013 at 2:08 AM, jan-mathijs schoffelen <span dir="ltr"><<a href="mailto:jan.schoffelen@donders.ru.nl" target="_blank">jan.schoffelen@donders.ru.nl</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="word-wrap:break-word">Hi Naran,<div><br></div><div>It seems you're almost there!</div><div>What I would do is the following:</div>
<div><br></div><div>Rather than computing the relative differece srcDiff, I'd keep the 2 conditions separate. Then, srcA and srcB (for all subjects) are ready to enter into ft_sourcestatistics. No need to call ft_sourcegrandaverage. Note: that if you call ft_sourcegrandaverage with cfg.keepindividual = 'no', you'll only get an average across subjects, so that's not useful for doing statistics to begin with. Alternatively, you could call ft_sourcegrandaverage with cfg.keepindividual = 'no', but this is a redundant step. Moreover, it is a bit silly, because in that case no 'grandaveraging' is done to begin with.</div>
<div>We don't have tutorial documentation (yet) for doing group statistics on source level data, but you may get some inspiration from the tutorials that deal with sensor-level data. Instead of using ft_timelockanalysis you should use ft_sourcestatistics.</div>
<div>The final step would be something like this:</div><div><br></div><div>cfg = [];</div><div>cfg ...</div><div>stat = ft_sourcestatistics(cfg, srcA1, srcA2, srcA3, ...., srcB1, srcB2, srcB3, ....</div><div><br></div><div>
where srcA1 etc pertain to the single subject results for condition A and likewise for B. It now boils down to informing ft_sourcestatistics with the appropriate design matrix, e.g.</div><div><br></div><div>cfg.design = [1:Nsubj 1:Nsubj;ones(1,Nsubj) ones(1,Nsubj)*2];</div>
<div>cfg.ivar = 2;</div><div>cfg.uvar = 1;</div><div>cfg.statistic = 'depsamplesT';</div><div><br></div><div>In this case you will perform a paired T-test between the 2 conditions.</div><div><br></div><div>Best,</div>
<div><br></div><div>Jan-Mathijs</div><div><br></div><div><br><div><div><div class="h5"><div>On Jan 19, 2013, at 7:00 AM, Narayanan Kutty wrote:</div><br></div></div><blockquote type="cite"><div><div class="h5">Dear All, <div>
<br></div><div>I had a question regarding across subject source level statistics using fieldtrip.</div><div><br></div><div>For 15 subjects I have two conditions conA and conB. For each subject I then</div><div>
<br></div><div>1. calculate a DICS beaformer for conA, conB, based on a common filter. (lets call them srcA and srcB resp.)</div><div><br></div><div>2. calculate normalized srcDiff by doing (srcA-srcB/srcA)</div><div><br>
</div><div>3. interpolate and normalize srcDiff using ft_sourceinterpolate and ft_volumenormalise (lets call the output srcDiffNorm)</div><div><br></div><div>After I have done that for each subject I use ft_sourcegrandaverage with cfg.keepindividual = 'yes'; to get grandavgAvsB.</div>
<div><br></div><div>What I would like to do is test just grandavgAvsB to see if any voxels are significantly different from zero (zero being conA = conB). </div><div><br></div><div>How should I go about doing this using ft_sourcestatistics (or another program). The example scripts seem want two datasets.</div>
<div><br></div><div>sincerely</div><div>Naran</div></div></div>
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<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" target="_blank">J.Schoffelen@donders.ru.nl</a></div><div>Telephone: <a href="tel:%2B31-24-3614793" value="+31243614793" target="_blank">+31-24-3614793</a></div>
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