<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>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>On Jan 19, 2013, at 7:00 AM, Narayanan Kutty wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite">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>
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<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-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; "><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-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 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-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 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-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></span></div></span></div></span></div></span></div></span></span>
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