<html><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div>Dear Marc,</div><div><br></div><div>It seems from your question that you are interested in single trial reconstructed time series. If your purpose is to do a statistical comparison across a set of conditions I would not do a noise normalization at all. You mention that you used a spatial filter common to the three conditions. As a consequence, the estimate of the noise will be independent of the condition, so any normalization you would apply just leads to a scaling of the data, and will not change the outcome of your statistical test. </div><div>Yet, I understand that it sometimes makes sense to do a normalization in order to be able to make a sensible visualization of the data (i.e. removing the big central blob). Also, in this case I would compute a descriptive statistic across the conditions, rather than normalizing with an estimate of the projected noise (which in FieldTrip is rather rudimentary because it assumes the noise to be spatially white). You could for example compute an F-value from a one-factor, three level anova, or do a pairwise comparison of conditions using a t-test.</div><div>Another alternative (which does not affect your statistical test either), is to work with norm-normalized leadfields. This will take away the blob in the centre of the volume, and facilitates visualization.</div><div><br></div><div>Best wishes,</div><div><br></div><div>Ja-Mathijs</div><div><br></div><br><div><div>On Apr 5, 2011, at 6:54 PM, Marc Recasens wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><font class="Apple-style-span" face="arial, helvetica, sans-serif">Dear all,</font><div><font class="Apple-style-span" face="arial, helvetica, sans-serif"><br></font></div><div><font class="Apple-style-span" face="arial, helvetica, sans-serif">As far as I know it is possible to reconstruct the time-course of the sources (obtained using sourceanalysis) by projecting/multiplying the filter-weights on the data.</font></div> <div><span class="Apple-style-span" style="white-space: pre;"><font class="Apple-style-span" face="arial, helvetica, sans-serif">In my case, the specific code I am using is:</font></span></div><div><span class="Apple-style-span" style="white-space: pre; "><p class="p1"> <font class="Apple-style-span" size="2" face="arial, helvetica, sans-serif"> <span class="s1">for</span> i=1:nvoxels(inside)</font></p><p class="p1"><font class="Apple-style-span" size="2" face="arial, helvetica, sans-serif"> <span class="s1">for</span> tr=1:ntrials</font></p><p class="p1"><font class="Apple-style-span" size="2" face="arial, helvetica, sans-serif"> ts(tr,:)=(source.avg.ori{source.inside(i)}(1,:)*source.avg.filter{source.inside(i)}*data2{condit}.trial{tr});</font></p><p class="p1"><font class="Apple-style-span" size="2" face="arial, helvetica, sans-serif"> <span class="s1">end</span></font></p><p class="p1"><font class="Apple-style-span" size="2" face="arial, helvetica, sans-serif"> datvx(i,:,:)= single_trial_time_course</font></p><p class="p1"><font class="Apple-style-span" size="2" face="arial, helvetica, sans-serif"></font><span class="Apple-style-span" style="font-family: arial, helvetica, sans-serif; ">end</span></p><font class="Apple-style-span" face="arial, helvetica, sans-serif">I used a common filter with 3 different conditions, thus I think this is the only way I have to reconstruct the activity for one of the conditions.</font></span></div> <div><font class="Apple-style-span" face="arial, helvetica, sans-serif"><span class="Apple-style-span" style="white-space: pre;">My Question is about how to apply the NAI normalization here. Can I just divide the output of the source reconstruction by the projected noise of the filter? I am tempted to do something like that</span></font></div> <div><font class="Apple-style-span" face="arial, helvetica, sans-serif"><span class="Apple-style-span" style="white-space: pre;"><br></span></font></div><div><font class="Apple-style-span" face="arial, helvetica, sans-serif"><span class="Apple-style-span" style="white-space: pre;"></span></font><span class="Apple-style-span" style="font-family: arial, helvetica, sans-serif; white-space: pre; ">datvx(i,:,:)= single_trial_time_course ./ repmat(noise(inside),[size(ts,1), size(ts,2)]</span></div> <meta charset="utf-8"><div><span class="Apple-style-span" style="white-space: pre; "><font class="Apple-style-span" face="arial, helvetica, sans-serif"><br></font></span></div><div><span class="Apple-style-span" style="white-space: pre; "><font class="Apple-style-span" face="arial, helvetica, sans-serif">That is, I divide the power by the noise estimate (for each voxel) in every trial and time-point. Could anyone tell me whether this is a correct way to procedure? Any other workaround?</font></span></div> <div><span class="Apple-style-span" style="white-space: pre; "><font class="Apple-style-span" face="arial, helvetica, sans-serif"><br></font></span></div><div><span class="Apple-style-span" style="white-space: pre; "><font class="Apple-style-span" face="arial, helvetica, sans-serif"><br> </font></span></div><div><span class="Apple-style-span" style="white-space: pre; "><font class="Apple-style-span" face="arial, helvetica, sans-serif">Thanks in advance!</font></span></div><div><br></div><div>-- </div><div> Marc Recasens<br>Tel.: +34 639 24 15 98<br><br> </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 apple-content-edited="true"> <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: 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-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>Dr. J.M. (Jan-Mathijs) Schoffelen </div><div>Donders Institute for Brain, Cognition and Behaviour, <br>Centre for Cognitive Neuroimaging,<br>Radboud University Nijmegen, The Netherlands</div><div><a href="mailto:J.Schoffelen@donders.ru.nl">J.Schoffelen@donders.ru.nl</a></div><div>Telephone: 0031-24-3614793</div></div></span></div></span> </div><br></body></html>