<div dir="ltr"><div><div><div><div><div><div><div>Hello Raghavan,<br></div>I am actually looking at GLM head motion correction method (FieldTrip regressconfound) with some phantom MEG data and found a few things that might be of interest to you:<br>
</div>- the method is best for Evoked Response analysis as it requires trial averaging at the end of the correction (you want to average motion corrected data not data that has motion); the method works for the resting state too but with an error linearly increasing with the amount of motion<br>
</div>- you should see differences in <span style="font-family:arial,sans-serif;font-size:13px">before and after regressconfound correction as the method re-aligns the waveforms for one (initial=averaged) head position; you will even see the differences if comparing true recordings (no motions) to recordings with the motion (which I did with the phantom) as the method is not perfect and can't possibly remove all motion confounds/effects completely<br>
- your question: "</span><span style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">What do I do if I am comparing a condition across different time points, rather than 2 conditions in one time point?"</span>, my answer: I would use one (initial) averaged head position (for 1st trail for example) to compare, because you want to convert/correct your measurements for <b>relative comparison</b> and they should represent the same head position (for better statistical sensitivity).<br>
<br></span><br><span style="font-family:arial,sans-serif;font-size:13px"><span style="background-color:rgb(255,255,204);font-family:arial,sans-serif;font-size:13px">Arjen</span><span style="font-family:arial,sans-serif;font-size:13px">,<br>
</span></span></div><span style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">Please correct me if I am mistaking.<br><br></span></span></div><span style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">Cheers,<br>
</span></span></div><span style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">Inna<br></span></span></div><span style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">Wake Forest Baptist Health<br>
MEG lab<br></span></span><div><div><div><div><span style="font-family:arial,sans-serif;font-size:13px"><br> <br></span></div></div></div></div></div><div class="gmail_extra"><br clear="all"><div>Inna McGowin<br></div>
<br><br><div class="gmail_quote">On Mon, Apr 7, 2014 at 12:59 PM, Raghavan Gopalakrishnan <span dir="ltr"><<a href="mailto:gopalar.ccf@gmail.com" target="_blank">gopalar.ccf@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div dir="ltr"><div>Arjen,</div><div><br></div><div>Thanks for all your previous help on using regressconfound.</div><div>It would be great if you can shed light on some questions I asked earlier (See below).</div><div>Also, when applying regressconfound to time-frequency (TF) data, regressconfound removes variance from TF from individual trials. So, utimately regressconfound helps to improve the induced TF responses rather than evoked responses. (By induced, I mean calculating TF on trial by trial basis and then computing average, But evoked I mean calculating TF on averaged data)</div>
<div>If one is interested in only evoked, then regressconfound would not be of much help. Is that correct?</div><div><br></div><div>Thanks,</div><div>Raghavan</div><div><br></div><div><br></div><div><br></div><div><br></div>
<div><br></div><br clear="all"><div><span style="font-family:arial,sans-serif;font-size:13px">Date: Wed, 26 Feb 2014 11:38:23 -0500</span><br style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">From: Raghavan Gopalakrishnan <</span><a href="mailto:gopalar.ccf@gmail.com" style="font-family:arial,sans-serif;font-size:13px" target="_blank">gopalar.ccf@gmail.com</a><span style="font-family:arial,sans-serif;font-size:13px">></span><br style="font-family:arial,sans-serif;font-size:13px">
<span style="font-family:arial,sans-serif;font-size:13px">To: </span><a href="mailto:fieldtrip@science.ru.nl" style="font-family:arial,sans-serif;font-size:13px" target="_blank">fieldtrip@science.ru.nl</a><br style="font-family:arial,sans-serif;font-size:13px">
<span style="font-family:arial,sans-serif;font-size:13px">Subject: [FieldTrip] regressconfound and statistics</span><br style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">Message-ID: <</span><a href="mailto:A36DB35E-1C8A-4C8A-A0F3-083B8E1131C1@gmail.com" style="font-family:arial,sans-serif;font-size:13px" target="_blank">A36DB35E-1C8A-4C8A-A0F3-083B8E1131C1@gmail.com</a><span style="font-family:arial,sans-serif;font-size:13px">></span><br style="font-family:arial,sans-serif;font-size:13px">
<span style="font-family:arial,sans-serif;font-size:13px">Content-Type: text/plain; charset=windows-1252</span><br style="font-family:arial,sans-serif;font-size:13px"><br style="font-family:arial,sans-serif;font-size:13px">
<span style="background-color:rgb(255,255,204);font-family:arial,sans-serif;font-size:13px">Arjen</span><span style="font-family:arial,sans-serif;font-size:13px">,</span><br style="font-family:arial,sans-serif;font-size:13px">
<br style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">I agree with your steps and your assumptions about my data, though I am only interested in evoked activity. I am indeed doing the steps 1 and 2 in the correct order. I just kept the 4 blocks and confounds associated with them separate from each other (for book keeping purposes), but I used the mean head position of all 4 blocks to demean the translations and rotations in each block. Technically, I guess this is the same as appending the blocks prior to running regress confound.</span><br style="font-family:arial,sans-serif;font-size:13px">
<br style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">When I said, "However, the problem is, whatever significance I found earlier (i.e. by comparing means rather than t-statistic) doesn?t test significant now.? I was comparing the means of the data that has been run through regress confound. When I plot (sum square of all gradiometers in each subject in each condition) before and after regress confound, I see some differences. Should I not see any differences at all?</span><br style="font-family:arial,sans-serif;font-size:13px">
<br style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px"> But there is one catch. What do I do if I am comparing a condition across different time points, rather than 2 conditions in one time point? MEG collected at two different time points will have different average head positions. In that case, should I use one average head position (computed from time point - 1) and use that to demean the translations and rotations in rest of the time points?</span><br style="font-family:arial,sans-serif;font-size:13px">
<br style="font-family:arial,sans-serif;font-size:13px"><br style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">Thanks for your support.</span><span class="HOEnZb"><font color="#888888"><br style="font-family:arial,sans-serif;font-size:13px">
<span style="font-family:arial,sans-serif;font-size:13px">Raghavan</span><br style="font-family:arial,sans-serif;font-size:13px"></font></span></div><span class="HOEnZb"><font color="#888888">-- <br><div dir="ltr"><br></div>
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