<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="">Hi Ingmar,<div class=""><br class=""></div><div class="">My suggestion is that if you are interested in the source, you’d better to do MNE localisation first and run RSA directly in the source space. Here are two papers from my own group applying this method.</div><div class=""><span style="caret-color: rgb(39, 39, 39); color: rgb(39, 39, 39); font-family: "Open Sans", sans-serif; font-size: 16px;" class=""><br class=""></span></div><div class=""><b class=""><span style="caret-color: rgb(39, 39, 39); color: rgb(39, 39, 39); font-family: "Open Sans", sans-serif; font-size: 16px;" class="">Wingfield, C., </span><span style="caret-color: rgb(39, 39, 39); color: rgb(39, 39, 39); font-family: "Open Sans", sans-serif; font-size: 16px; box-sizing: border-box;" class="">Su, L. </span><span style="caret-color: rgb(39, 39, 39); color: rgb(39, 39, 39); font-family: "Open Sans", sans-serif; font-size: 16px;" class="">, Liu, X., Zhang, C., Woodland, P., Thwaites, A., Fonteneau, E., Marslen-Wilson, W.D., (2017) Relating Dynamic Brain States to Dynamic Machine States: Human and Machine Solutions to the Speech Recognition Problem, PloS Computational Biology. <a href="http://doi.org/10.1371/journal.pcbi.1005617" class="">doi.org/10.1371/journal.pcbi.1005617</a></span></b></div><div class=""><b style="caret-color: rgb(39, 39, 39); color: rgb(39, 39, 39); font-family: "Open Sans", sans-serif; font-size: 16px; box-sizing: border-box;" class=""><br class=""></b></div><div class=""><b class=""><span style="caret-color: rgb(39, 39, 39); color: rgb(39, 39, 39); font-family: "Open Sans", sans-serif; font-size: 16px; box-sizing: border-box;" class="">Su, L. </span><span style="caret-color: rgb(39, 39, 39); color: rgb(39, 39, 39); font-family: "Open Sans", sans-serif; font-size: 16px;" class="">, Fonteneau, E., Marslen-Wilson, W. and Kriegeskorte, N. (2012) Spatiotemporal Searchlight Representational Similarity Analysis in EMEG Source Space, IEEE Xplore, 97-100, doi:10.1109/PRNI.2012.26</span></b></div><div class=""><br class=""></div><div class="">Bw,</div><div class=""><br class=""></div><div class="">Li Su</div><div class=""><br class=""></div><div class=""></div><div class=""><br class=""><div class=""><div class=""><span class="" style="font-size: 11px;">— </span></div><div class=""><div class="" style="font-size: 14px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><span class="" style="font-size: 11px;"><b class="">Professor Li Su, PhD</b><br class=""><font color="#011993" class="">Chair of Neuroimaging</font></span></div><div class="" style="font-size: 14px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><font color="#011993" class=""><span class="" style="color: rgb(146, 146, 146); font-size: 11px;">Sheffield Institute for Translational Neuroscience, </span></font></div><div class="" style="font-size: 14px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><span class="" style="font-size: 11px;"><font color="#011993" class=""><span class="" style="color: rgb(146, 146, 146);">University of Sheffield, </span></font><span class="" style="color: rgb(146, 146, 146);">385A Glossop Road, </span></span></div><div class="" style="font-size: 14px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><span class="" style="font-size: 11px;"><span class="" style="color: rgb(146, 146, 146);">Sheffield, </span><span class="" style="color: rgb(146, 146, 146);">S10 2HQ, UK</span></span></div><div class="" style="font-size: 14px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><font color="#011993" class="" style="font-size: 11px;">Alzheimer’s Research UK Senior Research Fellow</font></div><div class="" style="font-size: 14px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><font color="#929292" class="" style="font-size: 11px;">Department of Psychiatry, University of Cambridge, <br class="">Level E4, Box 189, Cambridge Biomedical Campus, <br class="">Cambridge, CB2 0SP, UK</font></div></div></div></div><div><br class=""><blockquote type="cite" class=""><div class="">On 29 Mar 2021, at 15:43, Ingmar de Vries <<a href="mailto:i.e.j.de.vries@gmail.com" class="">i.e.j.de.vries@gmail.com</a>> wrote:</div><br class="Apple-interchange-newline"><div class=""><div dir="ltr" class=""><div dir="ltr" class="">Dear Fieldtrip community,<div class=""><br class=""></div><div class="">I have a conceptual question that potentially applies to many possible situations. If it helps, here is my situation: </div><div class=""><br class=""></div><div class="">I am currently analyzing MEG data (Neuromag, 306 sensors), using representational similarity analysis (RSA). I have applied a spatial searchlight at the sensor level, which results in RSA (i.e. correlation) values at each of the 306 sensors. This results in an interesting spatial peak of correlation above areas that are expected to be involved. </div><div class=""><br class=""></div><div class="">Is it possible to localize the cortical source of this sensor-level peak in correlation? </div><div class="">This question can be generalized to any metric, i.e.: Is it possible to localize the cortical source of any sensor-level metric?</div><div class=""><br class=""></div><div class="">I can't find any literature doing something similar, and the main problem I foresee is in the definition of a head model. That is, standard head models either define electrical conductivity (EEG), or spread of the magnetic field (MEG), but not the spread of an information-based metric (here correlation, but could be decoding accuracy as well). Is there a solution for this?</div><div class=""><br class=""></div><div class="">The alternative approach (plan B) would be to first apply source reconstruction on the raw MEG signal with a distributed source model (e.g. MNE), and subsequently apply the RSA analyses on the source-reconstructed signal. </div><div class=""><br class=""></div><div class="">Thanks for the input and I hope you are all healthy and safe, and able to continue your research in these strange times!</div><div class=""><br class=""></div><div class="">cheers,</div></div><div class=""><br class=""></div>-- <br class=""><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr" class=""><div class=""><div dir="ltr" class=""><div class=""><div dir="ltr" class=""><div style="font-family: Tahoma, serif, EmojiFont; font-size: 13px;" class=""><font style="font-family:Helvetica,serif,EmojiFont" class=""><b class="">~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~</b></font></div><div style="font-family: Tahoma, serif, EmojiFont; font-size: 13px;" class=""><font style="font-family:Helvetica,serif,EmojiFont" class=""><b class="">Ingmar de Vries, PhD.</b></font></div><div style="font-family: Tahoma, serif, EmojiFont; font-size: 13px;" class=""><b class=""><font style="font-family:Helvetica,serif,EmojiFont" class="">Postdoc @ CIMeC, University of Trento</font></b><div class=""><div class=""><font style="font-family:Helvetica,serif,EmojiFont" class=""><b class=""><a href="mailto:i.e.j.de.vries@gmail.com" target="_blank" class="">i.e.j.de.vries@gmail.com</a></b></font></div><div class=""><span style="font-family:Helvetica,serif,EmojiFont" class=""><b class="">~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~</b></span></div></div></div></div></div></div></div></div></div></div>
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