<div dir="ltr"><div><div>> Many thanks! I removed the invariant features and now the script gives no warnings.<br></div>Great. BTW invariant features are problem during the z-scoring, since you got an error during the classification, I assume you didn't z-score. Depending on a classifier, it can make a big difference, you should consider doing it.<br><br>> There seems to be an algorithm
"GroupClusterThreshold" for evaluation the group level accuracy maps. Is
there any example script of using that algorithm?<br></div><div>We used it in this data paper <a href="http://f1000research.com/articles/4-174/v1">http://f1000research.com/articles/4-174/v1</a> and publish whole analysis pipeline here <a href="https://github.com/psychoinformatics-de/paper-f1000_pandora_data">https://github.com/psychoinformatics-de/paper-f1000_pandora_data</a> and you should also check out this whole thread for more verbose explanation of the code <a href="http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/2015q3/003200.html">http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/2015q3/003200.html</a><br></div><div><br>> Is there any way to evaluate whether the results look reasonable for
individual subjects? I have now just thresholded the accuracy maps with
different thresholds (e.g 80%, 85% and 90%) and viewed the results.<br></div><div>As a quick quality check that is exactly what I would do. You can also use lower threshold and make a mean map accuracy map of all your subjects. As a rule of thumb you should have "blobs" at least in those areas where you have them with GLM. As a sanity check you can also try to predict something easy like rest vs. condition, left vs right button press etc.<br><br></div><div>Best wishes,<br></div><div>Richard<br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Sun, Jan 24, 2016 at 5:43 PM, Maria Hakonen <span dir="ltr"><<a href="mailto:maria.hakonen@gmail.com" target="_blank">maria.hakonen@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">Many thanks! I removed the invariant features and now the script gives no warnings.<div>I have calculated sensitivity maps, mapped them back to the original space and saved them as .nifti files. There seems to be an algorithm "GroupClusterThreshold" for evaluation the group level accuracy maps. Is there any example script of using that algorithm? Is there any way to evaluate whether the results look reasonable for individual subjects? I have now just thresholded the accuracy maps with different thresholds (e.g 80%, 85% and 90%) and viewed the results.</div><span class="HOEnZb"><font color="#888888"><div><br></div><div>-Maria </div></font></span></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div class="gmail_quote">2016-01-23 20:31 GMT+02:00 Richard Dinga <span dir="ltr"><<a href="mailto:dinga92@gmail.com" target="_blank">dinga92@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">I might be wrong, but it sounds like you have invariant features in your data. U can get a better mask or just remove them with remove_invariant_features()<div><div><br><br>On Sat, Jan 23, 2016 at 5:37 PM, Maria Hakonen <<a href="mailto:maria.hakonen@gmail.com" target="_blank">maria.hakonen@gmail.com</a>> wrote:<br>><br>> Hi,<br>><br>> Many thanks for your answers! <br>> I would like to identify brain regions sensitive to speech intelligibility. I have already done this with GLM by comparing responses to blocks of intelligible and unintelligible sentences. However, I would also like to try if MVPA finds some other regions since I have understood that it is more sensitive. Perhaps this could be done by running searchlight analysis on the full brain and then analyzing the clusters as introduced in Etzel <a href="http://et.al" target="_blank">et.al</a>. (2013, i.e. the link in the previous message). <br>> <br>> I tried searchlight but it gives me the following warning:<br>><br>> WARNING: Obtained degenerate data with zero norm for training of <LinearCSVMC>. Scaling of C cannot be done.<br>><br>> I wonder if you have any advice how to solve this problem?<br>><br>> Regards,<br>> Maria<br>><br>> 2016-01-21 17:02 GMT+02:00 Jo Etzel <<a href="mailto:jetzel@wustl.edu" target="_blank">jetzel@wustl.edu</a>>:<br>>><br>>> I quite agree with Nick's "quite tricky": about the only way in which averaging the weights over 18 the cross-validation folds will give you a correct impression of the "important" voxels is if most of the voxels in your ROI have no information at all, and the remaining are uniquely informative (each distinguishes the classes, but not correlated with each other). Needless to say, this scenario is not exactly common for fMRI datasets. (and even more complicated if multiple people are being analyzed.)<br>>><br>>> Searchlights can give a decent reflection of where *local* information occurs, though there are many caveats (to cite myself, see <a href="http://www.ncbi.nlm.nih.gov/pubmed/23558106" target="_blank">http://www.ncbi.nlm.nih.gov/pubmed/23558106</a>).<br>>><br>>> I generally suggest tailoring the analysis to the hypothesis. If you're really interested in the activity in individual voxels, some sort of mass-univariate analysis is probably best. If you're interested in ROIs, ROI-based MVPA can work very well. But trying to interpret *voxels* from a *ROI-based* analysis is problematic at best.<br>>><br>>> Jo<br>>><br>>><br>>><br>>> On 1/21/2016 8:27 AM, Nick Oosterhof wrote:<br>>>><br>>>><br>>>>> On 21 Jan 2016, at 15:18, Maria Hakonen <<a href="mailto:maria.hakonen@gmail.com" target="_blank">maria.hakonen@gmail.com</a>><br>>>>> wrote:<br>>>>><br>>>>> I am working on my first fMRI data and would like to try MVPA<br>>>>> analysis. I have two classes that I have classified with linear<br>>>>> SVM. I would like to determine which voxels contribute most to the<br>>>>> clasifier’s successful discrimination of the classes. As far as<br>>>>> understand, the absolute value of the SVM weights directly reflect<br>>>>> the importance of a feature (voxel) in discriminating the two<br>>>>> classes.<br>>>><br>>>><br>>>> Interpretation of SVM weights is quite tricky, see for example Haufe<br>>>> et al 2015 Neuroimage, doi:10.1016/j.neuroimage.2013.10.067.<br>>>><br>>>> If you want to make inferences about the spatial location of<br>>>> multivariate discrimination, you may want to consider using a<br>>>> searchlight analysis instead.<br>>>><br>>>>> I would like to average the SVM weights across all 18<br>>>>> cross-validation folds for each voxel and wrap the resulting map<br>>>>> into the standard space in order to display a map of the resulting<br>>>>> overlap.<br>>>><br>>>><br>>>> Even if one would be confident that SVM weights were interpretable,<br>>>> why take the absolute value? It would seem that this makes it much<br>>>> more difficult to do any stats or interpret the results. In<br>>>> particular, lack of signal but difference in variance of weights<br>>>> across regions may then yield differences in average absolute<br>>>> values.<br>>><br>>> ><br>>>><br>>>> _______________________________________________<br>>>> Pkg-ExpPsy-PyMVPA mailing list<br>>>> <a href="mailto:Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org" target="_blank">Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org</a><br>>>> <a href="http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa" target="_blank">http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa</a><br>>>><br>>>><br>>> --<br>>> Joset A. Etzel, Ph.D.<br>>> Research Analyst<br>>> Cognitive Control & Psychopathology Lab<br>>> Washington University in St. Louis<br>>> <a href="http://mvpa.blogspot.com/" target="_blank">http://mvpa.blogspot.com/</a><br>>><br>>><br>>> _______________________________________________<br>>> Pkg-ExpPsy-PyMVPA mailing list<br>>> <a href="mailto:Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org" target="_blank">Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org</a><br>>> <a href="http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa" target="_blank">http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa</a><br>><br>><br>><br>> _______________________________________________<br>> Pkg-ExpPsy-PyMVPA mailing list<br>> <a href="mailto:Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org" target="_blank">Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org</a><br>> <a href="http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa" target="_blank">http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa</a><br><br></div></div></div>
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