[pymvpa] Visualization of the sensitivity map

[Maria Hakonen]

Many thanks! I removed the invariant features and now the script gives no
warnings.
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.

-Maria

2016-01-23 20:31 GMT+02:00 Richard Dinga <dinga92 at gmail.com>:

> 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()
>
>
> On Sat, Jan 23, 2016 at 5:37 PM, Maria Hakonen <maria.hakonen at gmail.com>
> wrote:
> >
> > Hi,
> >
> > Many thanks for your answers!
> > 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 et.al. (2013, i.e. the link in the previous message).
>
> >
> > I tried searchlight but it gives me the following warning:
> >
> > WARNING: Obtained degenerate data with zero norm for training of
> <LinearCSVMC>.  Scaling of C cannot be done.
> >
> > I wonder if you have any advice how to solve this problem?
> >
> > Regards,
> > Maria
> >
> > 2016-01-21 17:02 GMT+02:00 Jo Etzel <jetzel at wustl.edu>:
> >>
> >> 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.)
> >>
> >> Searchlights can give a decent reflection of where *local* information
> occurs, though there are many caveats (to cite myself, see
> http://www.ncbi.nlm.nih.gov/pubmed/23558106).
> >>
> >> 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.
> >>
> >> Jo
> >>
> >>
> >>
> >> On 1/21/2016 8:27 AM, Nick Oosterhof wrote:
> >>>
> >>>
> >>>> On 21 Jan 2016, at 15:18, Maria Hakonen <maria.hakonen at gmail.com>
> >>>> wrote:
> >>>>
> >>>> I am working on my first fMRI data and would like to try MVPA
> >>>> analysis. I have two classes that I have classified with linear
> >>>> SVM. I would like to determine which voxels contribute most to the
> >>>> clasifier’s successful discrimination of the classes. As far as
> >>>> understand, the absolute value of the SVM weights directly reflect
> >>>> the importance of a feature (voxel) in discriminating the two
> >>>> classes.
> >>>
> >>>
> >>> Interpretation of SVM weights is quite tricky, see for example Haufe
> >>> et al 2015 Neuroimage, doi:10.1016/j.neuroimage.2013.10.067.
> >>>
> >>> If you want to make inferences about the spatial location of
> >>> multivariate discrimination, you may want to consider using a
> >>> searchlight analysis instead.
> >>>
> >>>> I would like to average the SVM weights across all 18
> >>>> cross-validation folds for each voxel and wrap the resulting map
> >>>> into the standard space in order to display a map of the resulting
> >>>> overlap.
> >>>
> >>>
> >>> Even if one would be confident that SVM weights were interpretable,
> >>> why take the absolute value? It would seem that this makes it much
> >>> more difficult to do any stats or interpret the results. In
> >>> particular, lack of signal but difference in variance of weights
> >>> across regions may then yield differences in average absolute
> >>> values.
> >>
> >> >
> >>>
> >>> _______________________________________________
> >>> Pkg-ExpPsy-PyMVPA mailing list
> >>> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
> >>>
> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
> >>>
> >>>
> >> --
> >> Joset A. Etzel, Ph.D.
> >> Research Analyst
> >> Cognitive Control & Psychopathology Lab
> >> Washington University in St. Louis
> >> http://mvpa.blogspot.com/
> >>
> >>
> >> _______________________________________________
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> >> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
> >>
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> >
> >
> >
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> >
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