A question regarding the smoothing, Gaussian random field theory requires
that you apply a lot of smoothing for the statistics to be valid. How do
you calculate p-values corrected for multiple comparisons if you don't
smooth the data? Do you use permutation tests?

- Anders

2015-03-14 23:01 GMT+01:00 Glasser, Matthew <[email protected]>:

>  If you don’t want ventricle activations in the task fMRI data, it would
> be good to clean the data with something like ICA+FIX which will remove
> spatially specific structured noise (such as whatever is causing the
> ventricles to light up) prior to fitting the GLM.  There are other stimulus
> correlated artifacts in the task data (uncorrelated artifacts would tend to
> get averaged out in the GLM analysis) such as strong deactivation in
> orbitofrontal regions in the Tongue movement contrast.  Use of ICA+FIX in
> task analysis was looked at some inside the consortium, but I’m not sure if
> there was ever a focus on seeing that stimulus correlated artifacts were
> being removed (vs just seeing how Z-stats changed with cleanup, which they
> don’t much since most of the variance in HCP fMRI timeseries is
> unstructured).
>
>  Also it makes me sad to see you aren’t using the CIFTI data, which are
> substantially more accurately registered across subjects and don’t have the
> unnecessary blurring with white matter and CSF signals (and in 3D across
> sulci and gyri) induced by unconstrained volume-based smoothing as
> misalignment between functional areas.  The volume-based data simply don’t
> allow you to take advantage of the high spatial resolution that the HCP
> data were acquired with like the CIFTI data do, so you’re missing out on
> all the cool new things you can see.
>
>  Peace,
>
>  Matt.
>
>   From: vanessa sochat <[email protected]>
> Date: Saturday, March 14, 2015 at 4:29 PM
> To: "[email protected]" <[email protected]>
> Cc: Russell Poldrack <[email protected]>
> Subject: [HCP-Users] De-activations in "LANGUAGE" Task Contrast Maps
>
>   Hi HCP Users,
>
>  We recently generated group contrast maps using the 500 subjects release
> data, and discovered strong deactivations in the ventricles for the "STORY"
> contrast. We generated the group maps using fsl's randomise:
>
>   randomise -i 4D -o OneSampT -1 -T
>
>
>  and the 4D file was concatenated cope1.nii.gz images from the single
> subject directories, eg:
>
>
>> /MNINonLinear/Results/tfMRI_LANGUAGE/tfMRI_LANGUAGE_hp200_s4_level2vol.feat/cope1.feat/stats/cope1.nii.gz
>
>
>  We have prepared a notebook that visualizes outliers
> <http://nbviewer.ipython.org/github/vsoch/brainmeta/blob/master/image_comparison/notebook/outliers_in_hcp.ipynb#Visualizing-The-Outliers>
> +/- 6 standard deviations from the mean for each of the 86 task/contrasts,
> as well as counting the total number
> <http://nbviewer.ipython.org/github/vsoch/brainmeta/blob/master/image_comparison/notebook/outliers_in_hcp.ipynb#Counting-Outliers-+/--6-Standard-Deviations>
> .
>
>  The outliers that are positive activations (which are beautiful, by the
> way!) are not concerning. Would it be possible to talk about the outliers
> in the ventricles for the following contrasts?
>
>
>    - tfMRI_LANGUAGE_STORY
>    - tfMRI_LANGUAGE_MATH
>    - tfMRI_LANGUAGE_neg_MATH
>    - tfMRI_LANGUAGE_neg_STORY
>
>  We did a similar procedure
> <http://nbviewer.ipython.org/github/vsoch/brainmeta/blob/master/image_comparison/notebook/hcp_language_explore.ipynb>
> for a subset (N=46) of the single subject maps for the STORY contrast (the
> same cope images described above) in case it is helpful.
>
>  Thanks for your help with this.  This dataset is amazing to have for
> meta-analysis research!
>
>  Best,
>
>  Vanessa
>
>  --
> Vanessa Villamia Sochat
> Stanford University
>  (603) 321-0676
>
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-- 
Anders Eklund, PhD

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