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 _______________________________________________ HCP-Users mailing list [email protected] http://lists.humanconnectome.org/mailman/listinfo/hcp-users
