Re: [HCP-Users] De-activations in "LANGUAGE" Task Contrast Maps

[Glasser, Matthew]

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 <vsoc...@stanford.edu> Date: Saturday, March 14, 2015 at 4:29 PM To: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org> Cc: Russell Poldrack <poldr...@stanford.edu> 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 +/- 6 standard deviations from the mean for each of the 86 task/contrasts, as well as counting the total number. 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 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 HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users   The materials in this message are private and may contain Protected Healthcare Information or other information of a sensitive nature. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited. If you have received this email in error, please immediately notify the sender via telephone or return mail. _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users