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Brain picture
Beauchamp Lab


Glasser et al. (Nature 2016) used the HCP dataset (including resting-state functional connectivity, T1 and T2 images) to parcellate the cerebral cortex into 180 areas per hemisphere, creating an atlas known as the HCP MMP (multimodal parcellation) 1.0 atlas. This page describes one technique to use this atlas in AFNI/SUMA. As an overview, a cortical surface model is created for each subject and registered to the FreeSurfer template (fsaverage). Benson et al. ( created an HCP-aligned fsaverage surface. The correspondence between all fsaverage-aligned surfaces means that the Benson et al. label file can be applied to any individual's surface. In the example below, this is illustrated with the Colin N27 brain, a single subject dataset that is a common reference. While all alignment and labeling is done on the surface, in many cases it is desirable to apply the resulting labels to EPI data such as task-based fMRI or resting-state fMRI. This can be done either by mapping EPI data to the surface, or mapping labels from the surface back to the volume. Steps for the latter method are provided below. Coalson et al. ( extensively documents the inaccuracies inherent in volume-based intersubject alignment methods and their inferiority to the surface-based methods described on this page.

Processing Steps

Create standardized cortical surface models (std.141) for your subject(s). See Cortical Surface models overview for details. Create or obtain a copy of the HCP Atlas in standard space. Here are instructions from Kate Mills:

Copy the HCP atlas converted into std.141 FreeSurfer template brain space into the subject directory. There are two files, one for each hemisphere. For instance, if we would like to see the labels on the N27 standard brain,

 cd /Volumes/data/BCM/N27/suma_MNI_N27
 cp /Volumes/data/scripts/std.141.?h.HCP.annot.niml.dset .

start Suma and load the annotation files. Make sure you load the std.141 brains or the labels will be incorrect.

 suma -spec std.141.MNI_N27_both.spec &
 DriveSuma -com surf_cont -load_dset std.141.lh.HCP.annot.niml.dset -surf_label lh.smoothwm.gii  -view_surf_cont y -switch_cmap ROI_i256

Different values/colors on the surface correspond to each atlas label.

Volume Processing Steps

It can also be useful to have the atlas labels in the volume, since this is the native space of the MRI data. This can be done with the AFNI program 3dSurf2Vol. Here is a sample command line

 foreach hemi (lh rh)
 3dSurf2Vol -spec std.141.MNI_N27_{$hemi}.spec -surf_A smoothwm -surf_B pial -grid_parent T1.nii \
 -sdata std.141.{$hemi}.HCP.annot.niml.dset -map_func mode -f_steps 10 -prefix HCP.volume_{$hemi}.nii -sv T1.nii
 3dcalc -datum byte -prefix HCP.volume_both.nii -a HCP.volume_lh.nii -b HCP.volume_rh.nii -expr "max(a,b)"

The max operation is used to combine hemispheres in case any voxels overlap between the two hemispheres.

3drefit -cmap INT_CMAP HCP.volume_both.nii 
3drefit -labeltable /Volumes/data/BCM/HCP_Atlas/ HCP.volume_both.nii 

The volume dataset has the HCP label at each location.

Automated Viewing

To make viewing easier, DriveSuma can be used to automate loading of the files.

set h = /Volumes/data/scripts
set p = `pwd` 
afni -R -niml &
suma -spec std.141.MNI_N27_both.spec -sv T1.nii &
DriveSuma -com surf_cont -load_dset std.141.lh.sulc.niml.dset -surf_label lh.smoothwm.gii  -view_surf_cont y -load_cmap {$h}/nice.1D.cmap -Dim 0.6
DriveSuma -com surf_cont -load_dset std.141.rh.sulc.niml.dsett -surf_label rh.smoothwm.gii  -view_surf_cont y -switch_cmap nice.1D -Dim 0.6
DriveSuma -com viewer_cont -key b  -1_only n
DriveSuma -com viewer_cont -key F3
DriveSuma -com viewer_cont -load_view  {$h}/nice.niml.vvs -com surf_cont -switch_surf lh.inf_200.gii
DriveSuma -com viewer_cont -key t
DriveSuma -com surf_cont -1_only n
plugout_drive -com 'SWITCH_OVERLAY HCP.volume_both.nii' -com 'SEE_OVERLAY +' -quit

Right click on the AFNI color bar and choose the "ROI_i256" color bar for optimal viewing.