Exploring ConWhAt Atlases

There are four different atlas types in ConWhat, corresponding to the 2 ontology types (Tract-based / Connectivity-Based) and 2 representation types (Volumetric / Streamlinetric).

(More on this schema here)



In [1]:

    
# ConWhAt stuff
from conwhat import VolConnAtlas,StreamConnAtlas,VolTractAtlas,StreamTractAtlas
from conwhat.viz.volume import plot_vol_scatter,plot_vol_and_rois_nilearn

# Neuroimaging stuff
import nibabel as nib
from nilearn.plotting import plot_stat_map,plot_surf_roi

# Viz stuff
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns

# Generic stuff
import glob, numpy as np, pandas as pd, networkx as nx

We'll start with the scale 33 lausanne 2008 volumetric connectivity-based atlas.

Define the atlas name and top-level directory location



In [2]:

    
atlas_dir = '/scratch/hpc3230/Data/conwhat_atlases'
atlas_name = 'CWL2k8Sc33Vol3d100s_v01'

Initialize the atlas class



In [3]:

    
vca = VolConnAtlas(atlas_dir=atlas_dir + '/' + atlas_name,
                   atlas_name=atlas_name)









    



loading file mapping
loading vol bbox
loading connectivity

This atlas object contains various pieces of general information



In [9]:

    
vca.atlas_name









    Out[9]:





'CWL2k8Sc33Vol3d100s_v01'



In [8]:

    
vca.atlas_dir









    Out[8]:





'/scratch/hpc3230/Data/conwhat_atlases/CWL2k8Sc33Vol3d100s_v01'

Information about each atlas entry is contained in the vfms attribute, which returns a pandas dataframe



In [14]:

    
vca.vfms.head()









    Out[14]:







  
    
      
      name
      nii_file
      nii_file_id
      4dvolind
    
  
  
    
      0
      61_to_80
      vismap_grp_62-81_norm.nii.gz
      0
      NaN
    
    
      1
      38_to_55
      vismap_grp_39-56_norm.nii.gz
      1
      NaN
    
    
      2
      28_to_38
      vismap_grp_29-39_norm.nii.gz
      2
      NaN
    
    
      3
      18_to_19
      vismap_grp_19-20_norm.nii.gz
      3
      NaN
    
    
      4
      26_to_55
      vismap_grp_27-56_norm.nii.gz
      4
      NaN

Additionally, connectivity-based atlases also contain a networkx graph object vca.Gnx, which contains information about each connectome edge



In [62]:

    
vca.Gnx.edges[(10,35)]









    Out[62]:





{'attr_dict': {'4dvolind': nan,
  'fullname': 'L_paracentral_to_L_caudate',
  'idx': 1637,
  'name': '10_to_35',
  'nii_file': 'vismap_grp_11-36_norm.nii.gz',
  'nii_file_id': 1637,
  'weight': 50.240000000000002,
  'xmax': 92,
  'xmin': 61,
  'ymax': 167,
  'ymin': 75,
  'zmax': 92,
  'zmin': 62}}

Individual atlas entry nifti images can be grabbed like so



In [144]:

    
img = vca.get_vol_from_vfm(1637)









    



getting atlas entry 1637: image file /scratch/hpc3230/Data/conwhat_atlases/CWL2k8Sc33Vol3d100s_v01/vismap_grp_11-36_norm.nii.gz



In [146]:

    
plot_stat_map(img)









    Out[146]:





<nilearn.plotting.displays.OrthoSlicer at 0x7fb19fada410>

Or alternatively as a 3D scatter plot, along with the x,y,z bounding box



In [155]:

    
vca.bbox.ix[1637]









    Out[155]:





xmin     61
xmax     92
ymin     75
ymax    167
zmin     62
zmax     92
Name: 1637, dtype: int64



In [134]:

    
ax = plot_vol_scatter(vca.get_vol_from_vfm(1),c='r',bg_img='nilearn_destrieux',
                      bg_params={'s': 0.1, 'c':'k'},figsize=(20, 15))

ax.set_xlim([0,200]); ax.set_ylim([0,200]); ax.set_zlim([0,200]);









    



getting atlas entry 1: image file /scratch/hpc3230/Data/conwhat_atlases/CWL2k8Sc33Vol3d100s_v01/vismap_grp_39-56_norm.nii.gz

We can also view the weights matrix like so:



In [38]:

    
fig, ax = plt.subplots(figsize=(16,12))

sns.heatmap(np.log1p(vca.weights),xticklabels=vca.region_labels,
            yticklabels=vca.region_labels,ax=ax);
plt.tight_layout()

The vca object also contains x,y,z bounding boxes for each structure

We also stored additional useful information about the ROIs in the associated parcellation, including cortical/subcortical labels



In [156]:

    
vca.cortex









    Out[156]:





array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.])

...hemisphere labels



In [157]:

    
vca.hemispheres









    Out[157]:





array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.])

...and region mappings to freesurfer's fsaverage brain



In [158]:

    
vca.region_mapping_fsav_lh









    Out[158]:





array([ 24.,  29.,  28., ...,  16.,   7.,   7.])



In [159]:

    
vca.region_mapping_fsav_rh









    Out[159]:





array([ 24.,  29.,  22., ...,   9.,   9.,   9.])

which can be used for, e.g. plotting ROI data on a surface



In [167]:

    
f = '/opt/freesurfer/freesurfer/subjects/fsaverage/surf/lh.inflated'
vtx,tri = nib.freesurfer.read_geometry(f)
plot_surf_roi([vtx,tri],vca.region_mapping_fsav_lh);

	name	nii_file	nii_file_id	4dvolind
0	61_to_80	vismap_grp_62-81_norm.nii.gz	0	NaN
1	38_to_55	vismap_grp_39-56_norm.nii.gz	1	NaN
2	28_to_38	vismap_grp_29-39_norm.nii.gz	2	NaN
3	18_to_19	vismap_grp_19-20_norm.nii.gz	3	NaN
4	26_to_55	vismap_grp_27-56_norm.nii.gz	4	NaN