available atlases & resources¶

yabplot comes pre-packaged with support for several standard neuroimaging atlases. you can specify these using the atlas string argument in plotting functions.

In [1]:

import yabplot as yab
yab.get_available_resources()

import yabplot as yab yab.get_available_resources()

Out[1]:

{'cortical': ['aal3',
  'aparc',
  'brainnetome',
  'schaefer100',
  'schaefer1000',
  'schaefer200',
  'schaefer300',
  'schaefer400'],
 'subcortical': ['aal3',
  'aal3_nocer',
  'aseg',
  'brainnetome_sc',
  'musus100',
  'musus100_dbn',
  'musus100_tha',
  'tian2020_s1'],
 'tracts': ['hcp1065_medium',
  'hcp1065_small',
  'hcp1065_tiny',
  'xtract_large',
  'xtract_medium',
  'xtract_small',
  'xtract_tiny'],
 'bmesh': ['inflated',
  'midthickness',
  'pial',
  'swm',
  'very_inflated',
  'white'],
 'label': ['nomedialwall']}

cortical parcellations¶

used with yabplot.plot_cortical(atlas='name').

Desikan-Killiany (aparc)¶

the default FreeSurfer parcellation based on gyral and sulcal anatomy.

• key: 'aparc'
• regions: 70 (35 per hemisphere)
• reference: Desikan et al., 2006

In [2]:

atlas = 'aparc'
print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='cortical')[:10])
ax = yab.plot_cortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial'])
ax = ax.set_title(atlas, fontsize=7)

atlas = 'aparc' print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='cortical')[:10]) ax = yab.plot_cortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial']) ax = ax.set_title(atlas, fontsize=7)

first 10 regions: ['L_bankssts', 'L_caudalanteriorcingulate', 'L_caudalmiddlefrontal', 'L_corpuscallosum', 'L_cuneus', 'L_entorhinal', 'L_fusiform', 'L_inferiorparietal', 'L_inferiortemporal', 'L_isthmuscingulate']

Schaefer (2018)¶

functional parcellation based on resting-state fMRI. available in multiple granularities.

• keys: 'schaefer100', 'schaefer200', 'schaefer300', 'schaefer400', 'schaefer1000'
• reference: Schaefer et al., 2018

In [3]:

atlas = 'schaefer400'
print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='cortical')[:10])
ax = yab.plot_cortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial'])
ax = ax.set_title(atlas, fontsize=7)

atlas = 'schaefer400' print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='cortical')[:10]) ax = yab.plot_cortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial']) ax = ax.set_title(atlas, fontsize=7)

first 10 regions: ['7Networks_LH_Vis_1', '7Networks_LH_Vis_2', '7Networks_LH_Vis_3', '7Networks_LH_Vis_4', '7Networks_LH_Vis_5', '7Networks_LH_Vis_6', '7Networks_LH_Vis_7', '7Networks_LH_Vis_8', '7Networks_LH_Vis_9', '7Networks_LH_Vis_10']

Brainnetome (cortical)¶

connectivity-based parcellation using structural and functional magnetic resonance imaging.

• key: 'brainnetome'
• reference: Fan et al., 2016

In [4]:

atlas = 'brainnetome'
print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='cortical')[:10])
ax = yab.plot_cortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial'])
ax = ax.set_title(atlas, fontsize=7)

atlas = 'brainnetome' print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='cortical')[:10]) ax = yab.plot_cortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial']) ax = ax.set_title(atlas, fontsize=7)

first 10 regions: ['A8m_L', 'A8m_R', 'A8dl_L', 'A8dl_R', 'A9l_L', 'A9l_R', 'A6dl_L', 'A6dl_R', 'A6m_L', 'A6m_R']

Automated Anatomical Labeling 3, AAL3 (cortical)¶

macroanatomical parcellation. the cortical subset of the full atlas.

• key: 'aal3'
• reference: Rolls et al., 2020

In [5]:

atlas = 'aal3'
print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='cortical')[:10])
ax = yab.plot_cortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial'])
ax = ax.set_title(atlas, fontsize=7)

atlas = 'aal3' print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='cortical')[:10]) ax = yab.plot_cortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial']) ax = ax.set_title(atlas, fontsize=7)

first 10 regions: ['Precentral_L', 'Precentral_R', 'Frontal_Sup_2_L', 'Frontal_Sup_2_R', 'Frontal_Mid_2_L', 'Frontal_Mid_2_R', 'Frontal_Inf_Oper_L', 'Frontal_Inf_Oper_R', 'Frontal_Inf_Tri_L', 'Frontal_Inf_Tri_R']

subcortical structures¶

used with yabplot.plot_subcortical(atlas='name').

FreeSurfer segmentation (aseg)¶

standard subcortical segmentation including Thalamus, Caudate, Putamen, Pallidum, Hippocampus, Amygdala, and Accumbens.

• key: 'aseg'
• regions: 14 subcortical structures
• reference: Fischl et al., 200200569-X)

In [6]:

atlas = 'aseg'
print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='subcortical')[:10])
ax = yab.plot_subcortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial'])
ax = ax.set_title(atlas, fontsize=7)

atlas = 'aseg' print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='subcortical')[:10]) ax = yab.plot_subcortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial']) ax = ax.set_title(atlas, fontsize=7)

first 10 regions: ['Left-Thalamus', 'Left-Caudate', 'Left-Putamen', 'Left-Pallidum', 'Left-Hippocampus', 'Left-Amygdala', 'Left-Accumbens-area', 'Right-Thalamus', 'Right-Caudate', 'Right-Putamen']

Tian subcortical S1 (2020)¶

functional hierarchical parcellation of the subcortex based on resting-state fMRI. Scale 1 represents the most macroscale networks.

• key: 'tian2020_s1'
• regions: 16 regions
• reference: Tian et al., 2020

In [7]:

atlas = 'tian2020_s1'
print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='subcortical')[:10])
ax = yab.plot_subcortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial'])
ax = ax.set_title(atlas, fontsize=7)

atlas = 'tian2020_s1' print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='subcortical')[:10]) ax = yab.plot_subcortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial']) ax = ax.set_title(atlas, fontsize=7)

first 10 regions: ['HIP-rh', 'AMY-rh', 'pTHA-rh', 'aTHA-rh', 'GP-rh', 'NAc-rh', 'PUT-rh', 'CAU-rh', 'HIP-lh', 'AMY-lh']

MuSus-100¶

multi-modal subcortical structures atlas providing highly detailed segmentations of deep brain nuclei and thalamic subregions.

• keys: 'musus100' (full 100 regions), 'musus100_dbn' (20 deep brain nuclei), 'musus100_tha' (thalamic subregions)
• regions: 20 deep brain nuclei + 80 thalamic subregions
• reference: He, C. et al., 2023

In [8]:

atlas = 'musus100_dbn'
print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='subcortical')[:10])
ax = yab.plot_subcortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial'])
ax = ax.set_title(atlas, fontsize=7)

atlas = 'musus100_dbn' print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='subcortical')[:10]) ax = yab.plot_subcortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial']) ax = ax.set_title(atlas, fontsize=7)

first 10 regions: ['R-Cau', 'L-Cau', 'R-Pu', 'L-Pu', 'R-GPe', 'L-GPe', 'R-NAcc', 'L-NAcc', 'R-SNc', 'L-SNc']

Automated Anatomical Labeling 3, AAL3 (subcortical)¶

macroanatomical parcellation. the subcortical subset includes deep brain structures, the cerebellum, and brainstem.

• keys: 'aal3' or 'aal3_nocer' (without cerebellum)
• regions: 52 subcortical regions + 26 cerebellum subregions
• reference: Rolls et al., 2020

In [9]:

atlas = 'aal3'
print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='subcortical')[:10])
ax = yab.plot_subcortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial'])
ax = ax.set_title(atlas, fontsize=7)

atlas = 'aal3' print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='subcortical')[:10]) ax = yab.plot_subcortical(atlas=atlas, views=['left_lateral', 'superior', 'left_medial']) ax = ax.set_title(atlas, fontsize=7)

first 10 regions: ['Hippocampus_L', 'Hippocampus_R', 'Amygdala_L', 'Amygdala_R', 'Caudate_L', 'Caudate_R', 'Putamen_L', 'Putamen_R', 'Pallidum_L', 'Pallidum_R']

white matter tracts¶

used with yabplot.plot_tracts(atlas='name').

XTRACT standard¶

standardised automated probabilistic tractography protocols derived from the Human Connectome Project. available in multiple downsampled resolutions for performance.

• keys: 'xtract_tiny', 'xtract_small', 'xtract_medium', 'xtract_large'
• regions: 42 major white matter bundles
• reference: Warrington et al., 2020

In [10]:

atlas = 'xtract_large'
print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='tracts')[:10])
ax = yab.plot_tracts(atlas=atlas, orientation_coloring=False, views=['left_lateral', 'superior', 'left_medial'], bmesh='inflated')
ax = ax.set_title(atlas, fontsize=7)

atlas = 'xtract_large' print('first 10 regions:', yab.get_atlas_regions(atlas=atlas, category='tracts')[:10]) ax = yab.plot_tracts(atlas=atlas, orientation_coloring=False, views=['left_lateral', 'superior', 'left_medial'], bmesh='inflated') ax = ax.set_title(atlas, fontsize=7)

first 10 regions: ['AC', 'AF_L', 'AF_R', 'AR_L', 'AR_R', 'ATR_L', 'ATR_R', 'CST_L', 'CST_R', 'Cing_Dorsal_L']

context brain meshes¶

standard topological brain meshes used as the background context for plotting.

keys:

• 'midthickness' (middle of the cortical ribbon) [default]
• 'pial' (outer grey matter surface)
• 'white' (inner white matter surface)
• 'swm' (smoothed white matter)
• 'inflated' (smoothed surface exposing sulci)
• 'very_inflated' (spherical-like expanded surface)

In [11]:

bmeshes = ['midthickness', 'pial', 'white', 'swm', 'inflated', 'very_inflated']
for bmesh in bmeshes:
    ax = yab.plot_cortical(data={}, vminmax=[0, 0], views=['left_lateral', 'superior', 'left_medial'], bmesh=bmesh)
    ax = ax.set_title(bmesh, fontsize=7)

bmeshes = ['midthickness', 'pial', 'white', 'swm', 'inflated', 'very_inflated'] for bmesh in bmeshes: ax = yab.plot_cortical(data={}, vminmax=[0, 0], views=['left_lateral', 'superior', 'left_medial'], bmesh=bmesh) ax = ax.set_title(bmesh, fontsize=7)

In [ ]: