mindboggle.mio package¶

Submodules¶

mindboggle.mio.colors module¶

Colormap-related functions

Authors:

Arno Klein, 2016 (arno@mindboggle.info) http://binarybottle.com

distinguishable_colors(ncolors, backgrounds=[[0, 0, 0], [1, 1, 1]], save_csv=True, plot_colormap=True, verbose=True, out_dir='.')¶

Create a colormap of perceptually distinguishable colors.

This program is a Python program based on Tim Holy’s 2010-2011 BSD-licensed Matlab program “distinguishable_colors.m” (https://www.mathworks.com/matlabcentral/fileexchange/

29702-generate-maximally-perceptually-distinct-colors):

“This function generates a set of colors which are distinguishable by reference to the “Lab” color space, which more closely matches human color perception than RGB. Given an initial large list of possible RGB colors, it iteratively chooses the entry in the list that is farthest (in Lab space) from all previously-chosen entries. While this “greedy” algorithm does not yield a global maximum, it is simple and efficient. Moreover, the sequence of colors is consistent no matter how many you request, which facilitates the users’ ability to learn the color order and avoids major changes in the appearance of plots when adding or removing lines.”

Parameters:	ncolors (integer) – number of colors for the colormap backgrounds (list of list(s) of 3 elements between 0 and 1) – rgb background colors to initialize and distinguish from save_csv (Boolean) – save colormap as csv file? plot_colormap (Boolean) – plot colormap as horizontal bar chart? verbose (Boolean) – print to stdout?
Returns:	colors – rgb colormap
Return type:	numpy ndarray of ndarrays of 3 floats between 0 and 1

Examples

>>> from mindboggle.mio.colors import distinguishable_colors
>>> import numpy as np
>>> ncolors = 31
>>> backgrounds = [[0,0,0],[1,1,1]]
>>> save_csv = False
>>> plot_colormap = False
>>> verbose = False
>>> colors = distinguishable_colors(ncolors, backgrounds,
...     save_csv, plot_colormap, verbose)
>>> np.allclose(colors[0], [ 0.62068966,  0.06896552,  1.        ])
True
>>> np.allclose(colors[1], [ 0.       ,  0.5862069,  0.       ])
True
>>> np.allclose(colors[2], [ 0.75862069,  0.20689655,  0.        ])
True

group_colors(colormap, colormap_name, description='', adjacency_matrix=[], IDs=[], names=[], groups=[], save_text_files=True, plot_colors=True, plot_graphs=True, out_dir='.', verbose=True)¶

This greedy algoritm reorders a colormap so that labels assigned to the same group have more similar colors, but within a group (usually of adjacent labels), the colors are reordered so that adjacent labels have dissimilar colors:

Convert colormap to Lab color space which better represents human perception.
Load a binary (or weighted) adjacency matrix, where each row or column represents a label, and each value signifies whether (or the degree to which) a given pair of labels are adjacent. If a string (file) is provided instead of a numpy ndarray:

column 0 = label “ID” number column 1 = label “name” column 2 = “group” number (each label is assigned to a group) columns 3… = label adjacency matrix
Sort labels by decreasing number of adjacent labels (adjacency sum).
Order label groups by decreasing maximum adjacency sum.
Create a similarity matrix for pairs of colors.
Sort colors by decreasing perceptual difference from all other colors.
For each label group:

7.1. Select unpicked colors for group that are similar to the first

unpicked color (unpicked colors were sorted above by decreasing perceptual difference from all other colors).

7.2. Reorder subgraph colors according to label adjacency sum

(decreasing number of adjacent labels).
Assign new colors.

For plotting graphs and colormap:

Convert the matrix to a graph, where each node represents a label and each edge represents the adjacency value between connected nodes.
Break up the graph into subgraphs, where each subgraph contains labels assigned the same group number (which usually means they are adjacent).
Plot the colormap and colored sub/graphs.

NOTE: Requires pydotplus

Parameters:	colormap (string or numpy ndarray of ndarrays of 3 floats between 0 and 1) – csv file containing rgb colormap, or colormap array colormap_name (string) – name of colormap description (string) – description of colormap adjacency_matrix (string or NxN numpy ndarray (N = number of labels)) – csv file containing label adjacency matrix or matrix itself IDs (list of integers) – label ID numbers names (list of strings) – label names groups (list of integers) – label group numbers (one per label) save_text_files (Boolean) – save colormap as csv and json files? plot_colors (Boolean) – plot colormap as horizontal bar chart? plot_graphs (Boolean) – plot colormap as graphs? out_dir (string) – output directory path verbose (Boolean) – print to stdout?
Returns:	colors – rgb colormap
Return type:	numpy ndarray of ndarrays of 3 floats between 0 and 1

Examples

>>> # Get colormap:
>>> from mindboggle.mio.colors import distinguishable_colors
>>> import numpy as np
>>> colormap = distinguishable_colors(ncolors=31,
...     backgrounds=[[0,0,0],[1,1,1]],
...     save_csv=False, plot_colormap=False, verbose=False)
>>> # Get adjacency matrix:
>>> from mindboggle.mio.colors import label_adjacency_matrix
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> label_file = fetch_data(urls['left_manual_labels'], '', '.vtk')
>>> IDs, adjacency_matrix, output_table = label_adjacency_matrix(label_file,
...     ignore_values=[-1, 0], add_value=0, save_table=False,
...     output_format='', verbose=False)
>>> adjacency_matrix = adjacency_matrix.values
>>> adjacency_matrix = adjacency_matrix[:, 1::]
>>> # Reorganize colormap:
>>> from mindboggle.mio.colors import group_colors
>>> from mindboggle.mio.labels import DKTprotocol
>>> dkt = DKTprotocol()
>>> colormap_name = "DKT31colormap"
>>> description = "Colormap for DKT31 human brain cortical labels"
>>> save_text_files = True
>>> plot_colors = False
>>> plot_graphs = False
>>> out_dir = '.'
>>> verbose = False
>>> #IDs = dkt.DKT31_numbers
>>> names = dkt.DKT31_names #dkt.left_cerebrum_cortex_DKT31_names
>>> groups = dkt.DKT31_groups
>>> colors = group_colors(colormap, colormap_name, description,
...     adjacency_matrix, IDs, names, groups,
...     save_text_files, plot_colors, plot_graphs, out_dir, verbose)
>>> np.allclose(colors[0], [0.7586206896551724, 0.20689655172413793, 0.0])
True
>>> np.allclose(colors[1], [0.48275862068965514, 0.4482758620689655, 0.48275862068965514])
True
>>> np.allclose(colors[2], [0.3448275862068966, 0.3103448275862069, 0.034482758620689655])
True
>>> np.allclose(colors[-1], [0.7931034482758621, 0.9655172413793103, 0.7931034482758621])
True

No groups / subgraphs:

>>> groups = []
>>> colors = group_colors(colormap, colormap_name, description,
...     adjacency_matrix, IDs, names, groups,
...     save_text_files, plot_colors, plot_graphs, out_dir, verbose)
>>> np.allclose(colors[0], [0.5172413793103449, 0.8275862068965517, 1.0])
True
>>> np.allclose(colors[1], [0.13793103448275862, 0.0, 0.24137931034482757])
True
>>> np.allclose(colors[2], [0.3793103448275862, 0.27586206896551724, 0.48275862068965514])
True
>>> np.allclose(colors[-1], [0.6206896551724138, 0.48275862068965514, 0.3448275862068966])
True

label_adjacency_matrix(label_file, ignore_values=[-1, 999], add_value=0, save_table=True, output_format='csv', verbose=True, out_dir='.')¶

Extract surface or volume label boundaries, find unique label pairs, and write adjacency matrix (useful for constructing a colormap).

Each row of the (upper triangular) adjacency matrix corresponds to an index to a unique label, where each column has a 1 if the label indexed by that column is adjacent to the label indexed by the row.

Parameters:

label_file (string) – path to VTK surface file or nibabel-readable volume file with labels
ignore_values (list of integers) – labels to ignore
add_value (integer) – value to add to labels
matrix (pandas dataframe) – adjacency matrix
save_table (Boolean) – output table file?
output_format (string) – format of adjacency table file name (currently only ‘csv’)
verbose (Boolean) – print to stdout?

Returns:

labels (list) – label numbers
matrix (pandas DataFrame) – adjacency matrix
output_table (string) – adjacency table file name

Examples

>>> from mindboggle.mio.colors import label_adjacency_matrix
>>> from mindboggle.mio.fetch_data import prep_tests
>>> import numpy as np
>>> urls, fetch_data = prep_tests()
>>> ignore_values = [-1, 0]
>>> add_value = 0
>>> save_table = False
>>> output_format = 'csv'
>>> verbose = False
>>> label_file = fetch_data(urls['left_manual_labels'], '', '.vtk')
>>> labels, matrix, output_table = label_adjacency_matrix(label_file,
...     ignore_values, add_value, save_table, output_format, verbose)
>>> out = matrix.lookup([20,21,22,23,24,25,26,27,28,29],
...               [35,35,35,35,35,35,35,35,35,35])
>>> np.allclose(out, [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  1.])
True

>>> label_file = fetch_data(urls['freesurfer_labels'], '', '.nii.gz')
>>> labels, matrix, output_table = label_adjacency_matrix(label_file,
...     ignore_values, add_value, save_table, output_format, verbose)
>>> out = matrix.lookup([4,5,7,8,10,11,12,13,14,15], [4,4,4,4,4,4,4,4,4,4])
>>> np.allclose(out, [ 1.,  1.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.])
True

viridis_colormap()¶

https://github.com/BIDS/colormap/blob/master/colormaps.py

Returns:	viridis – viridis colormap
Return type:	list of lists of three floats

write_json_colormap(colormap, label_numbers, label_names=[], colormap_file='', colormap_name='', description='')¶

Write colormap to json format.

Parameters:	colormap (numpy ndarray or list of lists of string and floats) – label, 1, red, green, blue label_names (list of strings) – label names label_numbers (list of integers) – label numbers colormap_file (string) – output json file name colormap_name (string) – name of colormap description (string) – description of colormap

Examples

>>> from mindboggle.mio.colors import write_xml_colormap
>>> from mindboggle.mio.labels import DKTprotocol
>>> import numpy as np
>>> dkt = DKTprotocol()
>>> colormap = dkt.colormap_normalized
>>> colormap = [[x[2], x[3], x[4]] for x in colormap]
>>> label_numbers = dkt.label_numbers
>>> label_names = dkt.label_names
>>> colormap_file = ''
>>> colormap_name = "DKT31colormap"
>>> description = "Colormap for DKT31 human brain cortical labels"
>>> np.allclose(colormap[0], [0.803921568627451, 0.24313725490196078, 0.3058823529411765])
True
>>> write_json_colormap(colormap, label_numbers, label_names,
...     colormap_file, colormap_name, description)

write_xml_colormap(colormap, label_numbers, colormap_file='', colormap_name='')¶

Write colormap to xml format.

Parameters:	colormap (numpy ndarray or list of lists of string and floats) – label, 1, red, green, blue label_numbers (list of integers) – label numbers colormap_file (string) – output xml file name colormap_name (string) – name of colormap

Examples

>>> from mindboggle.mio.colors import write_xml_colormap
>>> from mindboggle.mio.labels import DKTprotocol
>>> import numpy as np
>>> dkt = DKTprotocol()
>>> colormap = dkt.colormap_normalized
>>> colormap = [[x[2], x[3], x[4]] for x in colormap]
>>> label_numbers = dkt.label_numbers
>>> colormap_file = ''
>>> colormap_name = 'DKT31colormap'
>>> np.allclose(colormap[0], [0.803921568627451, 0.24313725490196078, 0.3058823529411765])
True
>>> write_xml_colormap(colormap, label_numbers, colormap_file,
...     colormap_name)

mindboggle.mio.convert_volumes module¶

Functions for reading and writing nifti volume files.

Authors:

Arno Klein, 2012-2016 (arno@mindboggle.info) http://binarybottle.com

convert2nii(input_file, reference_file, output_file='', interp='continuous')¶

Convert volume from the input file format to the output file format.

If output_file is empty, reslice to nifti format using nibabel and scipy.ndimage.affine_transform, after nilearn.image.resample_img:

from nilearn.image import resample_img
resliced = resample_img(input_file, target_affine=xfm2,
                        target_shape=dim2,
                        interpolation=interp).get_data()

Example use: Convert FreeSurfer ‘unconformed’ .mgz file to nifti.

Parameters:	input_file (string) – input file name reference_file (string) – target file name output_file (string) – name of output file interp (string) – interpolation method: ‘continuous’ (default) or ‘nearest’
Returns:	output_file – name of output file
Return type:	string

Examples

>>> from mindboggle.mio.convert_volumes import convert2nii
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_file = fetch_data(urls['freesurfer_orig_mgz'], '', '.mgz')
>>> reference_file = fetch_data(urls['freesurfer_segmentation'], '', '.nii.gz')
>>> output_file = 'convert2nii.nii.gz'
>>> interp = 'nearest'
>>> output_file = convert2nii(input_file, reference_file, output_file,
...                           interp)

View nifti file (skip test):

>>> from mindboggle.mio.plots import plot_volumes
>>> plot_volumes(output_file) # doctest: +SKIP

xyz2nii(input_xyz_file, output_nii_file='', origin=[], pad=10)¶

Convert [x,y,z] coordinate file to nifti (nii.gz) volume file.

Parameters:	input_xyz_file (string) – input [x,y,z] coordinate text file output_nii_file (string) – output nifti (nii.gz) volume file origin (list of floats) – [x,y,z] coordinates for origin pad (integer) – number of voxels to pad input coordinates in x, y, and z directions
Returns:	output_nii_file – output nifti (nii.gz) volume file
Return type:	string

Examples

>>> from mindboggle.mio.convert_volumes import xyz2nii
>>> input_xyz_file = 'face.xyz.txt' # doctest: +SKIP
>>> origin = []
>>> pad = 10
>>> output_nii_file = 'xyz2nii.nii.gz'
>>> xyz2nii(input_xyz_file) # doctest: +SKIP

View nifti file (skip test):

>>> from mindboggle.mio.plots import plot_volumes
>>> plot_volumes('xyz.nii.gz') # doctest: +SKIP

mindboggle.mio.fetch_data module¶

Functions for fetching data from a URL or from third party software.

Authors:

Arno Klein, 2013-2016 (arno@mindboggle.info) http://binarybottle.com

cache_hashes()¶

Hashes to verify retrieved data cached by the Mindboggle software.

Data include atlases and templates required for registration and labeling. See fetch_hash() to create new hashes.

Returns:	hashes – dictionary of data file names and hashes for those files
Return type:	dictionary

fetch_ants_data(segmented_file, use_ants_transforms=True)¶

Fetch antsCorticalThickness.sh output.

The input argument “segmented_file” is one of the relevant antsCorticalThickness.sh output files called by Mindboggle (assume path and PREFIX=”ants”):

ants_subjects/subject1/antsBrainExtractionMask.nii.gz ants_subjects/subject1/antsBrainSegmentation.nii.gz ants_subjects/subject1/antsSubjectToTemplate0GenericAffine.mat ants_subjects/subject1/antsSubjectToTemplate1Warp.nii.gz ants_subjects/subject1/antsTemplateToSubject0Warp.nii.gz ants_subjects/subject1/antsTemplateToSubject1GenericAffine.mat

The existence of the transform files are checked only if use_ants_transforms == True.

Parameters:

segmented_file (string) – full path to a subject’s antsCorticalThickness.sh segmented file
use_ants_transforms (bool) – include antsCorticalThickness.sh-generated transforms?

Returns:

mask (string) – antsBrainExtraction.sh brain volume mask for extracting brain volume
segments (string) – Atropos-segmented brain volume
affine_subject2template (string) – subject to template affine transform (antsRegistration)
warp_subject2template (string) – subject to template nonlinear transform (antsRegistration)
affine_template2subject (string) – template to subject affine transform (antsRegistration)
warp_template2subject (string) – template to subject nonlinear transform (antsRegistration)

Examples

>>> from mindboggle.mio.fetch_data import fetch_ants_data
>>> segmented_file = 'ants/OASIS-TRT-20-1/tmpBrainSegmentation.nii.gz'
>>> use_ants_transforms = True
>>> m, s, a_s2t, w_s2t, a_t2s, w_t2s = fetch_ants_data(segmented_file,
...     use_ants_transforms) # doctest: +SKIP

fetch_check_data(data_file, url, hashes, cache_directory='', append='', verbose=False)¶

Get data file through a URL call and check its hash:

Check hash table for data file name.

Check hash subdirectory within cache directory for data file.

If data file not in cache, download, compute hash, and verify hash.

If hash correct, save file (+ append); otherwise, raise an error.

Parameters:	data_file (string) – name of file (not the full path) url (string) – URL for data file hashes (dictionary) – file names and md5 hashes (if empty, simply download file from url) cache_directory (string) – cache directory (full path) append (string) – append to output file (ex: ‘.nii.gz’) verbose (bool) – print statements?
Returns:	data_path – data file name (full path)
Return type:	string

Examples

>>> from mindboggle.mio.fetch_data import fetch_check_data
>>> from mindboggle.mio.fetch_data import cache_hashes
>>> # osf.io URL for OASIS-30_Atropos_template_to_MNI152_affine.txt
>>> data_file = 'OASIS-30_Atropos_template_to_MNI152_affine.txt'
>>> url = 'https://osf.io/ufydw/?action=download&version=1'
>>> hashes = cache_hashes()
>>> cache_directory = ''
>>> append = ''
>>> verbose = False
>>> data_path = fetch_check_data(data_file, url, hashes, cache_directory,
...                              append, verbose) # doctest: +SKIP

fetch_data(url, output_file='', append='')¶

Download file from a URL to a specified or a temporary file.

Optionally append to file name.

Parameters:	url (string) – URL for data file output_file (string) – name of output file (full path) append (string) – append to output file (ex: ‘.nii.gz’)
Returns:	output_file – name of output file (full path)
Return type:	string

Examples

>>> from mindboggle.mio.fetch_data import fetch_data, fetch_hash
>>> output_file = ''
>>> append = ''
>>> # osf.io URL for OASIS-30_Atropos_template_to_MNI152_affine.txt
>>> url = 'https://osf.io/ufydw/?action=download&version=1'
>>> output_file = fetch_data(url, output_file, append)
>>> fetch_hash(output_file)
'f36e3d5d99f7c4a9bb70e2494ed7340b'

fetch_hash(data_file)¶

Get hash of data file.

Parameters:	data_file (string) – data file name
Returns:	hash – hash of data file
Return type:	string

Examples

>>> from mindboggle.mio.fetch_data import fetch_hash, fetch_data
>>> # osf.io URL for OASIS-30_Atropos_template_to_MNI152_affine.txt
>>> url = 'https://osf.io/ufydw/?action=download&version=1'
>>> data_file = fetch_data(url)
>>> fetch_hash(data_file)
'f36e3d5d99f7c4a9bb70e2494ed7340b'

prep_tests()¶

Prepare to fetch data in docstring tests.

Returns:	urls (dictionary) – dictionary of names and urls for data files fetch_data (function) – fetch_data() function

Examples

>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> urls['left_mean_curvature']  # shapes/left_cortical_surface/mean_curvature.vtk
'https://osf.io/2q7hb/?action=download&version=1'

test_urls()¶

URLs corresponding to Mindboggle test (example output) data.

Returns:	urls – dictionary of names and urls for data files
Return type:	dictionary

Examples

>>> from mindboggle.mio.fetch_data import test_urls
>>> urls = test_urls()
>>> urls['left_mean_curvature']  # shapes/left_cortical_surface/mean_curvature.vtk
'https://osf.io/2q7hb/?action=download&version=1'

mindboggle.mio.labels module¶

Numbers, names, and colormaps for the DKT and FreeSurfer labeling protocols¶

The DKTprotocol class provides variables related to the Desikan-Killiany-Tourville (DKT) human brain cortical labeling protocol. For more information, see http://mindboggle.info/data/ and the article:

http://www.frontiersin.org/Brain_Imaging_Methods/10.3389/fnins.2012.00171/full “101 labeled brain images and a consistent human cortical labeling protocol” Arno Klein, Jason Tourville. Frontiers in Brain Imaging Methods. 6:171. DOI: 10.3389/fnins.2012.00171

See http://mindboggle.info/faq/labels.html for additional information.

The return_numbers_names_colors() and extract_numbers_names_colors() functions return numbers, names, and colors representing anatomical brain regions from FreeSurfer’s FreeSurferColorLUT.txt lookup table file.

Authors:

Jason Tourville, 2011-2012 (jtour@bu.edu)
Arno Klein, 2011-2015 (arno@mindboggle.info) http://binarybottle.com

class DKTprotocol¶

Bases: object

Variables related to the Desikan-Killiany-Tourville labeling protocol.

For more information about the Desikan-Killiany-Tourville (DKT) human brain cortical labeling protocol, see http://mindboggle.info/data/ and the article:

http://www.frontiersin.org/Brain_Imaging_Methods/10.3389/fnins.2012.00171/full “101 labeled brain images and a consistent human cortical labeling protocol” Arno Klein, Jason Tourville. Frontiers in Brain Imaging Methods. 6:171. DOI: 10.3389/fnins.2012.00171

Returns:

[left, right]_cerebrum_cortex_[DKT31_][numbers, names, colors]
[left, right]_ventricle_[numbers, names, colors]
medial_ventricle_[numbers, names, colors]
[left, right]_cerebrum_noncortex_[numbers, names, colors]
medial_cerebrum_noncortex_[numbers, names, colors]
[left, right]_cerebellum_cortex_[numbers, names, colors]
[left, right]_cerebellum_noncortex_[numbers, names, colors]
medial_cerebellum_noncortex_[numbers, names, colors]
brainstem_[numbers, names, colors]
extra_[numbers, names, colors]
misc_[numbers, names, colors]
ventricle_[numbers, names, colors]
cerebrum_cortex_[numbers, names, colors]
cerebrum_noncortex_[numbers, names, colors]
[left, right]_cerebrum_[numbers, names, colors]
cerebrum_[numbers, names, colors]
[left, right]_cerebellum_[numbers, names, colors]
cerebellum_cortex_[numbers, names, colors]
cerebellum_noncortex_[numbers, names, colors]
cerebellum_[numbers, names, colors]
label_[numbers, names, colors]
colormap (list of lists)
colormap_normalized (list of lists)
sulcus_[names[_abbr], numbers]
unique_sulcus_label_pairs (list of unique pairs of integers) – unique label pairs corresponding to label boundaries / sulcus / fundus
[left_, right_]sulcus_label_pair_lists (list of two lists of lists of integer pairs) – list containing left and/or right lists, each with multiple lists of integer pairs corresponding to label boundaries / sulcus / fundus

Examples

>>> from mindboggle.mio.labels import DKTprotocol
>>> dkt = DKTprotocol()
>>> dkt.left_cerebrum_names[0:3]
['Left-Cerebral-Cortex', 'Left-Insula', 'Left-Operculum']
>>> dkt.left_cerebrum_numbers[0:10]
[3, 19, 20, 1000, 1001, 1002, 1003, 1005, 1006, 1007]
>>> dkt.left_cerebrum_colors[0]
[205, 62, 78]

DKT31_groups = [6, 1, 4, 6, 3, 5, 3, 6, 4, 1, 4, 1, 3, 6, 2, 1, 1, 1, 5, 5, 6, 1, 5, 6, 1, 1, 5, 3, 5, 3, 1]¶

DKT31_names = ['caudal anterior cingulate', 'caudal middle frontal', 'cuneus', 'entorhinal', 'fusiform', 'inferior parietal', 'inferior temporal', 'isthmus cingulate', 'lateral occipital', 'lateral orbitofrontal', 'lingual', 'medial orbitofrontal', 'middle temporal', 'parahippocampal', 'paracentral', 'pars opercularis', 'pars orbitalis', 'pars triangularis', 'pericalcarine', 'postcentral', 'posterior cingulate', 'precentral', 'precuneus', 'rostral anterior cingulate', 'rostral middle frontal', 'superior frontal', 'superior parietal', 'superior temporal', 'supramarginal', 'transverse temporal', 'insula']¶

DKT31_numbers = [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34, 35]¶

brainstem_colors = [[119, 159, 176], [119, 159, 176], [119, 0, 176], [119, 100, 176], [119, 200, 176], [119, 159, 100], [119, 159, 200]]¶

brainstem_list = [16, 170, 171, 172, 173, 174, 175]¶

brainstem_names = ['Brain-Stem', 'brainstem', 'DCG', 'Vermis', 'Midbrain', 'Pons', 'Medulla']¶

brainstem_numbers = [16, 170, 171, 172, 173, 174, 175]¶

cerebellum_colors = [[0, 148, 0], [230, 148, 34], [0, 148, 0], [230, 148, 34], [220, 248, 164], [220, 248, 164]]¶

cerebellum_cortex_colors = [[0, 148, 0], [230, 148, 34], [0, 148, 0], [230, 148, 34]]¶

cerebellum_cortex_names = ['Left-Cerebellum-Exterior', 'Left-Cerebellum-Cortex', 'Right-Cerebellum-Exterior', 'Right-Cerebellum-Cortex']¶

cerebellum_cortex_numbers = [6, 8, 45, 47]¶

cerebellum_names = ['Left-Cerebellum-Exterior', 'Left-Cerebellum-Cortex', 'Right-Cerebellum-Exterior', 'Right-Cerebellum-Cortex', 'Left-Cerebellum-White-Matter', 'Right-Cerebellum-White-Matter']¶

cerebellum_noncortex_colors = [[220, 248, 164], [220, 248, 164]]¶

cerebellum_noncortex_names = ['Left-Cerebellum-White-Matter', 'Right-Cerebellum-White-Matter']¶

cerebellum_noncortex_numbers = [7, 46]¶

cerebellum_numbers = [6, 8, 45, 47, 7, 46]¶

cerebrum_colors = [[205, 62, 78], [80, 196, 98], [60, 58, 210], [25, 5, 25], [25, 100, 40], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [100, 0, 100], [70, 70, 70], [150, 150, 200], [255, 192, 32], [245, 245, 245], [120, 18, 134], [196, 58, 250], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [12, 48, 255], [220, 216, 20], [103, 255, 255], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [160, 32, 240], [0, 200, 200], [255, 148, 10], [205, 10, 125], [124, 140, 178], [125, 140, 178], [126, 140, 178], [127, 140, 178], [124, 141, 178], [124, 142, 178], [124, 143, 178], [124, 144, 178], [124, 140, 179], [238, 59, 59], [62, 10, 205], [17, 85, 137], [119, 187, 103], [204, 68, 35], [204, 0, 254], [221, 187, 16], [153, 221, 239], [51, 17, 18], [0, 119, 86], [20, 100, 201], [120, 70, 50], [230, 250, 230], [230, 155, 215], [130, 155, 95], [155, 230, 255], [135, 185, 205], [35, 235, 155], [35, 235, 245], [75, 35, 115], [35, 195, 35], [75, 215, 135], [115, 235, 115], [235, 225, 115], [220, 180, 205], [30, 115, 115], [55, 220, 180], [95, 155, 205], [235, 35, 195], [195, 35, 195], [35, 75, 115], [235, 155, 205], [35, 195, 235], [135, 155, 195], [35, 235, 235], [35, 75, 35], [195, 235, 35], [95, 115, 75], [175, 235, 115], [180, 205, 130], [235, 35, 95], [235, 75, 115], [115, 35, 35], [175, 95, 235], [155, 255, 155], [185, 185, 185], [105, 105, 55], [254, 191, 31], [20, 30, 40], [205, 62, 78], [80, 196, 98], [60, 58, 210], [25, 5, 25], [25, 100, 40], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [100, 0, 100], [70, 70, 70], [150, 150, 200], [255, 192, 32], [0, 225, 0], [120, 18, 134], [196, 58, 250], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [13, 48, 255], [220, 216, 20], [103, 255, 255], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [160, 32, 240], [0, 200, 221], [255, 148, 10], [205, 10, 125], [124, 140, 178], [125, 140, 178], [126, 140, 178], [127, 140, 178], [124, 141, 178], [124, 142, 178], [124, 143, 178], [124, 144, 178], [124, 140, 179], [238, 59, 59], [62, 10, 205], [17, 85, 136], [119, 187, 102], [204, 68, 34], [204, 0, 255], [221, 187, 17], [153, 221, 238], [51, 17, 17], [0, 119, 85], [20, 100, 200], [120, 70, 50], [230, 250, 230], [230, 155, 215], [130, 155, 95], [155, 230, 255], [135, 185, 205], [35, 235, 155], [35, 235, 245], [75, 35, 115], [35, 195, 35], [75, 215, 135], [115, 235, 115], [235, 225, 115], [220, 180, 205], [30, 115, 115], [55, 220, 180], [95, 155, 205], [235, 35, 195], [195, 35, 195], [35, 75, 115], [235, 155, 205], [35, 195, 235], [135, 155, 195], [35, 235, 235], [35, 75, 35], [195, 235, 35], [95, 115, 75], [175, 235, 115], [180, 205, 130], [235, 35, 95], [235, 75, 115], [115, 35, 35], [175, 95, 235], [155, 255, 155], [185, 185, 185], [105, 105, 55], [254, 191, 31], [20, 30, 40]]¶

cerebrum_cortex_DKT31_colors = [[125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [150, 150, 200], [255, 192, 32], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [150, 150, 200], [255, 192, 32]]¶

cerebrum_cortex_DKT31_names = ['ctx-lh-caudalanteriorcingulate', 'ctx-lh-caudalmiddlefrontal', 'ctx-lh-cuneus', 'ctx-lh-entorhinal', 'ctx-lh-fusiform', 'ctx-lh-inferiorparietal', 'ctx-lh-inferiortemporal', 'ctx-lh-isthmuscingulate', 'ctx-lh-lateraloccipital', 'ctx-lh-lateralorbitofrontal', 'ctx-lh-lingual', 'ctx-lh-medialorbitofrontal', 'ctx-lh-middletemporal', 'ctx-lh-parahippocampal', 'ctx-lh-paracentral', 'ctx-lh-parsopercularis', 'ctx-lh-parsorbitalis', 'ctx-lh-parstriangularis', 'ctx-lh-pericalcarine', 'ctx-lh-postcentral', 'ctx-lh-posteriorcingulate', 'ctx-lh-precentral', 'ctx-lh-precuneus', 'ctx-lh-rostralanteriorcingulate', 'ctx-lh-rostralmiddlefrontal', 'ctx-lh-superiorfrontal', 'ctx-lh-superiorparietal', 'ctx-lh-superiortemporal', 'ctx-lh-supramarginal', 'ctx-lh-transversetemporal', 'ctx-lh-insula', 'ctx-rh-caudalanteriorcingulate', 'ctx-rh-caudalmiddlefrontal', 'ctx-rh-cuneus', 'ctx-rh-entorhinal', 'ctx-rh-fusiform', 'ctx-rh-inferiorparietal', 'ctx-rh-inferiortemporal', 'ctx-rh-isthmuscingulate', 'ctx-rh-lateraloccipital', 'ctx-rh-lateralorbitofrontal', 'ctx-rh-lingual', 'ctx-rh-medialorbitofrontal', 'ctx-rh-middletemporal', 'ctx-rh-parahippocampal', 'ctx-rh-paracentral', 'ctx-rh-parsopercularis', 'ctx-rh-parsorbitalis', 'ctx-rh-parstriangularis', 'ctx-rh-pericalcarine', 'ctx-rh-postcentral', 'ctx-rh-posteriorcingulate', 'ctx-rh-precentral', 'ctx-rh-precuneus', 'ctx-rh-rostralanteriorcingulate', 'ctx-rh-rostralmiddlefrontal', 'ctx-rh-superiorfrontal', 'ctx-rh-superiorparietal', 'ctx-rh-superiortemporal', 'ctx-rh-supramarginal', 'ctx-rh-transversetemporal', 'ctx-rh-insula']¶

cerebrum_cortex_DKT31_numbers = [1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1034, 1035, 2002, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2034, 2035]¶

cerebrum_cortex_colors = [[205, 62, 78], [80, 196, 98], [60, 58, 210], [25, 5, 25], [25, 100, 40], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [100, 0, 100], [70, 70, 70], [150, 150, 200], [255, 192, 32], [205, 62, 78], [80, 196, 98], [60, 58, 210], [25, 5, 25], [25, 100, 40], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [100, 0, 100], [70, 70, 70], [150, 150, 200], [255, 192, 32]]¶

cerebrum_cortex_names = ['Left-Cerebral-Cortex', 'Left-Insula', 'Left-Operculum', 'ctx-lh-unknown', 'ctx-lh-bankssts', 'ctx-lh-caudalanteriorcingulate', 'ctx-lh-caudalmiddlefrontal', 'ctx-lh-cuneus', 'ctx-lh-entorhinal', 'ctx-lh-fusiform', 'ctx-lh-inferiorparietal', 'ctx-lh-inferiortemporal', 'ctx-lh-isthmuscingulate', 'ctx-lh-lateraloccipital', 'ctx-lh-lateralorbitofrontal', 'ctx-lh-lingual', 'ctx-lh-medialorbitofrontal', 'ctx-lh-middletemporal', 'ctx-lh-parahippocampal', 'ctx-lh-paracentral', 'ctx-lh-parsopercularis', 'ctx-lh-parsorbitalis', 'ctx-lh-parstriangularis', 'ctx-lh-pericalcarine', 'ctx-lh-postcentral', 'ctx-lh-posteriorcingulate', 'ctx-lh-precentral', 'ctx-lh-precuneus', 'ctx-lh-rostralanteriorcingulate', 'ctx-lh-rostralmiddlefrontal', 'ctx-lh-superiorfrontal', 'ctx-lh-superiorparietal', 'ctx-lh-superiortemporal', 'ctx-lh-supramarginal', 'ctx-lh-frontalpole', 'ctx-lh-temporalpole', 'ctx-lh-transversetemporal', 'ctx-lh-insula', 'Right-Cerebral-Cortex', 'Right-Insula', 'Right-Operculum', 'ctx-rh-unknown', 'ctx-rh-bankssts', 'ctx-rh-caudalanteriorcingulate', 'ctx-rh-caudalmiddlefrontal', 'ctx-rh-cuneus', 'ctx-rh-entorhinal', 'ctx-rh-fusiform', 'ctx-rh-inferiorparietal', 'ctx-rh-inferiortemporal', 'ctx-rh-isthmuscingulate', 'ctx-rh-lateraloccipital', 'ctx-rh-lateralorbitofrontal', 'ctx-rh-lingual', 'ctx-rh-medialorbitofrontal', 'ctx-rh-middletemporal', 'ctx-rh-parahippocampal', 'ctx-rh-paracentral', 'ctx-rh-parsopercularis', 'ctx-rh-parsorbitalis', 'ctx-rh-parstriangularis', 'ctx-rh-pericalcarine', 'ctx-rh-postcentral', 'ctx-rh-posteriorcingulate', 'ctx-rh-precentral', 'ctx-rh-precuneus', 'ctx-rh-rostralanteriorcingulate', 'ctx-rh-rostralmiddlefrontal', 'ctx-rh-superiorfrontal', 'ctx-rh-superiorparietal', 'ctx-rh-superiortemporal', 'ctx-rh-supramarginal', 'ctx-rh-frontalpole', 'ctx-rh-temporalpole', 'ctx-rh-transversetemporal', 'ctx-rh-insula']¶

cerebrum_cortex_numbers = [3, 19, 20, 1000, 1001, 1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 42, 55, 56, 2000, 2001, 2002, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035]¶

cerebrum_names = ['Left-Cerebral-Cortex', 'Left-Insula', 'Left-Operculum', 'ctx-lh-unknown', 'ctx-lh-bankssts', 'ctx-lh-caudalanteriorcingulate', 'ctx-lh-caudalmiddlefrontal', 'ctx-lh-cuneus', 'ctx-lh-entorhinal', 'ctx-lh-fusiform', 'ctx-lh-inferiorparietal', 'ctx-lh-inferiortemporal', 'ctx-lh-isthmuscingulate', 'ctx-lh-lateraloccipital', 'ctx-lh-lateralorbitofrontal', 'ctx-lh-lingual', 'ctx-lh-medialorbitofrontal', 'ctx-lh-middletemporal', 'ctx-lh-parahippocampal', 'ctx-lh-paracentral', 'ctx-lh-parsopercularis', 'ctx-lh-parsorbitalis', 'ctx-lh-parstriangularis', 'ctx-lh-pericalcarine', 'ctx-lh-postcentral', 'ctx-lh-posteriorcingulate', 'ctx-lh-precentral', 'ctx-lh-precuneus', 'ctx-lh-rostralanteriorcingulate', 'ctx-lh-rostralmiddlefrontal', 'ctx-lh-superiorfrontal', 'ctx-lh-superiorparietal', 'ctx-lh-superiortemporal', 'ctx-lh-supramarginal', 'ctx-lh-frontalpole', 'ctx-lh-temporalpole', 'ctx-lh-transversetemporal', 'ctx-lh-insula', 'Left-Cerebral-White-Matter', 'Left-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Left-Thalamus', 'Left-Thalamus-Proper', 'Left-Caudate', 'Left-Putamen', 'Left-Pallidum', 'Left-Hippocampus', 'Left-Amygdala', 'Left-Lesion', 'Left-Accumbens-area', 'Left-Substancia-Nigra', 'Left-VentralDC', 'Left-vessel', 'Left-choroid-plexus', 'Left-WM-hypointensities', 'Left-Amygdala-Anterior', 'Left-wm-intensity-abnormality', 'Left-caudate-intensity-abnormality', 'Left-putamen-intensity-abnormality', 'Left-accumbens-intensity-abnormality', 'Left-pallidum-intensity-abnormality', 'Left-amygdala-intensity-abnormality', 'Left-hippocampus-intensity-abnormality', 'Left-thalamus-intensity-abnormality', 'Left-VDC-intensity-abnormality', 'Left-IntCapsule-Ant', 'Left-IntCapsule-Pos', 'left_CA2_3', 'left_alveus', 'left_CA1', 'left_fimbria', 'left_presubiculum', 'left_hippocampal_fissure', 'left_CA4_DG', 'left_subiculum', 'left_fornix', 'ctx-lh-corpuscallosum', 'wm-lh-unknown', 'wm-lh-bankssts', 'wm-lh-caudalanteriorcingulate', 'wm-lh-caudalmiddlefrontal', 'wm-lh-corpuscallosum', 'wm-lh-cuneus', 'wm-lh-entorhinal', 'wm-lh-fusiform', 'wm-lh-inferiorparietal', 'wm-lh-inferiortemporal', 'wm-lh-isthmuscingulate', 'wm-lh-lateraloccipital', 'wm-lh-lateralorbitofrontal', 'wm-lh-lingual', 'wm-lh-medialorbitofrontal', 'wm-lh-middletemporal', 'wm-lh-parahippocampal', 'wm-lh-paracentral', 'wm-lh-parsopercularis', 'wm-lh-parsorbitalis', 'wm-lh-parstriangularis', 'wm-lh-pericalcarine', 'wm-lh-postcentral', 'wm-lh-posteriorcingulate', 'wm-lh-precentral', 'wm-lh-precuneus', 'wm-lh-rostralanteriorcingulate', 'wm-lh-rostralmiddlefrontal', 'wm-lh-superiorfrontal', 'wm-lh-superiorparietal', 'wm-lh-superiortemporal', 'wm-lh-supramarginal', 'wm-lh-frontalpole', 'wm-lh-temporalpole', 'wm-lh-transversetemporal', 'wm-lh-insula', 'Left-UnsegmentedWhiteMatter', 'Right-Cerebral-Cortex', 'Right-Insula', 'Right-Operculum', 'ctx-rh-unknown', 'ctx-rh-bankssts', 'ctx-rh-caudalanteriorcingulate', 'ctx-rh-caudalmiddlefrontal', 'ctx-rh-cuneus', 'ctx-rh-entorhinal', 'ctx-rh-fusiform', 'ctx-rh-inferiorparietal', 'ctx-rh-inferiortemporal', 'ctx-rh-isthmuscingulate', 'ctx-rh-lateraloccipital', 'ctx-rh-lateralorbitofrontal', 'ctx-rh-lingual', 'ctx-rh-medialorbitofrontal', 'ctx-rh-middletemporal', 'ctx-rh-parahippocampal', 'ctx-rh-paracentral', 'ctx-rh-parsopercularis', 'ctx-rh-parsorbitalis', 'ctx-rh-parstriangularis', 'ctx-rh-pericalcarine', 'ctx-rh-postcentral', 'ctx-rh-posteriorcingulate', 'ctx-rh-precentral', 'ctx-rh-precuneus', 'ctx-rh-rostralanteriorcingulate', 'ctx-rh-rostralmiddlefrontal', 'ctx-rh-superiorfrontal', 'ctx-rh-superiorparietal', 'ctx-rh-superiortemporal', 'ctx-rh-supramarginal', 'ctx-rh-frontalpole', 'ctx-rh-temporalpole', 'ctx-rh-transversetemporal', 'ctx-rh-insula', 'Right-Cerebral-White-Matter', 'Right-Lateral-Ventricle', 'Right-Inf-Lat-Vent', 'Right-Thalamus', 'Right-Thalamus-Proper', 'Right-Caudate', 'Right-Putamen', 'Right-Pallidum', 'Right-Hippocampus', 'Right-Amygdala', 'Right-Lesion', 'Right-Accumbens-area', 'Right-Substancia-Nigra', 'Right-VentralDC', 'Right-vessel', 'Right-choroid-plexus', 'Right-WM-hypointensities', 'Right-Amygdala-Anterior', 'Right-wm-intensity-abnormality', 'Right-caudate-intensity-abnormality', 'Right-putamen-intensity-abnormality', 'Right-accumbens-intensity-abnormality', 'Right-pallidum-intensity-abnormality', 'Right-amygdala-intensity-abnormality', 'Right-hippocampus-intensity-abnormality', 'Right-thalamus-intensity-abnormality', 'Right-VDC-intensity-abnormality', 'Right-IntCapsule-Ant', 'Right-IntCapsule-Pos', 'right_CA2_3', 'right_alveus', 'right_CA1', 'right_fimbria', 'right_presubiculum', 'right_hippocampal_fissure', 'right_CA4_DG', 'right_subiculum', 'right_fornix', 'ctx-rh-corpuscallosum', 'wm-rh-unknown', 'wm-rh-bankssts', 'wm-rh-caudalanteriorcingulate', 'wm-rh-caudalmiddlefrontal', 'wm-rh-corpuscallosum', 'wm-rh-cuneus', 'wm-rh-entorhinal', 'wm-rh-fusiform', 'wm-rh-inferiorparietal', 'wm-rh-inferiortemporal', 'wm-rh-isthmuscingulate', 'wm-rh-lateraloccipital', 'wm-rh-lateralorbitofrontal', 'wm-rh-lingual', 'wm-rh-medialorbitofrontal', 'wm-rh-middletemporal', 'wm-rh-parahippocampal', 'wm-rh-paracentral', 'wm-rh-parsopercularis', 'wm-rh-parsorbitalis', 'wm-rh-parstriangularis', 'wm-rh-pericalcarine', 'wm-rh-postcentral', 'wm-rh-posteriorcingulate', 'wm-rh-precentral', 'wm-rh-precuneus', 'wm-rh-rostralanteriorcingulate', 'wm-rh-rostralmiddlefrontal', 'wm-rh-superiorfrontal', 'wm-rh-superiorparietal', 'wm-rh-superiortemporal', 'wm-rh-supramarginal', 'wm-rh-frontalpole', 'wm-rh-temporalpole', 'wm-rh-transversetemporal', 'wm-rh-insula', 'Right-UnsegmentedWhiteMatter']¶

cerebrum_noncortex_colors = [[245, 245, 245], [120, 18, 134], [196, 58, 250], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [12, 48, 255], [220, 216, 20], [103, 255, 255], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [160, 32, 240], [0, 200, 200], [255, 148, 10], [205, 10, 125], [124, 140, 178], [125, 140, 178], [126, 140, 178], [127, 140, 178], [124, 141, 178], [124, 142, 178], [124, 143, 178], [124, 144, 178], [124, 140, 179], [238, 59, 59], [62, 10, 205], [17, 85, 137], [119, 187, 103], [204, 68, 35], [204, 0, 254], [221, 187, 16], [153, 221, 239], [51, 17, 18], [0, 119, 86], [20, 100, 201], [120, 70, 50], [230, 250, 230], [230, 155, 215], [130, 155, 95], [155, 230, 255], [135, 185, 205], [35, 235, 155], [35, 235, 245], [75, 35, 115], [35, 195, 35], [75, 215, 135], [115, 235, 115], [235, 225, 115], [220, 180, 205], [30, 115, 115], [55, 220, 180], [95, 155, 205], [235, 35, 195], [195, 35, 195], [35, 75, 115], [235, 155, 205], [35, 195, 235], [135, 155, 195], [35, 235, 235], [35, 75, 35], [195, 235, 35], [95, 115, 75], [175, 235, 115], [180, 205, 130], [235, 35, 95], [235, 75, 115], [115, 35, 35], [175, 95, 235], [155, 255, 155], [185, 185, 185], [105, 105, 55], [254, 191, 31], [20, 30, 40], [0, 225, 0], [120, 18, 134], [196, 58, 250], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [13, 48, 255], [220, 216, 20], [103, 255, 255], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [160, 32, 240], [0, 200, 221], [255, 148, 10], [205, 10, 125], [124, 140, 178], [125, 140, 178], [126, 140, 178], [127, 140, 178], [124, 141, 178], [124, 142, 178], [124, 143, 178], [124, 144, 178], [124, 140, 179], [238, 59, 59], [62, 10, 205], [17, 85, 136], [119, 187, 102], [204, 68, 34], [204, 0, 255], [221, 187, 17], [153, 221, 238], [51, 17, 17], [0, 119, 85], [20, 100, 200], [120, 70, 50], [230, 250, 230], [230, 155, 215], [130, 155, 95], [155, 230, 255], [135, 185, 205], [35, 235, 155], [35, 235, 245], [75, 35, 115], [35, 195, 35], [75, 215, 135], [115, 235, 115], [235, 225, 115], [220, 180, 205], [30, 115, 115], [55, 220, 180], [95, 155, 205], [235, 35, 195], [195, 35, 195], [35, 75, 115], [235, 155, 205], [35, 195, 235], [135, 155, 195], [35, 235, 235], [35, 75, 35], [195, 235, 35], [95, 115, 75], [175, 235, 115], [180, 205, 130], [235, 35, 95], [235, 75, 115], [115, 35, 35], [175, 95, 235], [155, 255, 155], [185, 185, 185], [105, 105, 55], [254, 191, 31], [20, 30, 40], [204, 182, 142], [42, 204, 164], [120, 190, 150], [250, 255, 50], [255, 0, 0], [0, 0, 64], [0, 0, 112], [0, 0, 160], [0, 0, 208], [0, 0, 255], [120, 18, 134], [196, 58, 250], [120, 18, 134], [196, 58, 250], [204, 182, 142], [42, 204, 164], [120, 190, 150]]¶

cerebrum_noncortex_names = ['Left-Cerebral-White-Matter', 'Left-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Left-Thalamus', 'Left-Thalamus-Proper', 'Left-Caudate', 'Left-Putamen', 'Left-Pallidum', 'Left-Hippocampus', 'Left-Amygdala', 'Left-Lesion', 'Left-Accumbens-area', 'Left-Substancia-Nigra', 'Left-VentralDC', 'Left-vessel', 'Left-choroid-plexus', 'Left-WM-hypointensities', 'Left-Amygdala-Anterior', 'Left-wm-intensity-abnormality', 'Left-caudate-intensity-abnormality', 'Left-putamen-intensity-abnormality', 'Left-accumbens-intensity-abnormality', 'Left-pallidum-intensity-abnormality', 'Left-amygdala-intensity-abnormality', 'Left-hippocampus-intensity-abnormality', 'Left-thalamus-intensity-abnormality', 'Left-VDC-intensity-abnormality', 'Left-IntCapsule-Ant', 'Left-IntCapsule-Pos', 'left_CA2_3', 'left_alveus', 'left_CA1', 'left_fimbria', 'left_presubiculum', 'left_hippocampal_fissure', 'left_CA4_DG', 'left_subiculum', 'left_fornix', 'ctx-lh-corpuscallosum', 'wm-lh-unknown', 'wm-lh-bankssts', 'wm-lh-caudalanteriorcingulate', 'wm-lh-caudalmiddlefrontal', 'wm-lh-corpuscallosum', 'wm-lh-cuneus', 'wm-lh-entorhinal', 'wm-lh-fusiform', 'wm-lh-inferiorparietal', 'wm-lh-inferiortemporal', 'wm-lh-isthmuscingulate', 'wm-lh-lateraloccipital', 'wm-lh-lateralorbitofrontal', 'wm-lh-lingual', 'wm-lh-medialorbitofrontal', 'wm-lh-middletemporal', 'wm-lh-parahippocampal', 'wm-lh-paracentral', 'wm-lh-parsopercularis', 'wm-lh-parsorbitalis', 'wm-lh-parstriangularis', 'wm-lh-pericalcarine', 'wm-lh-postcentral', 'wm-lh-posteriorcingulate', 'wm-lh-precentral', 'wm-lh-precuneus', 'wm-lh-rostralanteriorcingulate', 'wm-lh-rostralmiddlefrontal', 'wm-lh-superiorfrontal', 'wm-lh-superiorparietal', 'wm-lh-superiortemporal', 'wm-lh-supramarginal', 'wm-lh-frontalpole', 'wm-lh-temporalpole', 'wm-lh-transversetemporal', 'wm-lh-insula', 'Left-UnsegmentedWhiteMatter', 'Right-Cerebral-White-Matter', 'Right-Lateral-Ventricle', 'Right-Inf-Lat-Vent', 'Right-Thalamus', 'Right-Thalamus-Proper', 'Right-Caudate', 'Right-Putamen', 'Right-Pallidum', 'Right-Hippocampus', 'Right-Amygdala', 'Right-Lesion', 'Right-Accumbens-area', 'Right-Substancia-Nigra', 'Right-VentralDC', 'Right-vessel', 'Right-choroid-plexus', 'Right-WM-hypointensities', 'Right-Amygdala-Anterior', 'Right-wm-intensity-abnormality', 'Right-caudate-intensity-abnormality', 'Right-putamen-intensity-abnormality', 'Right-accumbens-intensity-abnormality', 'Right-pallidum-intensity-abnormality', 'Right-amygdala-intensity-abnormality', 'Right-hippocampus-intensity-abnormality', 'Right-thalamus-intensity-abnormality', 'Right-VDC-intensity-abnormality', 'Right-IntCapsule-Ant', 'Right-IntCapsule-Pos', 'right_CA2_3', 'right_alveus', 'right_CA1', 'right_fimbria', 'right_presubiculum', 'right_hippocampal_fissure', 'right_CA4_DG', 'right_subiculum', 'right_fornix', 'ctx-rh-corpuscallosum', 'wm-rh-unknown', 'wm-rh-bankssts', 'wm-rh-caudalanteriorcingulate', 'wm-rh-caudalmiddlefrontal', 'wm-rh-corpuscallosum', 'wm-rh-cuneus', 'wm-rh-entorhinal', 'wm-rh-fusiform', 'wm-rh-inferiorparietal', 'wm-rh-inferiortemporal', 'wm-rh-isthmuscingulate', 'wm-rh-lateraloccipital', 'wm-rh-lateralorbitofrontal', 'wm-rh-lingual', 'wm-rh-medialorbitofrontal', 'wm-rh-middletemporal', 'wm-rh-parahippocampal', 'wm-rh-paracentral', 'wm-rh-parsopercularis', 'wm-rh-parsorbitalis', 'wm-rh-parstriangularis', 'wm-rh-pericalcarine', 'wm-rh-postcentral', 'wm-rh-posteriorcingulate', 'wm-rh-precentral', 'wm-rh-precuneus', 'wm-rh-rostralanteriorcingulate', 'wm-rh-rostralmiddlefrontal', 'wm-rh-superiorfrontal', 'wm-rh-superiorparietal', 'wm-rh-superiortemporal', 'wm-rh-supramarginal', 'wm-rh-frontalpole', 'wm-rh-temporalpole', 'wm-rh-transversetemporal', 'wm-rh-insula', 'Right-UnsegmentedWhiteMatter', '3rd-Ventricle', '4th-Ventricle', '5th-Ventricle', 'Corpus_Callosum', 'Fornix', 'CC_Posterior', 'CC_Mid_Posterior', 'CC_Central', 'CC_Mid_Anterior', 'CC_Anterior', 'Left-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Right-Lateral-Ventricle', 'Right-Inf-Lat-Vent', '3rd-Ventricle', '4th-Ventricle', '5th-Ventricle']¶

cerebrum_noncortex_numbers = [2, 4, 5, 9, 10, 11, 12, 13, 17, 18, 25, 26, 27, 28, 30, 31, 78, 96, 100, 101, 102, 103, 104, 105, 106, 107, 108, 155, 157, 550, 551, 552, 553, 554, 555, 556, 557, 558, 1004, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 5001, 41, 43, 44, 48, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 63, 79, 97, 109, 110, 111, 112, 113, 114, 115, 116, 117, 156, 158, 500, 501, 502, 503, 504, 505, 506, 507, 508, 2004, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033, 4034, 4035, 5002, 14, 15, 72, 192, 250, 251, 252, 253, 254, 255, 4, 5, 43, 44, 14, 15, 72]¶

cerebrum_numbers = [3, 19, 20, 1000, 1001, 1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 2, 4, 5, 9, 10, 11, 12, 13, 17, 18, 25, 26, 27, 28, 30, 31, 78, 96, 100, 101, 102, 103, 104, 105, 106, 107, 108, 155, 157, 550, 551, 552, 553, 554, 555, 556, 557, 558, 1004, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 5001, 42, 55, 56, 2000, 2001, 2002, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 41, 43, 44, 48, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 63, 79, 97, 109, 110, 111, 112, 113, 114, 115, 116, 117, 156, 158, 500, 501, 502, 503, 504, 505, 506, 507, 508, 2004, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033, 4034, 4035, 5002]¶

colormap = [[3, 1, 205, 62, 78], [19, 1, 80, 196, 98], [20, 1, 60, 58, 210], [1000, 1, 25, 5, 25], [1001, 1, 25, 100, 40], [1002, 1, 125, 100, 160], [1003, 1, 100, 25, 0], [1005, 1, 220, 20, 100], [1006, 1, 220, 20, 10], [1007, 1, 180, 220, 140], [1008, 1, 220, 60, 220], [1009, 1, 180, 40, 120], [1010, 1, 140, 20, 140], [1011, 1, 20, 30, 140], [1012, 1, 35, 75, 50], [1013, 1, 225, 140, 140], [1014, 1, 200, 35, 75], [1015, 1, 160, 100, 50], [1016, 1, 20, 220, 60], [1017, 1, 60, 220, 60], [1018, 1, 220, 180, 140], [1019, 1, 20, 100, 50], [1020, 1, 220, 60, 20], [1021, 1, 120, 100, 60], [1022, 1, 220, 20, 20], [1023, 1, 220, 180, 220], [1024, 1, 60, 20, 220], [1025, 1, 160, 140, 180], [1026, 1, 80, 20, 140], [1027, 1, 75, 50, 125], [1028, 1, 20, 220, 160], [1029, 1, 20, 180, 140], [1030, 1, 140, 220, 220], [1031, 1, 80, 160, 20], [1032, 1, 100, 0, 100], [1033, 1, 70, 70, 70], [1034, 1, 150, 150, 200], [1035, 1, 255, 192, 32], [2, 1, 245, 245, 245], [4, 1, 120, 18, 134], [5, 1, 196, 58, 250], [9, 1, 0, 118, 14], [10, 1, 0, 118, 14], [11, 1, 122, 186, 220], [12, 1, 236, 13, 176], [13, 1, 12, 48, 255], [17, 1, 220, 216, 20], [18, 1, 103, 255, 255], [25, 1, 255, 165, 0], [26, 1, 255, 165, 0], [27, 1, 0, 255, 127], [28, 1, 165, 42, 42], [30, 1, 160, 32, 240], [31, 1, 0, 200, 200], [78, 1, 255, 148, 10], [96, 1, 205, 10, 125], [100, 1, 124, 140, 178], [101, 1, 125, 140, 178], [102, 1, 126, 140, 178], [103, 1, 127, 140, 178], [104, 1, 124, 141, 178], [105, 1, 124, 142, 178], [106, 1, 124, 143, 178], [107, 1, 124, 144, 178], [108, 1, 124, 140, 179], [155, 1, 238, 59, 59], [157, 1, 62, 10, 205], [550, 1, 17, 85, 137], [551, 1, 119, 187, 103], [552, 1, 204, 68, 35], [553, 1, 204, 0, 254], [554, 1, 221, 187, 16], [555, 1, 153, 221, 239], [556, 1, 51, 17, 18], [557, 1, 0, 119, 86], [558, 1, 20, 100, 201], [1004, 1, 120, 70, 50], [3000, 1, 230, 250, 230], [3001, 1, 230, 155, 215], [3002, 1, 130, 155, 95], [3003, 1, 155, 230, 255], [3004, 1, 135, 185, 205], [3005, 1, 35, 235, 155], [3006, 1, 35, 235, 245], [3007, 1, 75, 35, 115], [3008, 1, 35, 195, 35], [3009, 1, 75, 215, 135], [3010, 1, 115, 235, 115], [3011, 1, 235, 225, 115], [3012, 1, 220, 180, 205], [3013, 1, 30, 115, 115], [3014, 1, 55, 220, 180], [3015, 1, 95, 155, 205], [3016, 1, 235, 35, 195], [3017, 1, 195, 35, 195], [3018, 1, 35, 75, 115], [3019, 1, 235, 155, 205], [3020, 1, 35, 195, 235], [3021, 1, 135, 155, 195], [3022, 1, 35, 235, 235], [3023, 1, 35, 75, 35], [3024, 1, 195, 235, 35], [3025, 1, 95, 115, 75], [3026, 1, 175, 235, 115], [3027, 1, 180, 205, 130], [3028, 1, 235, 35, 95], [3029, 1, 235, 75, 115], [3030, 1, 115, 35, 35], [3031, 1, 175, 95, 235], [3032, 1, 155, 255, 155], [3033, 1, 185, 185, 185], [3034, 1, 105, 105, 55], [3035, 1, 254, 191, 31], [5001, 1, 20, 30, 40], [42, 1, 205, 62, 78], [55, 1, 80, 196, 98], [56, 1, 60, 58, 210], [2000, 1, 25, 5, 25], [2001, 1, 25, 100, 40], [2002, 1, 125, 100, 160], [2003, 1, 100, 25, 0], [2005, 1, 220, 20, 100], [2006, 1, 220, 20, 10], [2007, 1, 180, 220, 140], [2008, 1, 220, 60, 220], [2009, 1, 180, 40, 120], [2010, 1, 140, 20, 140], [2011, 1, 20, 30, 140], [2012, 1, 35, 75, 50], [2013, 1, 225, 140, 140], [2014, 1, 200, 35, 75], [2015, 1, 160, 100, 50], [2016, 1, 20, 220, 60], [2017, 1, 60, 220, 60], [2018, 1, 220, 180, 140], [2019, 1, 20, 100, 50], [2020, 1, 220, 60, 20], [2021, 1, 120, 100, 60], [2022, 1, 220, 20, 20], [2023, 1, 220, 180, 220], [2024, 1, 60, 20, 220], [2025, 1, 160, 140, 180], [2026, 1, 80, 20, 140], [2027, 1, 75, 50, 125], [2028, 1, 20, 220, 160], [2029, 1, 20, 180, 140], [2030, 1, 140, 220, 220], [2031, 1, 80, 160, 20], [2032, 1, 100, 0, 100], [2033, 1, 70, 70, 70], [2034, 1, 150, 150, 200], [2035, 1, 255, 192, 32], [41, 1, 0, 225, 0], [43, 1, 120, 18, 134], [44, 1, 196, 58, 250], [48, 1, 0, 118, 14], [49, 1, 0, 118, 14], [50, 1, 122, 186, 220], [51, 1, 236, 13, 176], [52, 1, 13, 48, 255], [53, 1, 220, 216, 20], [54, 1, 103, 255, 255], [57, 1, 255, 165, 0], [58, 1, 255, 165, 0], [59, 1, 0, 255, 127], [60, 1, 165, 42, 42], [62, 1, 160, 32, 240], [63, 1, 0, 200, 221], [79, 1, 255, 148, 10], [97, 1, 205, 10, 125], [109, 1, 124, 140, 178], [110, 1, 125, 140, 178], [111, 1, 126, 140, 178], [112, 1, 127, 140, 178], [113, 1, 124, 141, 178], [114, 1, 124, 142, 178], [115, 1, 124, 143, 178], [116, 1, 124, 144, 178], [117, 1, 124, 140, 179], [156, 1, 238, 59, 59], [158, 1, 62, 10, 205], [500, 1, 17, 85, 136], [501, 1, 119, 187, 102], [502, 1, 204, 68, 34], [503, 1, 204, 0, 255], [504, 1, 221, 187, 17], [505, 1, 153, 221, 238], [506, 1, 51, 17, 17], [507, 1, 0, 119, 85], [508, 1, 20, 100, 200], [2004, 1, 120, 70, 50], [4000, 1, 230, 250, 230], [4001, 1, 230, 155, 215], [4002, 1, 130, 155, 95], [4003, 1, 155, 230, 255], [4004, 1, 135, 185, 205], [4005, 1, 35, 235, 155], [4006, 1, 35, 235, 245], [4007, 1, 75, 35, 115], [4008, 1, 35, 195, 35], [4009, 1, 75, 215, 135], [4010, 1, 115, 235, 115], [4011, 1, 235, 225, 115], [4012, 1, 220, 180, 205], [4013, 1, 30, 115, 115], [4014, 1, 55, 220, 180], [4015, 1, 95, 155, 205], [4016, 1, 235, 35, 195], [4017, 1, 195, 35, 195], [4018, 1, 35, 75, 115], [4019, 1, 235, 155, 205], [4020, 1, 35, 195, 235], [4021, 1, 135, 155, 195], [4022, 1, 35, 235, 235], [4023, 1, 35, 75, 35], [4024, 1, 195, 235, 35], [4025, 1, 95, 115, 75], [4026, 1, 175, 235, 115], [4027, 1, 180, 205, 130], [4028, 1, 235, 35, 95], [4029, 1, 235, 75, 115], [4030, 1, 115, 35, 35], [4031, 1, 175, 95, 235], [4032, 1, 155, 255, 155], [4033, 1, 185, 185, 185], [4034, 1, 105, 105, 55], [4035, 1, 254, 191, 31], [5002, 1, 20, 30, 40], [6, 1, 0, 148, 0], [8, 1, 230, 148, 34], [45, 1, 0, 148, 0], [47, 1, 230, 148, 34], [7, 1, 220, 248, 164], [46, 1, 220, 248, 164], [16, 1, 119, 159, 176], [170, 1, 119, 159, 176], [171, 1, 119, 0, 176], [172, 1, 119, 100, 176], [173, 1, 119, 200, 176], [174, 1, 119, 159, 100], [175, 1, 119, 159, 200], [24, 1, 60, 60, 60], [85, 1, 234, 169, 30]]¶

colormap_normalized = [[3, 1, 0.803921568627451, 0.24313725490196078, 0.3058823529411765], [19, 1, 0.3137254901960784, 0.7686274509803922, 0.3843137254901961], [20, 1, 0.23529411764705882, 0.22745098039215686, 0.8235294117647058], [1000, 1, 0.09803921568627451, 0.0196078431372549, 0.09803921568627451], [1001, 1, 0.09803921568627451, 0.39215686274509803, 0.1568627450980392], [1002, 1, 0.49019607843137253, 0.39215686274509803, 0.6274509803921569], [1003, 1, 0.39215686274509803, 0.09803921568627451, 0.0], [1005, 1, 0.8627450980392157, 0.0784313725490196, 0.39215686274509803], [1006, 1, 0.8627450980392157, 0.0784313725490196, 0.0392156862745098], [1007, 1, 0.7058823529411765, 0.8627450980392157, 0.5490196078431373], [1008, 1, 0.8627450980392157, 0.23529411764705882, 0.8627450980392157], [1009, 1, 0.7058823529411765, 0.1568627450980392, 0.47058823529411764], [1010, 1, 0.5490196078431373, 0.0784313725490196, 0.5490196078431373], [1011, 1, 0.0784313725490196, 0.11764705882352941, 0.5490196078431373], [1012, 1, 0.13725490196078433, 0.29411764705882354, 0.19607843137254902], [1013, 1, 0.8823529411764706, 0.5490196078431373, 0.5490196078431373], [1014, 1, 0.7843137254901961, 0.13725490196078433, 0.29411764705882354], [1015, 1, 0.6274509803921569, 0.39215686274509803, 0.19607843137254902], [1016, 1, 0.0784313725490196, 0.8627450980392157, 0.23529411764705882], [1017, 1, 0.23529411764705882, 0.8627450980392157, 0.23529411764705882], [1018, 1, 0.8627450980392157, 0.7058823529411765, 0.5490196078431373], [1019, 1, 0.0784313725490196, 0.39215686274509803, 0.19607843137254902], [1020, 1, 0.8627450980392157, 0.23529411764705882, 0.0784313725490196], [1021, 1, 0.47058823529411764, 0.39215686274509803, 0.23529411764705882], [1022, 1, 0.8627450980392157, 0.0784313725490196, 0.0784313725490196], [1023, 1, 0.8627450980392157, 0.7058823529411765, 0.8627450980392157], [1024, 1, 0.23529411764705882, 0.0784313725490196, 0.8627450980392157], [1025, 1, 0.6274509803921569, 0.5490196078431373, 0.7058823529411765], [1026, 1, 0.3137254901960784, 0.0784313725490196, 0.5490196078431373], [1027, 1, 0.29411764705882354, 0.19607843137254902, 0.49019607843137253], [1028, 1, 0.0784313725490196, 0.8627450980392157, 0.6274509803921569], [1029, 1, 0.0784313725490196, 0.7058823529411765, 0.5490196078431373], [1030, 1, 0.5490196078431373, 0.8627450980392157, 0.8627450980392157], [1031, 1, 0.3137254901960784, 0.6274509803921569, 0.0784313725490196], [1032, 1, 0.39215686274509803, 0.0, 0.39215686274509803], [1033, 1, 0.27450980392156865, 0.27450980392156865, 0.27450980392156865], [1034, 1, 0.5882352941176471, 0.5882352941176471, 0.7843137254901961], [1035, 1, 1.0, 0.7529411764705882, 0.12549019607843137], [2, 1, 0.9607843137254902, 0.9607843137254902, 0.9607843137254902], [4, 1, 0.47058823529411764, 0.07058823529411765, 0.5254901960784314], [5, 1, 0.7686274509803922, 0.22745098039215686, 0.9803921568627451], [9, 1, 0.0, 0.4627450980392157, 0.054901960784313725], [10, 1, 0.0, 0.4627450980392157, 0.054901960784313725], [11, 1, 0.47843137254901963, 0.7294117647058823, 0.8627450980392157], [12, 1, 0.9254901960784314, 0.050980392156862744, 0.6901960784313725], [13, 1, 0.047058823529411764, 0.18823529411764706, 1.0], [17, 1, 0.8627450980392157, 0.8470588235294118, 0.0784313725490196], [18, 1, 0.403921568627451, 1.0, 1.0], [25, 1, 1.0, 0.6470588235294118, 0.0], [26, 1, 1.0, 0.6470588235294118, 0.0], [27, 1, 0.0, 1.0, 0.4980392156862745], [28, 1, 0.6470588235294118, 0.16470588235294117, 0.16470588235294117], [30, 1, 0.6274509803921569, 0.12549019607843137, 0.9411764705882353], [31, 1, 0.0, 0.7843137254901961, 0.7843137254901961], [78, 1, 1.0, 0.5803921568627451, 0.0392156862745098], [96, 1, 0.803921568627451, 0.0392156862745098, 0.49019607843137253], [100, 1, 0.48627450980392156, 0.5490196078431373, 0.6980392156862745], [101, 1, 0.49019607843137253, 0.5490196078431373, 0.6980392156862745], [102, 1, 0.49411764705882355, 0.5490196078431373, 0.6980392156862745], [103, 1, 0.4980392156862745, 0.5490196078431373, 0.6980392156862745], [104, 1, 0.48627450980392156, 0.5529411764705883, 0.6980392156862745], [105, 1, 0.48627450980392156, 0.5568627450980392, 0.6980392156862745], [106, 1, 0.48627450980392156, 0.5607843137254902, 0.6980392156862745], [107, 1, 0.48627450980392156, 0.5647058823529412, 0.6980392156862745], [108, 1, 0.48627450980392156, 0.5490196078431373, 0.7019607843137254], [155, 1, 0.9333333333333333, 0.23137254901960785, 0.23137254901960785], [157, 1, 0.24313725490196078, 0.0392156862745098, 0.803921568627451], [550, 1, 0.06666666666666667, 0.3333333333333333, 0.5372549019607843], [551, 1, 0.4666666666666667, 0.7333333333333333, 0.403921568627451], [552, 1, 0.8, 0.26666666666666666, 0.13725490196078433], [553, 1, 0.8, 0.0, 0.996078431372549], [554, 1, 0.8666666666666667, 0.7333333333333333, 0.06274509803921569], [555, 1, 0.6, 0.8666666666666667, 0.9372549019607843], [556, 1, 0.2, 0.06666666666666667, 0.07058823529411765], [557, 1, 0.0, 0.4666666666666667, 0.33725490196078434], [558, 1, 0.0784313725490196, 0.39215686274509803, 0.788235294117647], [1004, 1, 0.47058823529411764, 0.27450980392156865, 0.19607843137254902], [3000, 1, 0.9019607843137255, 0.9803921568627451, 0.9019607843137255], [3001, 1, 0.9019607843137255, 0.6078431372549019, 0.8431372549019608], [3002, 1, 0.5098039215686274, 0.6078431372549019, 0.37254901960784315], [3003, 1, 0.6078431372549019, 0.9019607843137255, 1.0], [3004, 1, 0.5294117647058824, 0.7254901960784313, 0.803921568627451], [3005, 1, 0.13725490196078433, 0.9215686274509803, 0.6078431372549019], [3006, 1, 0.13725490196078433, 0.9215686274509803, 0.9607843137254902], [3007, 1, 0.29411764705882354, 0.13725490196078433, 0.45098039215686275], [3008, 1, 0.13725490196078433, 0.7647058823529411, 0.13725490196078433], [3009, 1, 0.29411764705882354, 0.8431372549019608, 0.5294117647058824], [3010, 1, 0.45098039215686275, 0.9215686274509803, 0.45098039215686275], [3011, 1, 0.9215686274509803, 0.8823529411764706, 0.45098039215686275], [3012, 1, 0.8627450980392157, 0.7058823529411765, 0.803921568627451], [3013, 1, 0.11764705882352941, 0.45098039215686275, 0.45098039215686275], [3014, 1, 0.21568627450980393, 0.8627450980392157, 0.7058823529411765], [3015, 1, 0.37254901960784315, 0.6078431372549019, 0.803921568627451], [3016, 1, 0.9215686274509803, 0.13725490196078433, 0.7647058823529411], [3017, 1, 0.7647058823529411, 0.13725490196078433, 0.7647058823529411], [3018, 1, 0.13725490196078433, 0.29411764705882354, 0.45098039215686275], [3019, 1, 0.9215686274509803, 0.6078431372549019, 0.803921568627451], [3020, 1, 0.13725490196078433, 0.7647058823529411, 0.9215686274509803], [3021, 1, 0.5294117647058824, 0.6078431372549019, 0.7647058823529411], [3022, 1, 0.13725490196078433, 0.9215686274509803, 0.9215686274509803], [3023, 1, 0.13725490196078433, 0.29411764705882354, 0.13725490196078433], [3024, 1, 0.7647058823529411, 0.9215686274509803, 0.13725490196078433], [3025, 1, 0.37254901960784315, 0.45098039215686275, 0.29411764705882354], [3026, 1, 0.6862745098039216, 0.9215686274509803, 0.45098039215686275], [3027, 1, 0.7058823529411765, 0.803921568627451, 0.5098039215686274], [3028, 1, 0.9215686274509803, 0.13725490196078433, 0.37254901960784315], [3029, 1, 0.9215686274509803, 0.29411764705882354, 0.45098039215686275], [3030, 1, 0.45098039215686275, 0.13725490196078433, 0.13725490196078433], [3031, 1, 0.6862745098039216, 0.37254901960784315, 0.9215686274509803], [3032, 1, 0.6078431372549019, 1.0, 0.6078431372549019], [3033, 1, 0.7254901960784313, 0.7254901960784313, 0.7254901960784313], [3034, 1, 0.4117647058823529, 0.4117647058823529, 0.21568627450980393], [3035, 1, 0.996078431372549, 0.7490196078431373, 0.12156862745098039], [5001, 1, 0.0784313725490196, 0.11764705882352941, 0.1568627450980392], [42, 1, 0.803921568627451, 0.24313725490196078, 0.3058823529411765], [55, 1, 0.3137254901960784, 0.7686274509803922, 0.3843137254901961], [56, 1, 0.23529411764705882, 0.22745098039215686, 0.8235294117647058], [2000, 1, 0.09803921568627451, 0.0196078431372549, 0.09803921568627451], [2001, 1, 0.09803921568627451, 0.39215686274509803, 0.1568627450980392], [2002, 1, 0.49019607843137253, 0.39215686274509803, 0.6274509803921569], [2003, 1, 0.39215686274509803, 0.09803921568627451, 0.0], [2005, 1, 0.8627450980392157, 0.0784313725490196, 0.39215686274509803], [2006, 1, 0.8627450980392157, 0.0784313725490196, 0.0392156862745098], [2007, 1, 0.7058823529411765, 0.8627450980392157, 0.5490196078431373], [2008, 1, 0.8627450980392157, 0.23529411764705882, 0.8627450980392157], [2009, 1, 0.7058823529411765, 0.1568627450980392, 0.47058823529411764], [2010, 1, 0.5490196078431373, 0.0784313725490196, 0.5490196078431373], [2011, 1, 0.0784313725490196, 0.11764705882352941, 0.5490196078431373], [2012, 1, 0.13725490196078433, 0.29411764705882354, 0.19607843137254902], [2013, 1, 0.8823529411764706, 0.5490196078431373, 0.5490196078431373], [2014, 1, 0.7843137254901961, 0.13725490196078433, 0.29411764705882354], [2015, 1, 0.6274509803921569, 0.39215686274509803, 0.19607843137254902], [2016, 1, 0.0784313725490196, 0.8627450980392157, 0.23529411764705882], [2017, 1, 0.23529411764705882, 0.8627450980392157, 0.23529411764705882], [2018, 1, 0.8627450980392157, 0.7058823529411765, 0.5490196078431373], [2019, 1, 0.0784313725490196, 0.39215686274509803, 0.19607843137254902], [2020, 1, 0.8627450980392157, 0.23529411764705882, 0.0784313725490196], [2021, 1, 0.47058823529411764, 0.39215686274509803, 0.23529411764705882], [2022, 1, 0.8627450980392157, 0.0784313725490196, 0.0784313725490196], [2023, 1, 0.8627450980392157, 0.7058823529411765, 0.8627450980392157], [2024, 1, 0.23529411764705882, 0.0784313725490196, 0.8627450980392157], [2025, 1, 0.6274509803921569, 0.5490196078431373, 0.7058823529411765], [2026, 1, 0.3137254901960784, 0.0784313725490196, 0.5490196078431373], [2027, 1, 0.29411764705882354, 0.19607843137254902, 0.49019607843137253], [2028, 1, 0.0784313725490196, 0.8627450980392157, 0.6274509803921569], [2029, 1, 0.0784313725490196, 0.7058823529411765, 0.5490196078431373], [2030, 1, 0.5490196078431373, 0.8627450980392157, 0.8627450980392157], [2031, 1, 0.3137254901960784, 0.6274509803921569, 0.0784313725490196], [2032, 1, 0.39215686274509803, 0.0, 0.39215686274509803], [2033, 1, 0.27450980392156865, 0.27450980392156865, 0.27450980392156865], [2034, 1, 0.5882352941176471, 0.5882352941176471, 0.7843137254901961], [2035, 1, 1.0, 0.7529411764705882, 0.12549019607843137], [41, 1, 0.0, 0.8823529411764706, 0.0], [43, 1, 0.47058823529411764, 0.07058823529411765, 0.5254901960784314], [44, 1, 0.7686274509803922, 0.22745098039215686, 0.9803921568627451], [48, 1, 0.0, 0.4627450980392157, 0.054901960784313725], [49, 1, 0.0, 0.4627450980392157, 0.054901960784313725], [50, 1, 0.47843137254901963, 0.7294117647058823, 0.8627450980392157], [51, 1, 0.9254901960784314, 0.050980392156862744, 0.6901960784313725], [52, 1, 0.050980392156862744, 0.18823529411764706, 1.0], [53, 1, 0.8627450980392157, 0.8470588235294118, 0.0784313725490196], [54, 1, 0.403921568627451, 1.0, 1.0], [57, 1, 1.0, 0.6470588235294118, 0.0], [58, 1, 1.0, 0.6470588235294118, 0.0], [59, 1, 0.0, 1.0, 0.4980392156862745], [60, 1, 0.6470588235294118, 0.16470588235294117, 0.16470588235294117], [62, 1, 0.6274509803921569, 0.12549019607843137, 0.9411764705882353], [63, 1, 0.0, 0.7843137254901961, 0.8666666666666667], [79, 1, 1.0, 0.5803921568627451, 0.0392156862745098], [97, 1, 0.803921568627451, 0.0392156862745098, 0.49019607843137253], [109, 1, 0.48627450980392156, 0.5490196078431373, 0.6980392156862745], [110, 1, 0.49019607843137253, 0.5490196078431373, 0.6980392156862745], [111, 1, 0.49411764705882355, 0.5490196078431373, 0.6980392156862745], [112, 1, 0.4980392156862745, 0.5490196078431373, 0.6980392156862745], [113, 1, 0.48627450980392156, 0.5529411764705883, 0.6980392156862745], [114, 1, 0.48627450980392156, 0.5568627450980392, 0.6980392156862745], [115, 1, 0.48627450980392156, 0.5607843137254902, 0.6980392156862745], [116, 1, 0.48627450980392156, 0.5647058823529412, 0.6980392156862745], [117, 1, 0.48627450980392156, 0.5490196078431373, 0.7019607843137254], [156, 1, 0.9333333333333333, 0.23137254901960785, 0.23137254901960785], [158, 1, 0.24313725490196078, 0.0392156862745098, 0.803921568627451], [500, 1, 0.06666666666666667, 0.3333333333333333, 0.5333333333333333], [501, 1, 0.4666666666666667, 0.7333333333333333, 0.4], [502, 1, 0.8, 0.26666666666666666, 0.13333333333333333], [503, 1, 0.8, 0.0, 1.0], [504, 1, 0.8666666666666667, 0.7333333333333333, 0.06666666666666667], [505, 1, 0.6, 0.8666666666666667, 0.9333333333333333], [506, 1, 0.2, 0.06666666666666667, 0.06666666666666667], [507, 1, 0.0, 0.4666666666666667, 0.3333333333333333], [508, 1, 0.0784313725490196, 0.39215686274509803, 0.7843137254901961], [2004, 1, 0.47058823529411764, 0.27450980392156865, 0.19607843137254902], [4000, 1, 0.9019607843137255, 0.9803921568627451, 0.9019607843137255], [4001, 1, 0.9019607843137255, 0.6078431372549019, 0.8431372549019608], [4002, 1, 0.5098039215686274, 0.6078431372549019, 0.37254901960784315], [4003, 1, 0.6078431372549019, 0.9019607843137255, 1.0], [4004, 1, 0.5294117647058824, 0.7254901960784313, 0.803921568627451], [4005, 1, 0.13725490196078433, 0.9215686274509803, 0.6078431372549019], [4006, 1, 0.13725490196078433, 0.9215686274509803, 0.9607843137254902], [4007, 1, 0.29411764705882354, 0.13725490196078433, 0.45098039215686275], [4008, 1, 0.13725490196078433, 0.7647058823529411, 0.13725490196078433], [4009, 1, 0.29411764705882354, 0.8431372549019608, 0.5294117647058824], [4010, 1, 0.45098039215686275, 0.9215686274509803, 0.45098039215686275], [4011, 1, 0.9215686274509803, 0.8823529411764706, 0.45098039215686275], [4012, 1, 0.8627450980392157, 0.7058823529411765, 0.803921568627451], [4013, 1, 0.11764705882352941, 0.45098039215686275, 0.45098039215686275], [4014, 1, 0.21568627450980393, 0.8627450980392157, 0.7058823529411765], [4015, 1, 0.37254901960784315, 0.6078431372549019, 0.803921568627451], [4016, 1, 0.9215686274509803, 0.13725490196078433, 0.7647058823529411], [4017, 1, 0.7647058823529411, 0.13725490196078433, 0.7647058823529411], [4018, 1, 0.13725490196078433, 0.29411764705882354, 0.45098039215686275], [4019, 1, 0.9215686274509803, 0.6078431372549019, 0.803921568627451], [4020, 1, 0.13725490196078433, 0.7647058823529411, 0.9215686274509803], [4021, 1, 0.5294117647058824, 0.6078431372549019, 0.7647058823529411], [4022, 1, 0.13725490196078433, 0.9215686274509803, 0.9215686274509803], [4023, 1, 0.13725490196078433, 0.29411764705882354, 0.13725490196078433], [4024, 1, 0.7647058823529411, 0.9215686274509803, 0.13725490196078433], [4025, 1, 0.37254901960784315, 0.45098039215686275, 0.29411764705882354], [4026, 1, 0.6862745098039216, 0.9215686274509803, 0.45098039215686275], [4027, 1, 0.7058823529411765, 0.803921568627451, 0.5098039215686274], [4028, 1, 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220], [221, 100, 20], [181, 200, 20], [101, 20, 20], [101, 100, 180], [181, 220, 20], [21, 140, 200], [21, 20, 240], [21, 20, 200], [21, 20, 60], [101, 60, 60], [21, 180, 180], [223, 220, 60], [221, 60, 60], [0, 0, 0], [23, 220, 60], [23, 60, 180], [63, 100, 60], [63, 20, 220], [13, 0, 250], [26, 60, 0], [26, 60, 75], [26, 60, 150], [25, 60, 250], [60, 25, 25], [180, 20, 20], [220, 20, 100], [140, 60, 60], [180, 220, 140], [140, 100, 180], [180, 20, 140], [23, 10, 10], [225, 140, 140], [180, 60, 180], [20, 220, 60], [60, 20, 140], [220, 180, 140], [65, 100, 20], [220, 60, 20], [20, 60, 220], [100, 100, 60], [220, 180, 220], [20, 180, 140], [60, 140, 180], [25, 20, 140], [20, 60, 100], [60, 220, 20], [60, 60, 220], [220, 60, 220], [65, 220, 60], [25, 140, 20], [220, 220, 100], [180, 60, 60], [61, 20, 220], [61, 20, 60], [61, 60, 100], [25, 25, 25], [140, 20, 60], [220, 180, 20], [63, 180, 180], [221, 20, 10], [221, 20, 100], [221, 60, 140], [221, 20, 220], [61, 220, 220], [100, 200, 200], [10, 200, 200], [221, 220, 20], [141, 20, 100], [61, 220, 100], [141, 60, 20], [143, 20, 220], [101, 60, 220], [21, 20, 140], [61, 20, 180], [221, 140, 20], [141, 100, 220], [221, 100, 20], [181, 200, 20], [101, 20, 20], [101, 100, 180], [181, 220, 20], [21, 140, 200], [21, 20, 240], [21, 20, 200], [21, 20, 60], [101, 60, 60], [21, 180, 180], [223, 220, 60], [221, 60, 60], [0, 0, 0], [23, 220, 60], [23, 60, 180], [63, 100, 60], [63, 20, 220], [13, 0, 250], [26, 60, 0], [26, 60, 75], [26, 60, 150], [25, 60, 250], [60, 25, 25], [180, 20, 20], [220, 20, 100], [140, 60, 60], [180, 220, 140], [140, 100, 180], [180, 20, 140], [23, 10, 10], [225, 140, 140], [180, 60, 180], [20, 220, 60], [60, 20, 140], [220, 180, 140], [65, 100, 20], [220, 60, 20], [20, 60, 220], [100, 100, 60], [220, 180, 220], [20, 180, 140], [60, 140, 180], [25, 20, 140], [20, 60, 100], [60, 220, 20], [60, 60, 220], [220, 60, 220], [65, 220, 60], [25, 140, 20], [220, 220, 100], [180, 60, 60], [61, 20, 220], [61, 20, 60], [61, 60, 100], [25, 25, 25], [140, 20, 60], [220, 180, 20], [63, 180, 180], [221, 20, 10], [221, 20, 100], [221, 60, 140], [221, 20, 220], [61, 220, 220], [100, 200, 200], [10, 200, 200], [221, 220, 20], [141, 20, 100], [61, 220, 100], [141, 60, 20], [143, 20, 220], [101, 60, 220], [21, 20, 140], [61, 20, 180], [221, 140, 20], [141, 100, 220], [221, 100, 20], [181, 200, 20], [101, 20, 20], [101, 100, 180], [181, 220, 20], [21, 140, 200], [21, 20, 240], [21, 20, 200], [21, 20, 60], [101, 60, 60], [21, 180, 180], [223, 220, 60], [221, 60, 60], [0, 0, 0], [23, 220, 60], [23, 60, 180], [63, 100, 60], [63, 20, 220], [13, 0, 250], [26, 60, 0], [26, 60, 75], [26, 60, 150], [25, 60, 250], [60, 25, 25], [180, 20, 20], [220, 20, 100], [140, 60, 60], [180, 220, 140], [140, 100, 180], [180, 20, 140], [23, 10, 10], [225, 140, 140], [180, 60, 180], [20, 220, 60], [60, 20, 140], [220, 180, 140], [65, 100, 20], [220, 60, 20], [20, 60, 220], [100, 100, 60], [220, 180, 220], [20, 180, 140], [60, 140, 180], [25, 20, 140], [20, 60, 100], [60, 220, 20], [60, 60, 220], [220, 60, 220], [65, 220, 60], [25, 140, 20], [220, 220, 100], [180, 60, 60], [61, 20, 220], [61, 20, 60], [61, 60, 100], [25, 25, 25], [140, 20, 60], [220, 180, 20], [63, 180, 180], [221, 20, 10], [221, 20, 100], [221, 60, 140], [221, 20, 220], [61, 220, 220], [100, 200, 200], [10, 200, 200], [221, 220, 20], [141, 20, 100], [61, 220, 100], [141, 60, 20], [143, 20, 220], [101, 60, 220], [21, 20, 140], [61, 20, 180], [221, 140, 20], [141, 100, 220], [221, 100, 20], [181, 200, 20], [101, 20, 20], [101, 100, 180], [181, 220, 20], [21, 140, 200], [21, 20, 240], [21, 20, 200], [21, 20, 60], [101, 60, 60], [21, 180, 180], [223, 220, 60], [221, 60, 60]]¶

extra_colors = [[60, 60, 60], [234, 169, 30]]¶

extra_list = [24, 85]¶

extra_names = ['CSF', 'Optic-Chiasm']¶

extra_numbers = [24, 85]¶

i = 242¶

label_colors = [[205, 62, 78], [80, 196, 98], [60, 58, 210], [25, 5, 25], [25, 100, 40], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [100, 0, 100], [70, 70, 70], [150, 150, 200], [255, 192, 32], [245, 245, 245], [120, 18, 134], [196, 58, 250], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [12, 48, 255], [220, 216, 20], [103, 255, 255], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [160, 32, 240], [0, 200, 200], [255, 148, 10], [205, 10, 125], [124, 140, 178], [125, 140, 178], [126, 140, 178], [127, 140, 178], [124, 141, 178], [124, 142, 178], [124, 143, 178], [124, 144, 178], [124, 140, 179], [238, 59, 59], [62, 10, 205], [17, 85, 137], [119, 187, 103], [204, 68, 35], [204, 0, 254], [221, 187, 16], [153, 221, 239], [51, 17, 18], [0, 119, 86], [20, 100, 201], [120, 70, 50], [230, 250, 230], [230, 155, 215], [130, 155, 95], [155, 230, 255], [135, 185, 205], [35, 235, 155], [35, 235, 245], [75, 35, 115], [35, 195, 35], [75, 215, 135], [115, 235, 115], [235, 225, 115], [220, 180, 205], [30, 115, 115], [55, 220, 180], [95, 155, 205], [235, 35, 195], [195, 35, 195], [35, 75, 115], [235, 155, 205], [35, 195, 235], [135, 155, 195], [35, 235, 235], [35, 75, 35], [195, 235, 35], [95, 115, 75], [175, 235, 115], [180, 205, 130], [235, 35, 95], [235, 75, 115], [115, 35, 35], [175, 95, 235], [155, 255, 155], [185, 185, 185], [105, 105, 55], [254, 191, 31], [20, 30, 40], [205, 62, 78], [80, 196, 98], [60, 58, 210], [25, 5, 25], [25, 100, 40], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [100, 0, 100], [70, 70, 70], [150, 150, 200], [255, 192, 32], [0, 225, 0], [120, 18, 134], [196, 58, 250], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [13, 48, 255], [220, 216, 20], [103, 255, 255], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [160, 32, 240], [0, 200, 221], [255, 148, 10], [205, 10, 125], [124, 140, 178], [125, 140, 178], [126, 140, 178], [127, 140, 178], [124, 141, 178], [124, 142, 178], [124, 143, 178], [124, 144, 178], [124, 140, 179], [238, 59, 59], [62, 10, 205], [17, 85, 136], [119, 187, 102], [204, 68, 34], [204, 0, 255], [221, 187, 17], [153, 221, 238], [51, 17, 17], [0, 119, 85], [20, 100, 200], [120, 70, 50], [230, 250, 230], [230, 155, 215], [130, 155, 95], [155, 230, 255], [135, 185, 205], [35, 235, 155], [35, 235, 245], [75, 35, 115], [35, 195, 35], [75, 215, 135], [115, 235, 115], [235, 225, 115], [220, 180, 205], [30, 115, 115], [55, 220, 180], [95, 155, 205], [235, 35, 195], [195, 35, 195], [35, 75, 115], [235, 155, 205], [35, 195, 235], [135, 155, 195], [35, 235, 235], [35, 75, 35], [195, 235, 35], [95, 115, 75], [175, 235, 115], [180, 205, 130], [235, 35, 95], [235, 75, 115], [115, 35, 35], [175, 95, 235], [155, 255, 155], [185, 185, 185], [105, 105, 55], [254, 191, 31], [20, 30, 40], [0, 148, 0], [230, 148, 34], [0, 148, 0], [230, 148, 34], [220, 248, 164], [220, 248, 164], [119, 159, 176], [119, 159, 176], [119, 0, 176], [119, 100, 176], [119, 200, 176], [119, 159, 100], [119, 159, 200], [60, 60, 60], [234, 169, 30]]¶

label_names = ['Left-Cerebral-Cortex', 'Left-Insula', 'Left-Operculum', 'ctx-lh-unknown', 'ctx-lh-bankssts', 'ctx-lh-caudalanteriorcingulate', 'ctx-lh-caudalmiddlefrontal', 'ctx-lh-cuneus', 'ctx-lh-entorhinal', 'ctx-lh-fusiform', 'ctx-lh-inferiorparietal', 'ctx-lh-inferiortemporal', 'ctx-lh-isthmuscingulate', 'ctx-lh-lateraloccipital', 'ctx-lh-lateralorbitofrontal', 'ctx-lh-lingual', 'ctx-lh-medialorbitofrontal', 'ctx-lh-middletemporal', 'ctx-lh-parahippocampal', 'ctx-lh-paracentral', 'ctx-lh-parsopercularis', 'ctx-lh-parsorbitalis', 'ctx-lh-parstriangularis', 'ctx-lh-pericalcarine', 'ctx-lh-postcentral', 'ctx-lh-posteriorcingulate', 'ctx-lh-precentral', 'ctx-lh-precuneus', 'ctx-lh-rostralanteriorcingulate', 'ctx-lh-rostralmiddlefrontal', 'ctx-lh-superiorfrontal', 'ctx-lh-superiorparietal', 'ctx-lh-superiortemporal', 'ctx-lh-supramarginal', 'ctx-lh-frontalpole', 'ctx-lh-temporalpole', 'ctx-lh-transversetemporal', 'ctx-lh-insula', 'Left-Cerebral-White-Matter', 'Left-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Left-Thalamus', 'Left-Thalamus-Proper', 'Left-Caudate', 'Left-Putamen', 'Left-Pallidum', 'Left-Hippocampus', 'Left-Amygdala', 'Left-Lesion', 'Left-Accumbens-area', 'Left-Substancia-Nigra', 'Left-VentralDC', 'Left-vessel', 'Left-choroid-plexus', 'Left-WM-hypointensities', 'Left-Amygdala-Anterior', 'Left-wm-intensity-abnormality', 'Left-caudate-intensity-abnormality', 'Left-putamen-intensity-abnormality', 'Left-accumbens-intensity-abnormality', 'Left-pallidum-intensity-abnormality', 'Left-amygdala-intensity-abnormality', 'Left-hippocampus-intensity-abnormality', 'Left-thalamus-intensity-abnormality', 'Left-VDC-intensity-abnormality', 'Left-IntCapsule-Ant', 'Left-IntCapsule-Pos', 'left_CA2_3', 'left_alveus', 'left_CA1', 'left_fimbria', 'left_presubiculum', 'left_hippocampal_fissure', 'left_CA4_DG', 'left_subiculum', 'left_fornix', 'ctx-lh-corpuscallosum', 'wm-lh-unknown', 'wm-lh-bankssts', 'wm-lh-caudalanteriorcingulate', 'wm-lh-caudalmiddlefrontal', 'wm-lh-corpuscallosum', 'wm-lh-cuneus', 'wm-lh-entorhinal', 'wm-lh-fusiform', 'wm-lh-inferiorparietal', 'wm-lh-inferiortemporal', 'wm-lh-isthmuscingulate', 'wm-lh-lateraloccipital', 'wm-lh-lateralorbitofrontal', 'wm-lh-lingual', 'wm-lh-medialorbitofrontal', 'wm-lh-middletemporal', 'wm-lh-parahippocampal', 'wm-lh-paracentral', 'wm-lh-parsopercularis', 'wm-lh-parsorbitalis', 'wm-lh-parstriangularis', 'wm-lh-pericalcarine', 'wm-lh-postcentral', 'wm-lh-posteriorcingulate', 'wm-lh-precentral', 'wm-lh-precuneus', 'wm-lh-rostralanteriorcingulate', 'wm-lh-rostralmiddlefrontal', 'wm-lh-superiorfrontal', 'wm-lh-superiorparietal', 'wm-lh-superiortemporal', 'wm-lh-supramarginal', 'wm-lh-frontalpole', 'wm-lh-temporalpole', 'wm-lh-transversetemporal', 'wm-lh-insula', 'Left-UnsegmentedWhiteMatter', 'Right-Cerebral-Cortex', 'Right-Insula', 'Right-Operculum', 'ctx-rh-unknown', 'ctx-rh-bankssts', 'ctx-rh-caudalanteriorcingulate', 'ctx-rh-caudalmiddlefrontal', 'ctx-rh-cuneus', 'ctx-rh-entorhinal', 'ctx-rh-fusiform', 'ctx-rh-inferiorparietal', 'ctx-rh-inferiortemporal', 'ctx-rh-isthmuscingulate', 'ctx-rh-lateraloccipital', 'ctx-rh-lateralorbitofrontal', 'ctx-rh-lingual', 'ctx-rh-medialorbitofrontal', 'ctx-rh-middletemporal', 'ctx-rh-parahippocampal', 'ctx-rh-paracentral', 'ctx-rh-parsopercularis', 'ctx-rh-parsorbitalis', 'ctx-rh-parstriangularis', 'ctx-rh-pericalcarine', 'ctx-rh-postcentral', 'ctx-rh-posteriorcingulate', 'ctx-rh-precentral', 'ctx-rh-precuneus', 'ctx-rh-rostralanteriorcingulate', 'ctx-rh-rostralmiddlefrontal', 'ctx-rh-superiorfrontal', 'ctx-rh-superiorparietal', 'ctx-rh-superiortemporal', 'ctx-rh-supramarginal', 'ctx-rh-frontalpole', 'ctx-rh-temporalpole', 'ctx-rh-transversetemporal', 'ctx-rh-insula', 'Right-Cerebral-White-Matter', 'Right-Lateral-Ventricle', 'Right-Inf-Lat-Vent', 'Right-Thalamus', 'Right-Thalamus-Proper', 'Right-Caudate', 'Right-Putamen', 'Right-Pallidum', 'Right-Hippocampus', 'Right-Amygdala', 'Right-Lesion', 'Right-Accumbens-area', 'Right-Substancia-Nigra', 'Right-VentralDC', 'Right-vessel', 'Right-choroid-plexus', 'Right-WM-hypointensities', 'Right-Amygdala-Anterior', 'Right-wm-intensity-abnormality', 'Right-caudate-intensity-abnormality', 'Right-putamen-intensity-abnormality', 'Right-accumbens-intensity-abnormality', 'Right-pallidum-intensity-abnormality', 'Right-amygdala-intensity-abnormality', 'Right-hippocampus-intensity-abnormality', 'Right-thalamus-intensity-abnormality', 'Right-VDC-intensity-abnormality', 'Right-IntCapsule-Ant', 'Right-IntCapsule-Pos', 'right_CA2_3', 'right_alveus', 'right_CA1', 'right_fimbria', 'right_presubiculum', 'right_hippocampal_fissure', 'right_CA4_DG', 'right_subiculum', 'right_fornix', 'ctx-rh-corpuscallosum', 'wm-rh-unknown', 'wm-rh-bankssts', 'wm-rh-caudalanteriorcingulate', 'wm-rh-caudalmiddlefrontal', 'wm-rh-corpuscallosum', 'wm-rh-cuneus', 'wm-rh-entorhinal', 'wm-rh-fusiform', 'wm-rh-inferiorparietal', 'wm-rh-inferiortemporal', 'wm-rh-isthmuscingulate', 'wm-rh-lateraloccipital', 'wm-rh-lateralorbitofrontal', 'wm-rh-lingual', 'wm-rh-medialorbitofrontal', 'wm-rh-middletemporal', 'wm-rh-parahippocampal', 'wm-rh-paracentral', 'wm-rh-parsopercularis', 'wm-rh-parsorbitalis', 'wm-rh-parstriangularis', 'wm-rh-pericalcarine', 'wm-rh-postcentral', 'wm-rh-posteriorcingulate', 'wm-rh-precentral', 'wm-rh-precuneus', 'wm-rh-rostralanteriorcingulate', 'wm-rh-rostralmiddlefrontal', 'wm-rh-superiorfrontal', 'wm-rh-superiorparietal', 'wm-rh-superiortemporal', 'wm-rh-supramarginal', 'wm-rh-frontalpole', 'wm-rh-temporalpole', 'wm-rh-transversetemporal', 'wm-rh-insula', 'Right-UnsegmentedWhiteMatter', 'Left-Cerebellum-Exterior', 'Left-Cerebellum-Cortex', 'Right-Cerebellum-Exterior', 'Right-Cerebellum-Cortex', 'Left-Cerebellum-White-Matter', 'Right-Cerebellum-White-Matter', 'Brain-Stem', 'brainstem', 'DCG', 'Vermis', 'Midbrain', 'Pons', 'Medulla', 'CSF', 'Optic-Chiasm']¶

label_numbers = [3, 19, 20, 1000, 1001, 1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 2, 4, 5, 9, 10, 11, 12, 13, 17, 18, 25, 26, 27, 28, 30, 31, 78, 96, 100, 101, 102, 103, 104, 105, 106, 107, 108, 155, 157, 550, 551, 552, 553, 554, 555, 556, 557, 558, 1004, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 5001, 42, 55, 56, 2000, 2001, 2002, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 41, 43, 44, 48, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 63, 79, 97, 109, 110, 111, 112, 113, 114, 115, 116, 117, 156, 158, 500, 501, 502, 503, 504, 505, 506, 507, 508, 2004, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033, 4034, 4035, 5002, 6, 8, 45, 47, 7, 46, 16, 170, 171, 172, 173, 174, 175, 24, 85]¶

left_cerebellum_cortex_colors = [[0, 148, 0], [230, 148, 34]]¶

left_cerebellum_cortex_list = [6, 8]¶

left_cerebellum_cortex_names = ['Left-Cerebellum-Exterior', 'Left-Cerebellum-Cortex']¶

left_cerebellum_cortex_numbers = [6, 8]¶

left_cerebellum_names = ['Left-Cerebellum-Exterior', 'Left-Cerebellum-Cortex', 'Left-Cerebellum-White-Matter']¶

left_cerebellum_noncortex_colors = [[220, 248, 164]]¶

left_cerebellum_noncortex_list = [7]¶

left_cerebellum_noncortex_names = ['Left-Cerebellum-White-Matter']¶

left_cerebellum_noncortex_numbers = [7]¶

left_cerebellum_numbers = [6, 8, 7]¶

left_cerebrum_colors = [[205, 62, 78], [80, 196, 98], [60, 58, 210], [25, 5, 25], [25, 100, 40], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [100, 0, 100], [70, 70, 70], [150, 150, 200], [255, 192, 32], [245, 245, 245], [120, 18, 134], [196, 58, 250], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [12, 48, 255], [220, 216, 20], [103, 255, 255], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [160, 32, 240], [0, 200, 200], [255, 148, 10], [205, 10, 125], [124, 140, 178], [125, 140, 178], [126, 140, 178], [127, 140, 178], [124, 141, 178], [124, 142, 178], [124, 143, 178], [124, 144, 178], [124, 140, 179], [238, 59, 59], [62, 10, 205], [17, 85, 137], [119, 187, 103], [204, 68, 35], [204, 0, 254], [221, 187, 16], [153, 221, 239], [51, 17, 18], [0, 119, 86], [20, 100, 201], [120, 70, 50], [230, 250, 230], [230, 155, 215], [130, 155, 95], [155, 230, 255], [135, 185, 205], [35, 235, 155], [35, 235, 245], [75, 35, 115], [35, 195, 35], [75, 215, 135], [115, 235, 115], [235, 225, 115], [220, 180, 205], [30, 115, 115], [55, 220, 180], [95, 155, 205], [235, 35, 195], [195, 35, 195], [35, 75, 115], [235, 155, 205], [35, 195, 235], [135, 155, 195], [35, 235, 235], [35, 75, 35], [195, 235, 35], [95, 115, 75], [175, 235, 115], [180, 205, 130], [235, 35, 95], [235, 75, 115], [115, 35, 35], [175, 95, 235], [155, 255, 155], [185, 185, 185], [105, 105, 55], [254, 191, 31], [20, 30, 40]]¶

left_cerebrum_cortex_DKT31_colors = [[125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [150, 150, 200], [255, 192, 32]]¶

left_cerebrum_cortex_DKT31_list = [1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1034, 1035]¶

left_cerebrum_cortex_DKT31_names = ['ctx-lh-caudalanteriorcingulate', 'ctx-lh-caudalmiddlefrontal', 'ctx-lh-cuneus', 'ctx-lh-entorhinal', 'ctx-lh-fusiform', 'ctx-lh-inferiorparietal', 'ctx-lh-inferiortemporal', 'ctx-lh-isthmuscingulate', 'ctx-lh-lateraloccipital', 'ctx-lh-lateralorbitofrontal', 'ctx-lh-lingual', 'ctx-lh-medialorbitofrontal', 'ctx-lh-middletemporal', 'ctx-lh-parahippocampal', 'ctx-lh-paracentral', 'ctx-lh-parsopercularis', 'ctx-lh-parsorbitalis', 'ctx-lh-parstriangularis', 'ctx-lh-pericalcarine', 'ctx-lh-postcentral', 'ctx-lh-posteriorcingulate', 'ctx-lh-precentral', 'ctx-lh-precuneus', 'ctx-lh-rostralanteriorcingulate', 'ctx-lh-rostralmiddlefrontal', 'ctx-lh-superiorfrontal', 'ctx-lh-superiorparietal', 'ctx-lh-superiortemporal', 'ctx-lh-supramarginal', 'ctx-lh-transversetemporal', 'ctx-lh-insula']¶

left_cerebrum_cortex_DKT31_numbers = [1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1034, 1035]¶

left_cerebrum_cortex_colors = [[205, 62, 78], [80, 196, 98], [60, 58, 210], [25, 5, 25], [25, 100, 40], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [100, 0, 100], [70, 70, 70], [150, 150, 200], [255, 192, 32]]¶

left_cerebrum_cortex_list = [1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1034, 1035, 3, 19, 20, 1000, 1001, 1032, 1033]¶

left_cerebrum_cortex_names = ['Left-Cerebral-Cortex', 'Left-Insula', 'Left-Operculum', 'ctx-lh-unknown', 'ctx-lh-bankssts', 'ctx-lh-caudalanteriorcingulate', 'ctx-lh-caudalmiddlefrontal', 'ctx-lh-cuneus', 'ctx-lh-entorhinal', 'ctx-lh-fusiform', 'ctx-lh-inferiorparietal', 'ctx-lh-inferiortemporal', 'ctx-lh-isthmuscingulate', 'ctx-lh-lateraloccipital', 'ctx-lh-lateralorbitofrontal', 'ctx-lh-lingual', 'ctx-lh-medialorbitofrontal', 'ctx-lh-middletemporal', 'ctx-lh-parahippocampal', 'ctx-lh-paracentral', 'ctx-lh-parsopercularis', 'ctx-lh-parsorbitalis', 'ctx-lh-parstriangularis', 'ctx-lh-pericalcarine', 'ctx-lh-postcentral', 'ctx-lh-posteriorcingulate', 'ctx-lh-precentral', 'ctx-lh-precuneus', 'ctx-lh-rostralanteriorcingulate', 'ctx-lh-rostralmiddlefrontal', 'ctx-lh-superiorfrontal', 'ctx-lh-superiorparietal', 'ctx-lh-superiortemporal', 'ctx-lh-supramarginal', 'ctx-lh-frontalpole', 'ctx-lh-temporalpole', 'ctx-lh-transversetemporal', 'ctx-lh-insula']¶

left_cerebrum_cortex_numbers = [3, 19, 20, 1000, 1001, 1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035]¶

left_cerebrum_names = ['Left-Cerebral-Cortex', 'Left-Insula', 'Left-Operculum', 'ctx-lh-unknown', 'ctx-lh-bankssts', 'ctx-lh-caudalanteriorcingulate', 'ctx-lh-caudalmiddlefrontal', 'ctx-lh-cuneus', 'ctx-lh-entorhinal', 'ctx-lh-fusiform', 'ctx-lh-inferiorparietal', 'ctx-lh-inferiortemporal', 'ctx-lh-isthmuscingulate', 'ctx-lh-lateraloccipital', 'ctx-lh-lateralorbitofrontal', 'ctx-lh-lingual', 'ctx-lh-medialorbitofrontal', 'ctx-lh-middletemporal', 'ctx-lh-parahippocampal', 'ctx-lh-paracentral', 'ctx-lh-parsopercularis', 'ctx-lh-parsorbitalis', 'ctx-lh-parstriangularis', 'ctx-lh-pericalcarine', 'ctx-lh-postcentral', 'ctx-lh-posteriorcingulate', 'ctx-lh-precentral', 'ctx-lh-precuneus', 'ctx-lh-rostralanteriorcingulate', 'ctx-lh-rostralmiddlefrontal', 'ctx-lh-superiorfrontal', 'ctx-lh-superiorparietal', 'ctx-lh-superiortemporal', 'ctx-lh-supramarginal', 'ctx-lh-frontalpole', 'ctx-lh-temporalpole', 'ctx-lh-transversetemporal', 'ctx-lh-insula', 'Left-Cerebral-White-Matter', 'Left-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Left-Thalamus', 'Left-Thalamus-Proper', 'Left-Caudate', 'Left-Putamen', 'Left-Pallidum', 'Left-Hippocampus', 'Left-Amygdala', 'Left-Lesion', 'Left-Accumbens-area', 'Left-Substancia-Nigra', 'Left-VentralDC', 'Left-vessel', 'Left-choroid-plexus', 'Left-WM-hypointensities', 'Left-Amygdala-Anterior', 'Left-wm-intensity-abnormality', 'Left-caudate-intensity-abnormality', 'Left-putamen-intensity-abnormality', 'Left-accumbens-intensity-abnormality', 'Left-pallidum-intensity-abnormality', 'Left-amygdala-intensity-abnormality', 'Left-hippocampus-intensity-abnormality', 'Left-thalamus-intensity-abnormality', 'Left-VDC-intensity-abnormality', 'Left-IntCapsule-Ant', 'Left-IntCapsule-Pos', 'left_CA2_3', 'left_alveus', 'left_CA1', 'left_fimbria', 'left_presubiculum', 'left_hippocampal_fissure', 'left_CA4_DG', 'left_subiculum', 'left_fornix', 'ctx-lh-corpuscallosum', 'wm-lh-unknown', 'wm-lh-bankssts', 'wm-lh-caudalanteriorcingulate', 'wm-lh-caudalmiddlefrontal', 'wm-lh-corpuscallosum', 'wm-lh-cuneus', 'wm-lh-entorhinal', 'wm-lh-fusiform', 'wm-lh-inferiorparietal', 'wm-lh-inferiortemporal', 'wm-lh-isthmuscingulate', 'wm-lh-lateraloccipital', 'wm-lh-lateralorbitofrontal', 'wm-lh-lingual', 'wm-lh-medialorbitofrontal', 'wm-lh-middletemporal', 'wm-lh-parahippocampal', 'wm-lh-paracentral', 'wm-lh-parsopercularis', 'wm-lh-parsorbitalis', 'wm-lh-parstriangularis', 'wm-lh-pericalcarine', 'wm-lh-postcentral', 'wm-lh-posteriorcingulate', 'wm-lh-precentral', 'wm-lh-precuneus', 'wm-lh-rostralanteriorcingulate', 'wm-lh-rostralmiddlefrontal', 'wm-lh-superiorfrontal', 'wm-lh-superiorparietal', 'wm-lh-superiortemporal', 'wm-lh-supramarginal', 'wm-lh-frontalpole', 'wm-lh-temporalpole', 'wm-lh-transversetemporal', 'wm-lh-insula', 'Left-UnsegmentedWhiteMatter']¶

left_cerebrum_noncortex_colors = [[245, 245, 245], [120, 18, 134], [196, 58, 250], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [12, 48, 255], [220, 216, 20], [103, 255, 255], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [160, 32, 240], [0, 200, 200], [255, 148, 10], [205, 10, 125], [124, 140, 178], [125, 140, 178], [126, 140, 178], [127, 140, 178], [124, 141, 178], [124, 142, 178], [124, 143, 178], [124, 144, 178], [124, 140, 179], [238, 59, 59], [62, 10, 205], [17, 85, 137], [119, 187, 103], [204, 68, 35], [204, 0, 254], [221, 187, 16], [153, 221, 239], [51, 17, 18], [0, 119, 86], [20, 100, 201], [120, 70, 50], [230, 250, 230], [230, 155, 215], [130, 155, 95], [155, 230, 255], [135, 185, 205], [35, 235, 155], [35, 235, 245], [75, 35, 115], [35, 195, 35], [75, 215, 135], [115, 235, 115], [235, 225, 115], [220, 180, 205], [30, 115, 115], [55, 220, 180], [95, 155, 205], [235, 35, 195], [195, 35, 195], [35, 75, 115], [235, 155, 205], [35, 195, 235], [135, 155, 195], [35, 235, 235], [35, 75, 35], [195, 235, 35], [95, 115, 75], [175, 235, 115], [180, 205, 130], [235, 35, 95], [235, 75, 115], [115, 35, 35], [175, 95, 235], [155, 255, 155], [185, 185, 185], [105, 105, 55], [254, 191, 31], [20, 30, 40]]¶

left_cerebrum_noncortex_list = [2, 9, 10, 11, 12, 13, 17, 18, 25, 26, 27, 28, 30, 31, 78, 91, 96, 100, 101, 102, 103, 104, 105, 106, 107, 108, 155, 157, 550, 551, 552, 553, 554, 555, 556, 557, 558, 1004, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 5001, 4, 5]¶

left_cerebrum_noncortex_names = ['Left-Cerebral-White-Matter', 'Left-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Left-Thalamus', 'Left-Thalamus-Proper', 'Left-Caudate', 'Left-Putamen', 'Left-Pallidum', 'Left-Hippocampus', 'Left-Amygdala', 'Left-Lesion', 'Left-Accumbens-area', 'Left-Substancia-Nigra', 'Left-VentralDC', 'Left-vessel', 'Left-choroid-plexus', 'Left-WM-hypointensities', 'Left-Amygdala-Anterior', 'Left-wm-intensity-abnormality', 'Left-caudate-intensity-abnormality', 'Left-putamen-intensity-abnormality', 'Left-accumbens-intensity-abnormality', 'Left-pallidum-intensity-abnormality', 'Left-amygdala-intensity-abnormality', 'Left-hippocampus-intensity-abnormality', 'Left-thalamus-intensity-abnormality', 'Left-VDC-intensity-abnormality', 'Left-IntCapsule-Ant', 'Left-IntCapsule-Pos', 'left_CA2_3', 'left_alveus', 'left_CA1', 'left_fimbria', 'left_presubiculum', 'left_hippocampal_fissure', 'left_CA4_DG', 'left_subiculum', 'left_fornix', 'ctx-lh-corpuscallosum', 'wm-lh-unknown', 'wm-lh-bankssts', 'wm-lh-caudalanteriorcingulate', 'wm-lh-caudalmiddlefrontal', 'wm-lh-corpuscallosum', 'wm-lh-cuneus', 'wm-lh-entorhinal', 'wm-lh-fusiform', 'wm-lh-inferiorparietal', 'wm-lh-inferiortemporal', 'wm-lh-isthmuscingulate', 'wm-lh-lateraloccipital', 'wm-lh-lateralorbitofrontal', 'wm-lh-lingual', 'wm-lh-medialorbitofrontal', 'wm-lh-middletemporal', 'wm-lh-parahippocampal', 'wm-lh-paracentral', 'wm-lh-parsopercularis', 'wm-lh-parsorbitalis', 'wm-lh-parstriangularis', 'wm-lh-pericalcarine', 'wm-lh-postcentral', 'wm-lh-posteriorcingulate', 'wm-lh-precentral', 'wm-lh-precuneus', 'wm-lh-rostralanteriorcingulate', 'wm-lh-rostralmiddlefrontal', 'wm-lh-superiorfrontal', 'wm-lh-superiorparietal', 'wm-lh-superiortemporal', 'wm-lh-supramarginal', 'wm-lh-frontalpole', 'wm-lh-temporalpole', 'wm-lh-transversetemporal', 'wm-lh-insula', 'Left-UnsegmentedWhiteMatter']¶

left_cerebrum_noncortex_numbers = [2, 4, 5, 9, 10, 11, 12, 13, 17, 18, 25, 26, 27, 28, 30, 31, 78, 96, 100, 101, 102, 103, 104, 105, 106, 107, 108, 155, 157, 550, 551, 552, 553, 554, 555, 556, 557, 558, 1004, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 5001]¶

left_cerebrum_numbers = [3, 19, 20, 1000, 1001, 1002, 1003, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 2, 4, 5, 9, 10, 11, 12, 13, 17, 18, 25, 26, 27, 28, 30, 31, 78, 96, 100, 101, 102, 103, 104, 105, 106, 107, 108, 155, 157, 550, 551, 552, 553, 554, 555, 556, 557, 558, 1004, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 5001]¶

left_pair = [1007, 1013]¶

left_pairs = [[1006, 1007], [1007, 1016], [1007, 1013]]¶

left_sulcus_label_pair_lists = [[[1012, 1028]], [[1003, 1028], [1027, 1028]], [[1003, 1018], [1003, 1019], [1003, 1020], [1018, 1027], [1019, 1027], [1020, 1027]], [[1024, 1028], [1003, 1024], [1024, 1027], [1018, 1024], [1019, 1024], [1020, 1024]], [[1022, 1024]], [[1022, 1029], [1022, 1031]], [[1029, 1031], [1008, 1029]], [[1008, 1031]], [[1030, 1031]], [[1008, 1011], [1011, 1029]], [[1011, 1015], [1009, 1011]], [[1015, 1030]], [[1009, 1015]], [[1012, 1035], [1030, 1035], [1034, 1035], [1002, 1035], [1010, 1035], [1023, 1035], [1026, 1035], [1022, 1035], [1024, 1035], [1031, 1035]], [[1030, 1034]], [[1002, 1014], [1010, 1014], [1014, 1023], [1014, 1026], [1002, 1028], [1010, 1028], [1023, 1028], [1026, 1028], [1002, 1017], [1010, 1017], [1017, 1023], [1017, 1026], [1017, 1025]], [[1017, 1028]], [[1005, 1025]], [[1013, 1025], [1002, 1013], [1010, 1013], [1013, 1023], [1013, 1026]], [[1014, 1028]], [[1003, 1012], [1012, 1027], [1012, 1018], [1012, 1019], [1012, 1020]], [[1012, 1014]], [[1007, 1009], [1007, 1011]], [[1006, 1007], [1007, 1016], [1007, 1013]]]¶

left_ventricle_colors = [[120, 18, 134], [196, 58, 250]]¶

left_ventricle_list = [4, 5]¶

left_ventricle_names = ['Left-Lateral-Ventricle', 'Left-Inf-Lat-Vent']¶

left_ventricle_numbers = [4, 5]¶

medial_cerebellum_noncortex_colors = []¶

medial_cerebellum_noncortex_list = [630, 631, 632]¶

medial_cerebellum_noncortex_names = []¶

medial_cerebellum_noncortex_numbers = []¶

medial_cerebrum_noncortex_colors = [[204, 182, 142], [42, 204, 164], [120, 190, 150], [250, 255, 50], [255, 0, 0], [0, 0, 64], [0, 0, 112], [0, 0, 160], [0, 0, 208], [0, 0, 255]]¶

medial_cerebrum_noncortex_list = [14, 15, 72, 192, 250, 251, 252, 253, 254, 255]¶

medial_cerebrum_noncortex_names = ['3rd-Ventricle', '4th-Ventricle', '5th-Ventricle', 'Corpus_Callosum', 'Fornix', 'CC_Posterior', 'CC_Mid_Posterior', 'CC_Central', 'CC_Mid_Anterior', 'CC_Anterior']¶

medial_cerebrum_noncortex_numbers = [14, 15, 72, 192, 250, 251, 252, 253, 254, 255]¶

medial_ventricle_colors = [[204, 182, 142], [42, 204, 164], [120, 190, 150]]¶

medial_ventricle_list = [14, 15, 72]¶

medial_ventricle_names = ['3rd-Ventricle', '4th-Ventricle', '5th-Ventricle']¶

medial_ventricle_numbers = [14, 15, 72]¶

misc_colors = [[0, 0, 0], [70, 130, 180], [60, 58, 210], [60, 58, 210], [60, 58, 210], [135, 206, 235], [100, 50, 100], [135, 50, 74], [122, 135, 50], [51, 50, 135], [74, 155, 60], [120, 62, 43], [74, 155, 60], [122, 135, 50], [70, 130, 180], [135, 206, 235], [100, 50, 100], [135, 50, 74], [122, 135, 50], [51, 50, 135], [74, 155, 60], [120, 62, 43], [74, 155, 60], [122, 135, 50], [122, 135, 50], [122, 135, 50], [200, 70, 255], [164, 108, 226], [164, 108, 226], [164, 108, 226], [255, 218, 185], [255, 218, 185], [200, 120, 255], [200, 121, 255], [200, 122, 255], [160, 32, 240], [255, 20, 147], [205, 179, 139], [238, 238, 209], [200, 200, 200], [74, 255, 74], [238, 0, 0], [0, 0, 139], [173, 255, 47], [133, 203, 229], [26, 237, 57], [34, 139, 34], [30, 144, 255], [147, 19, 173], [238, 59, 59], [221, 39, 200], [238, 174, 238], [255, 0, 0], [72, 61, 139], [21, 39, 132], [21, 39, 132], [65, 135, 20], [65, 135, 20], [134, 4, 160], [221, 226, 68], [255, 255, 254], [52, 209, 226], [239, 160, 223], [70, 130, 180], [70, 130, 181], [139, 121, 94], [224, 224, 224], [255, 0, 0], [205, 205, 0], [238, 238, 209], [139, 121, 94], [238, 59, 59], [0, 118, 14], [0, 118, 14], [220, 216, 21], [220, 216, 21], [122, 186, 220], [122, 186, 220], [255, 165, 0], [14, 48, 255], [166, 42, 42], [121, 18, 134], [236, 13, 127], [236, 13, 126], [73, 61, 139], [73, 62, 139], [0, 196, 255], [255, 164, 164], [196, 196, 0], [0, 100, 255], [128, 196, 164], [0, 126, 75], [128, 96, 64], [0, 50, 128], [255, 204, 153], [255, 128, 128], [255, 255, 0], [64, 0, 64], [0, 0, 255], [255, 0, 0], [128, 128, 255], [0, 128, 0], [196, 160, 128], [32, 200, 255], [128, 255, 128], [204, 153, 204], [121, 17, 136], [128, 0, 0], [128, 32, 255], [255, 204, 102], [128, 128, 128], [104, 255, 255], [0, 226, 0], [205, 63, 78], [197, 58, 250], [33, 150, 250], [226, 0, 0], [100, 100, 100], [197, 150, 250], [170, 170, 255], [0, 0, 0], [255, 0, 0], [255, 80, 0], [255, 160, 0], [255, 255, 0], [0, 255, 0], [255, 0, 160], [255, 0, 255], [255, 50, 80], [80, 255, 50], [160, 255, 50], [160, 200, 255], [0, 255, 160], [0, 0, 255], [80, 50, 255], [160, 0, 255], [255, 210, 0], [0, 160, 255], [255, 200, 80], [255, 200, 160], [255, 80, 200], [255, 160, 200], [30, 255, 80], [80, 200, 255], [80, 255, 200], [195, 255, 200], [120, 200, 20], [170, 10, 200], [20, 130, 180], [20, 180, 130], [206, 62, 78], [121, 18, 134], [199, 58, 250], [1, 148, 0], [221, 248, 164], [231, 148, 34], [1, 118, 14], [120, 118, 14], [123, 186, 221], [238, 13, 177], [123, 186, 220], [138, 13, 206], [238, 130, 176], [218, 230, 76], [38, 213, 176], [1, 225, 176], [1, 225, 176], [200, 2, 100], [200, 2, 100], [5, 200, 90], [5, 200, 90], [100, 5, 200], [25, 255, 100], [25, 255, 100], [230, 7, 100], [230, 7, 100], [100, 5, 200], [150, 10, 200], [150, 10, 200], [175, 10, 176], [175, 10, 176], [10, 100, 255], [10, 100, 255], [150, 45, 70], [150, 45, 70], [45, 200, 15], [45, 200, 15], [227, 45, 100], [227, 45, 100], [227, 45, 100], [143, 188, 143], [255, 248, 220], [254, 254, 254], [70, 130, 180], [245, 245, 245], [205, 62, 78], [120, 18, 134], [196, 58, 250], [0, 148, 0], [220, 248, 164], [230, 148, 34], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [12, 48, 255], [204, 182, 142], [42, 204, 164], [119, 159, 176], [220, 216, 20], [103, 255, 255], [80, 196, 98], [60, 58, 210], [60, 58, 210], [60, 58, 210], [60, 58, 210], [60, 60, 60], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [204, 0, 0], [255, 0, 0], [0, 0, 255], [30, 144, 255], [100, 212, 237], [218, 165, 32], [255, 215, 0], [255, 255, 166], [153, 0, 204], [153, 141, 209], [204, 204, 255], [31, 212, 194], [3, 255, 237], [204, 255, 255], [86, 74, 147], [114, 114, 190], [184, 178, 255], [126, 138, 37], [189, 197, 117], [240, 230, 140], [204, 0, 0], [255, 0, 0], [0, 0, 255], [30, 144, 255], [100, 212, 237], [218, 165, 32], [255, 215, 0], [255, 255, 166], [153, 0, 204], [153, 141, 209], [204, 204, 255], [31, 212, 194], [3, 255, 237], [204, 255, 255], [86, 74, 147], [114, 114, 190], [184, 178, 255], [126, 138, 37], [189, 197, 117], [240, 230, 140], [120, 18, 134], [205, 62, 78], [0, 225, 0], [255, 100, 100], [0, 0, 0], [50, 50, 50], [180, 20, 30], [60, 25, 25], [25, 60, 60], [25, 60, 61], [25, 90, 60], [25, 120, 60], [25, 150, 60], [25, 180, 60], [25, 210, 60], [25, 150, 90], [25, 180, 90], [25, 210, 90], [180, 20, 20], [220, 20, 100], [140, 60, 60], [180, 220, 140], [140, 100, 180], [180, 20, 140], [140, 20, 140], [21, 10, 10], [225, 140, 140], [23, 60, 180], [180, 60, 180], [20, 220, 60], [60, 20, 140], [220, 180, 140], [65, 100, 20], [220, 60, 20], [60, 100, 60], [20, 60, 220], [100, 100, 60], [220, 180, 220], [20, 180, 140], [60, 140, 180], [25, 20, 140], [20, 60, 100], [60, 220, 20], [60, 20, 220], [220, 220, 100], [180, 60, 60], [60, 60, 220], [220, 60, 220], [65, 220, 60], [25, 140, 20], [13, 0, 250], [61, 20, 220], [61, 20, 60], [61, 60, 100], [25, 25, 25], [140, 20, 60], [220, 180, 20], [63, 180, 180], [221, 20, 10], [21, 220, 20], [183, 100, 20], [221, 20, 100], [221, 60, 140], [221, 20, 220], [61, 220, 220], [100, 200, 200], [10, 200, 200], [221, 220, 20], [141, 20, 100], [61, 220, 100], [21, 220, 60], [141, 60, 20], [143, 20, 220], [61, 20, 180], [101, 60, 220], [21, 20, 140], [221, 140, 20], [141, 100, 220], [101, 20, 20], [221, 100, 20], [181, 200, 20], [21, 180, 140], [101, 100, 180], [181, 220, 20], [21, 140, 200], [21, 20, 240], [21, 20, 200], [61, 180, 60], [61, 180, 250], [21, 20, 60], [101, 60, 60], [21, 220, 220], [21, 180, 180], [223, 220, 60], [221, 60, 60], [0, 0, 0], [50, 50, 50], [180, 20, 30], [60, 25, 25], [25, 60, 60], [180, 20, 20], [220, 20, 100], [140, 60, 60], [180, 220, 140], [140, 100, 180], [180, 20, 140], [140, 20, 140], [21, 10, 10], [225, 140, 140], [23, 60, 180], [180, 60, 180], [20, 220, 60], [60, 20, 140], [220, 180, 140], [65, 100, 20], [220, 60, 20], [60, 100, 60], [20, 60, 220], [100, 100, 60], [220, 180, 220], [20, 180, 140], [60, 140, 180], [25, 20, 140], [20, 60, 100], [60, 220, 20], [60, 20, 220], [220, 220, 100], [180, 60, 60], [60, 60, 220], [220, 60, 220], [65, 220, 60], [25, 140, 20], [13, 0, 250], [61, 20, 220], [61, 20, 60], [61, 60, 100], [25, 25, 25], [140, 20, 60], [220, 180, 20], [63, 180, 180], [221, 20, 10], [21, 220, 20], [183, 100, 20], [221, 20, 100], [221, 60, 140], [221, 20, 220], [61, 220, 220], [100, 200, 200], [10, 200, 200], [221, 220, 20], [141, 20, 100], [61, 220, 100], [21, 220, 60], [141, 60, 20], [143, 20, 220], [61, 20, 180], [101, 60, 220], [21, 20, 140], [221, 140, 20], [141, 100, 220], [101, 20, 20], [221, 100, 20], [181, 200, 20], [21, 180, 140], [101, 100, 180], [181, 220, 20], [21, 140, 200], [21, 20, 240], [21, 20, 200], [61, 180, 60], [61, 180, 250], [21, 20, 60], [101, 60, 60], [21, 220, 220], [21, 180, 180], [223, 220, 60], [221, 60, 60], [25, 60, 61], [25, 90, 60], [25, 120, 60], [25, 150, 60], [25, 180, 60], [25, 210, 60], [25, 150, 90], [25, 180, 90], [25, 210, 90], [0, 0, 0], [50, 50, 50], [180, 20, 30], [60, 25, 25], [25, 60, 60], [180, 20, 20], [220, 20, 100], [140, 60, 60], [180, 220, 140], [140, 100, 180], [180, 20, 140], [140, 20, 140], [21, 10, 10], [225, 140, 140], [23, 60, 180], [180, 60, 180], [20, 220, 60], [60, 20, 140], [220, 180, 140], [65, 100, 20], [220, 60, 20], [60, 100, 60], [20, 60, 220], [100, 100, 60], [220, 180, 220], [20, 180, 140], [60, 140, 180], [25, 20, 140], [20, 60, 100], [60, 220, 20], [60, 20, 220], [220, 220, 100], [180, 60, 60], [60, 60, 220], [220, 60, 220], [65, 220, 60], [25, 140, 20], [13, 0, 250], [61, 20, 220], [61, 20, 60], [61, 60, 100], [25, 25, 25], [140, 20, 60], [220, 180, 20], [63, 180, 180], [221, 20, 10], [21, 220, 20], [183, 100, 20], [221, 20, 100], [221, 60, 140], [221, 20, 220], [61, 220, 220], [100, 200, 200], [10, 200, 200], [221, 220, 20], [141, 20, 100], [61, 220, 100], [21, 220, 60], [141, 60, 20], [143, 20, 220], [61, 20, 180], [101, 60, 220], [21, 20, 140], [221, 140, 20], [141, 100, 220], [101, 20, 20], [221, 100, 20], [181, 200, 20], 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[61, 220, 220], [100, 200, 200], [10, 200, 200], [221, 220, 20], [141, 20, 100], [61, 220, 100], [21, 220, 60], [141, 60, 20], [143, 20, 220], [61, 20, 180], [101, 60, 220], [21, 20, 140], [221, 140, 20], [141, 100, 220], [101, 20, 20], [221, 100, 20], [181, 200, 20], [21, 180, 140], [101, 100, 180], [181, 220, 20], [21, 140, 200], [21, 20, 240], [21, 20, 200], [61, 180, 60], [61, 180, 250], [21, 20, 60], [101, 60, 60], [21, 220, 220], [21, 180, 180], [223, 220, 60], [221, 60, 60], [204, 102, 102], [204, 102, 102], [255, 255, 102], [153, 204, 0], [0, 153, 153], [204, 153, 255], [255, 153, 51], [204, 204, 204], [153, 255, 255], [102, 153, 255], [255, 255, 102], [153, 204, 0], [0, 153, 153], [204, 153, 255], [255, 153, 51], [204, 204, 204], [153, 255, 255], [102, 153, 255], [204, 102, 102], [204, 102, 102], [255, 255, 102], [153, 204, 0], [0, 153, 153], [204, 153, 255], [255, 153, 51], [204, 204, 204], [153, 255, 255], [102, 153, 255], [255, 255, 102], [153, 204, 0], [0, 153, 153], [204, 153, 255], [255, 153, 51], [204, 204, 204], [153, 255, 255], [102, 153, 255], [0, 255, 0], [255, 255, 0], [0, 255, 255], [0, 0, 255], [236, 16, 231], [237, 18, 232], [236, 13, 227], [236, 17, 228], [1, 255, 1], [2, 255, 1], [236, 14, 230], [237, 14, 230], [72, 132, 181], [243, 243, 243], [207, 63, 79], [121, 20, 135], [197, 60, 248], [2, 149, 2], [221, 249, 166], [232, 146, 35], [20, 60, 120], [250, 250, 0], [122, 187, 222], [237, 12, 177], [10, 49, 255], [205, 184, 144], [45, 205, 165], [117, 160, 175], [221, 217, 21], [20, 60, 120], [141, 21, 100], [225, 140, 141], [42, 201, 168], [168, 104, 162], [74, 130, 181], [242, 241, 240], [206, 65, 78], [120, 21, 133], [195, 61, 246], [3, 147, 6], [220, 251, 163], [232, 146, 33], [4, 114, 14], [121, 184, 220], [235, 11, 175], [12, 46, 250], [203, 182, 143], [42, 204, 167], [30, 5, 30], [30, 100, 45], [130, 100, 165], [105, 25, 5], [125, 70, 55], [225, 20, 105], [225, 20, 15], [30, 55, 30], [30, 150, 45], [130, 150, 165], [105, 75, 5], [125, 120, 55], [225, 70, 105], [225, 70, 15], [0, 0, 0], [23, 220, 60], [23, 60, 180], [63, 100, 60], [63, 20, 220], [13, 0, 250], [26, 60, 0], [26, 60, 75], [26, 60, 150], [25, 60, 250], [60, 25, 25], [180, 20, 20], [220, 20, 100], [140, 60, 60], [180, 220, 140], [140, 100, 180], [180, 20, 140], [23, 10, 10], [225, 140, 140], [180, 60, 180], [20, 220, 60], [60, 20, 140], [220, 180, 140], [65, 100, 20], [220, 60, 20], [20, 60, 220], [100, 100, 60], [220, 180, 220], [20, 180, 140], [60, 140, 180], [25, 20, 140], [20, 60, 100], [60, 220, 20], [60, 60, 220], [220, 60, 220], [65, 220, 60], [25, 140, 20], [220, 220, 100], [180, 60, 60], [61, 20, 220], [61, 20, 60], [61, 60, 100], [25, 25, 25], [140, 20, 60], [220, 180, 20], [63, 180, 180], [221, 20, 10], [221, 20, 100], [221, 60, 140], [221, 20, 220], [61, 220, 220], [100, 200, 200], [10, 200, 200], [221, 220, 20], [141, 20, 100], [61, 220, 100], [141, 60, 20], [143, 20, 220], [101, 60, 220], [21, 20, 140], [61, 20, 180], [221, 140, 20], [141, 100, 220], [221, 100, 20], [181, 200, 20], [101, 20, 20], [101, 100, 180], [181, 220, 20], [21, 140, 200], [21, 20, 240], [21, 20, 200], [21, 20, 60], [101, 60, 60], [21, 180, 180], [223, 220, 60], [221, 60, 60], [0, 0, 0], [23, 220, 60], [23, 60, 180], [63, 100, 60], [63, 20, 220], [13, 0, 250], [26, 60, 0], [26, 60, 75], [26, 60, 150], [25, 60, 250], [60, 25, 25], [180, 20, 20], [220, 20, 100], [140, 60, 60], [180, 220, 140], [140, 100, 180], [180, 20, 140], [23, 10, 10], [225, 140, 140], [180, 60, 180], [20, 220, 60], [60, 20, 140], [220, 180, 140], [65, 100, 20], [220, 60, 20], [20, 60, 220], [100, 100, 60], [220, 180, 220], [20, 180, 140], [60, 140, 180], [25, 20, 140], [20, 60, 100], [60, 220, 20], [60, 60, 220], [220, 60, 220], [65, 220, 60], [25, 140, 20], [220, 220, 100], [180, 60, 60], [61, 20, 220], [61, 20, 60], [61, 60, 100], [25, 25, 25], [140, 20, 60], [220, 180, 20], [63, 180, 180], [221, 20, 10], [221, 20, 100], [221, 60, 140], [221, 20, 220], [61, 220, 220], [100, 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misc_names = ['Unknown', 'Left-Cerebral-Exterior', 'Line-1', 'Line-2', 'Line-3', 'Left-undetermined', 'Left-F3orb', 'Left-lOg', 'Left-aOg', 'Left-mOg', 'Left-pOg', 'Left-Stellate', 'Left-Porg', 'Left-Aorg', 'Right-Cerebral-Exterior', 'Right-undetermined', 'Right-F3orb', 'Right-lOg', 'Right-aOg', 'Right-mOg', 'Right-pOg', 'Right-Stellate', 'Right-Porg', 'Right-Aorg', 'Left-Interior', 'Right-Interior', 'WM-hypointensities', 'non-WM-hypointensities', 'Left-non-WM-hypointensities', 'Right-non-WM-hypointensities', 'Left-F1', 'Right-F1', 'Left_future_WMSA', 'Right_future_WMSA', 'future_WMSA', 'Dura', 'Epidermis', 'Conn-Tissue', 'SC-Fat-Muscle', 'Cranium', 'CSF-SA', 'Muscle', 'Ear', 'Adipose', 'Spinal-Cord', 'Soft-Tissue', 'Nerve', 'Bone', 'Air', 'Orbital-Fat', 'Tongue', 'Nasal-Structures', 'Globe', 'Teeth', 'Left-Caudate-Putamen', 'Right-Caudate-Putamen', 'Left-Claustrum', 'Right-Claustrum', 'Cornea', 'Diploe', 'Vitreous-Humor', 'Lens', 'Aqueous-Humor', 'Outer-Table', 'Inner-Table', 'Periosteum', 'Endosteum', 'R-C-S', 'Iris', 'SC-Adipose-Muscle', 'SC-Tissue', 'Orbital-Adipose', 'Left-Cerebral-WM-unmyelinated', 'Right-Cerebral-WM-unmyelinated', 'Left-Cerebral-WM-myelinated', 'Right-Cerebral-WM-myelinated', 'Left-Subcortical-Gray-Matter', 'Right-Subcortical-Gray-Matter', 'Skull', 'Posterior-fossa', 'Scalp', 'Hematoma', 'Left-Basal-Ganglia', 'Right-Basal-Ganglia', 'Left-Cortical-Dysplasia', 'Right-Cortical-Dysplasia', 'Left-hippocampal_fissure', 'Left-CADG-head', 'Left-subiculum', 'Left-fimbria', 'Right-hippocampal_fissure', 'Right-CADG-head', 'Right-subiculum', 'Right-fimbria', 'alveus', 'perforant_pathway', 'parasubiculum', 'presubiculum', 'subiculum', 'CA1', 'CA2', 'CA3', 'CA4', 'GC-ML-DG', 'HATA', 'fimbria', 'lateral_ventricle', 'molecular_layer_HP', 'hippocampal_fissure', 'entorhinal_cortex', 'molecular_layer_subiculum', 'Amygdala', 'Cerebral_White_Matter', 'Cerebral_Cortex', 'Inf_Lat_Vent', 'Perirhinal', 'Cerebral_White_Matter_Edge', 'Background', 'Ectorhinal', 'HP_tail', 'Voxel-Unchanged', 'Aorta', 'Left-Common-IliacA', 'Right-Common-IliacA', 'Left-External-IliacA', 'Right-External-IliacA', 'Left-Internal-IliacA', 'Right-Internal-IliacA', 'Left-Lateral-SacralA', 'Right-Lateral-SacralA', 'Left-ObturatorA', 'Right-ObturatorA', 'Left-Internal-PudendalA', 'Right-Internal-PudendalA', 'Left-UmbilicalA', 'Right-UmbilicalA', 'Left-Inf-RectalA', 'Right-Inf-RectalA', 'Left-Common-IliacV', 'Right-Common-IliacV', 'Left-External-IliacV', 'Right-External-IliacV', 'Left-Internal-IliacV', 'Right-Internal-IliacV', 'Left-ObturatorV', 'Right-ObturatorV', 'Left-Internal-PudendalV', 'Right-Internal-PudendalV', 'Pos-Lymph', 'Neg-Lymph', 'V1', 'V2', 'BA44', 'BA45', 'BA4a', 'BA4p', 'BA6', 'BA2', 'BA1_old', 'BAun2', 'BA1', 'BA2b', 'BA3a', 'BA3b', 'MT', 'AIPS_AIP_l', 'AIPS_AIP_r', 'AIPS_VIP_l', 'AIPS_VIP_r', 'IPL_PFcm_l', 'IPL_PFcm_r', 'IPL_PF_l', 'IPL_PFm_l', 'IPL_PFm_r', 'IPL_PFop_l', 'IPL_PFop_r', 'IPL_PF_r', 'IPL_PFt_l', 'IPL_PFt_r', 'IPL_PGa_l', 'IPL_PGa_r', 'IPL_PGp_l', 'IPL_PGp_r', 'Visual_V3d_l', 'Visual_V3d_r', 'Visual_V4_l', 'Visual_V4_r', 'Visual_V5_b', 'Visual_VP_l', 'Visual_VP_r', 'wmsa', 'other_wmsa', 'Tumor', 'Cbm_Left_I_IV', 'Cbm_Right_I_IV', 'Cbm_Left_V', 'Cbm_Right_V', 'Cbm_Left_VI', 'Cbm_Vermis_VI', 'Cbm_Right_VI', 'Cbm_Left_CrusI', 'Cbm_Vermis_CrusI', 'Cbm_Right_CrusI', 'Cbm_Left_CrusII', 'Cbm_Vermis_CrusII', 'Cbm_Right_CrusII', 'Cbm_Left_VIIb', 'Cbm_Vermis_VIIb', 'Cbm_Right_VIIb', 'Cbm_Left_VIIIa', 'Cbm_Vermis_VIIIa', 'Cbm_Right_VIIIa', 'Cbm_Left_VIIIb', 'Cbm_Vermis_VIIIb', 'Cbm_Right_VIIIb', 'Cbm_Left_IX', 'Cbm_Vermis_IX', 'Cbm_Right_IX', 'Cbm_Left_X', 'Cbm_Vermis_X', 'Cbm_Right_X', 'Cbm_Right_I_V_med', 'Cbm_Right_I_V_mid', 'Cbm_Right_VI_med', 'Cbm_Right_VI_mid', 'Cbm_Right_VI_lat', 'Cbm_Right_CrusI_med', 'Cbm_Right_CrusI_mid', 'Cbm_Right_CrusI_lat', 'Cbm_Right_CrusII_med', 'Cbm_Right_CrusII_mid', 'Cbm_Right_CrusII_lat', 'Cbm_Right_7med', 'Cbm_Right_7mid', 'Cbm_Right_7lat', 'Cbm_Right_8med', 'Cbm_Right_8mid', 'Cbm_Right_8lat', 'Cbm_Right_PUNs', 'Cbm_Right_TONs', 'Cbm_Right_FLOs', 'Cbm_Left_I_V_med', 'Cbm_Left_I_V_mid', 'Cbm_Left_VI_med', 'Cbm_Left_VI_mid', 'Cbm_Left_VI_lat', 'Cbm_Left_CrusI_med', 'Cbm_Left_CrusI_mid', 'Cbm_Left_CrusI_lat', 'Cbm_Left_CrusII_med', 'Cbm_Left_CrusII_mid', 'Cbm_Left_CrusII_lat', 'Cbm_Left_7med', 'Cbm_Left_7mid', 'Cbm_Left_7lat', 'Cbm_Left_8med', 'Cbm_Left_8mid', 'Cbm_Left_8lat', 'Cbm_Left_PUNs', 'Cbm_Left_TONs', 'Cbm_Left_FLOs', 'CSF-FSL-FAST', 'GrayMatter-FSL-FAST', 'WhiteMatter-FSL-FAST', 'SUSPICIOUS', 'ctx-lh-Unknown', 'ctx-lh-Corpus_callosum', 'ctx-lh-G_and_S_Insula_ONLY_AVERAGE', 'ctx-lh-G_cingulate-Isthmus', 'ctx-lh-G_cingulate-Main_part', 'ctx-lh-G_cingulate-caudal_ACC', 'ctx-lh-G_cingulate-rostral_ACC', 'ctx-lh-G_cingulate-posterior', 'ctx-lh-S_cingulate-caudal_ACC', 'ctx-lh-S_cingulate-rostral_ACC', 'ctx-lh-S_cingulate-posterior', 'ctx-lh-S_pericallosal-caudal', 'ctx-lh-S_pericallosal-rostral', 'ctx-lh-S_pericallosal-posterior', 'ctx-lh-G_cuneus', 'ctx-lh-G_frontal_inf-Opercular_part', 'ctx-lh-G_frontal_inf-Orbital_part', 'ctx-lh-G_frontal_inf-Triangular_part', 'ctx-lh-G_frontal_middle', 'ctx-lh-G_frontal_superior', 'ctx-lh-G_frontomarginal', 'ctx-lh-G_insular_long', 'ctx-lh-G_insular_short', 'ctx-lh-G_and_S_occipital_inferior', 'ctx-lh-G_occipital_middle', 'ctx-lh-G_occipital_superior', 'ctx-lh-G_occipit-temp_lat-Or_fusiform', 'ctx-lh-G_occipit-temp_med-Lingual_part', 'ctx-lh-G_occipit-temp_med-Parahippocampal_part', 'ctx-lh-G_orbital', 'ctx-lh-G_paracentral', 'ctx-lh-G_parietal_inferior-Angular_part', 'ctx-lh-G_parietal_inferior-Supramarginal_part', 'ctx-lh-G_parietal_superior', 'ctx-lh-G_postcentral', 'ctx-lh-G_precentral', 'ctx-lh-G_precuneus', 'ctx-lh-G_rectus', 'ctx-lh-G_subcallosal', 'ctx-lh-G_subcentral', 'ctx-lh-G_temporal_inferior', 'ctx-lh-G_temporal_middle', 'ctx-lh-G_temp_sup-G_temp_transv_and_interm_S', 'ctx-lh-G_temp_sup-Lateral_aspect', 'ctx-lh-G_temp_sup-Planum_polare', 'ctx-lh-G_temp_sup-Planum_tempolare', 'ctx-lh-G_and_S_transverse_frontopolar', 'ctx-lh-Lat_Fissure-ant_sgt-ramus_horizontal', 'ctx-lh-Lat_Fissure-ant_sgt-ramus_vertical', 'ctx-lh-Lat_Fissure-post_sgt', 'ctx-lh-Medial_wall', 'ctx-lh-Pole_occipital', 'ctx-lh-Pole_temporal', 'ctx-lh-S_calcarine', 'ctx-lh-S_central', 'ctx-lh-S_central_insula', 'ctx-lh-S_cingulate-Main_part_and_Intracingulate', 'ctx-lh-S_cingulate-Marginalis_part', 'ctx-lh-S_circular_insula_anterior', 'ctx-lh-S_circular_insula_inferior', 'ctx-lh-S_circular_insula_superior', 'ctx-lh-S_collateral_transverse_ant', 'ctx-lh-S_collateral_transverse_post', 'ctx-lh-S_frontal_inferior', 'ctx-lh-S_frontal_middle', 'ctx-lh-S_frontal_superior', 'ctx-lh-S_frontomarginal', 'ctx-lh-S_intermedius_primus-Jensen', 'ctx-lh-S_intraparietal-and_Parietal_transverse', 'ctx-lh-S_occipital_anterior', 'ctx-lh-S_occipital_middle_and_Lunatus', 'ctx-lh-S_occipital_superior_and_transversalis', 'ctx-lh-S_occipito-temporal_lateral', 'ctx-lh-S_occipito-temporal_medial_and_S_Lingual', 'ctx-lh-S_orbital-H_shapped', 'ctx-lh-S_orbital_lateral', 'ctx-lh-S_orbital_medial-Or_olfactory', 'ctx-lh-S_paracentral', 'ctx-lh-S_parieto_occipital', 'ctx-lh-S_pericallosal', 'ctx-lh-S_postcentral', 'ctx-lh-S_precentral-Inferior-part', 'ctx-lh-S_precentral-Superior-part', 'ctx-lh-S_subcentral_ant', 'ctx-lh-S_subcentral_post', 'ctx-lh-S_suborbital', 'ctx-lh-S_subparietal', 'ctx-lh-S_supracingulate', 'ctx-lh-S_temporal_inferior', 'ctx-lh-S_temporal_superior', 'ctx-lh-S_temporal_transverse', 'ctx-rh-Unknown', 'ctx-rh-Corpus_callosum', 'ctx-rh-G_and_S_Insula_ONLY_AVERAGE', 'ctx-rh-G_cingulate-Isthmus', 'ctx-rh-G_cingulate-Main_part', 'ctx-rh-G_cuneus', 'ctx-rh-G_frontal_inf-Opercular_part', 'ctx-rh-G_frontal_inf-Orbital_part', 'ctx-rh-G_frontal_inf-Triangular_part', 'ctx-rh-G_frontal_middle', 'ctx-rh-G_frontal_superior', 'ctx-rh-G_frontomarginal', 'ctx-rh-G_insular_long', 'ctx-rh-G_insular_short', 'ctx-rh-G_and_S_occipital_inferior', 'ctx-rh-G_occipital_middle', 'ctx-rh-G_occipital_superior', 'ctx-rh-G_occipit-temp_lat-Or_fusiform', 'ctx-rh-G_occipit-temp_med-Lingual_part', 'ctx-rh-G_occipit-temp_med-Parahippocampal_part', 'ctx-rh-G_orbital', 'ctx-rh-G_paracentral', 'ctx-rh-G_parietal_inferior-Angular_part', 'ctx-rh-G_parietal_inferior-Supramarginal_part', 'ctx-rh-G_parietal_superior', 'ctx-rh-G_postcentral', 'ctx-rh-G_precentral', 'ctx-rh-G_precuneus', 'ctx-rh-G_rectus', 'ctx-rh-G_subcallosal', 'ctx-rh-G_subcentral', 'ctx-rh-G_temporal_inferior', 'ctx-rh-G_temporal_middle', 'ctx-rh-G_temp_sup-G_temp_transv_and_interm_S', 'ctx-rh-G_temp_sup-Lateral_aspect', 'ctx-rh-G_temp_sup-Planum_polare', 'ctx-rh-G_temp_sup-Planum_tempolare', 'ctx-rh-G_and_S_transverse_frontopolar', 'ctx-rh-Lat_Fissure-ant_sgt-ramus_horizontal', 'ctx-rh-Lat_Fissure-ant_sgt-ramus_vertical', 'ctx-rh-Lat_Fissure-post_sgt', 'ctx-rh-Medial_wall', 'ctx-rh-Pole_occipital', 'ctx-rh-Pole_temporal', 'ctx-rh-S_calcarine', 'ctx-rh-S_central', 'ctx-rh-S_central_insula', 'ctx-rh-S_cingulate-Main_part_and_Intracingulate', 'ctx-rh-S_cingulate-Marginalis_part', 'ctx-rh-S_circular_insula_anterior', 'ctx-rh-S_circular_insula_inferior', 'ctx-rh-S_circular_insula_superior', 'ctx-rh-S_collateral_transverse_ant', 'ctx-rh-S_collateral_transverse_post', 'ctx-rh-S_frontal_inferior', 'ctx-rh-S_frontal_middle', 'ctx-rh-S_frontal_superior', 'ctx-rh-S_frontomarginal', 'ctx-rh-S_intermedius_primus-Jensen', 'ctx-rh-S_intraparietal-and_Parietal_transverse', 'ctx-rh-S_occipital_anterior', 'ctx-rh-S_occipital_middle_and_Lunatus', 'ctx-rh-S_occipital_superior_and_transversalis', 'ctx-rh-S_occipito-temporal_lateral', 'ctx-rh-S_occipito-temporal_medial_and_S_Lingual', 'ctx-rh-S_orbital-H_shapped', 'ctx-rh-S_orbital_lateral', 'ctx-rh-S_orbital_medial-Or_olfactory', 'ctx-rh-S_paracentral', 'ctx-rh-S_parieto_occipital', 'ctx-rh-S_pericallosal', 'ctx-rh-S_postcentral', 'ctx-rh-S_precentral-Inferior-part', 'ctx-rh-S_precentral-Superior-part', 'ctx-rh-S_subcentral_ant', 'ctx-rh-S_subcentral_post', 'ctx-rh-S_suborbital', 'ctx-rh-S_subparietal', 'ctx-rh-S_supracingulate', 'ctx-rh-S_temporal_inferior', 'ctx-rh-S_temporal_superior', 'ctx-rh-S_temporal_transverse', 'ctx-rh-G_cingulate-caudal_ACC', 'ctx-rh-G_cingulate-rostral_ACC', 'ctx-rh-G_cingulate-posterior', 'ctx-rh-S_cingulate-caudal_ACC', 'ctx-rh-S_cingulate-rostral_ACC', 'ctx-rh-S_cingulate-posterior', 'ctx-rh-S_pericallosal-caudal', 'ctx-rh-S_pericallosal-rostral', 'ctx-rh-S_pericallosal-posterior', 'wm-lh-Unknown', 'wm-lh-Corpus_callosum', 'wm-lh-G_and_S_Insula_ONLY_AVERAGE', 'wm-lh-G_cingulate-Isthmus', 'wm-lh-G_cingulate-Main_part', 'wm-lh-G_cuneus', 'wm-lh-G_frontal_inf-Opercular_part', 'wm-lh-G_frontal_inf-Orbital_part', 'wm-lh-G_frontal_inf-Triangular_part', 'wm-lh-G_frontal_middle', 'wm-lh-G_frontal_superior', 'wm-lh-G_frontomarginal', 'wm-lh-G_insular_long', 'wm-lh-G_insular_short', 'wm-lh-G_and_S_occipital_inferior', 'wm-lh-G_occipital_middle', 'wm-lh-G_occipital_superior', 'wm-lh-G_occipit-temp_lat-Or_fusiform', 'wm-lh-G_occipit-temp_med-Lingual_part', 'wm-lh-G_occipit-temp_med-Parahippocampal_part', 'wm-lh-G_orbital', 'wm-lh-G_paracentral', 'wm-lh-G_parietal_inferior-Angular_part', 'wm-lh-G_parietal_inferior-Supramarginal_part', 'wm-lh-G_parietal_superior', 'wm-lh-G_postcentral', 'wm-lh-G_precentral', 'wm-lh-G_precuneus', 'wm-lh-G_rectus', 'wm-lh-G_subcallosal', 'wm-lh-G_subcentral', 'wm-lh-G_temporal_inferior', 'wm-lh-G_temporal_middle', 'wm-lh-G_temp_sup-G_temp_transv_and_interm_S', 'wm-lh-G_temp_sup-Lateral_aspect', 'wm-lh-G_temp_sup-Planum_polare', 'wm-lh-G_temp_sup-Planum_tempolare', 'wm-lh-G_and_S_transverse_frontopolar', 'wm-lh-Lat_Fissure-ant_sgt-ramus_horizontal', 'wm-lh-Lat_Fissure-ant_sgt-ramus_vertical', 'wm-lh-Lat_Fissure-post_sgt', 'wm-lh-Medial_wall', 'wm-lh-Pole_occipital', 'wm-lh-Pole_temporal', 'wm-lh-S_calcarine', 'wm-lh-S_central', 'wm-lh-S_central_insula', 'wm-lh-S_cingulate-Main_part_and_Intracingulate', 'wm-lh-S_cingulate-Marginalis_part', 'wm-lh-S_circular_insula_anterior', 'wm-lh-S_circular_insula_inferior', 'wm-lh-S_circular_insula_superior', 'wm-lh-S_collateral_transverse_ant', 'wm-lh-S_collateral_transverse_post', 'wm-lh-S_frontal_inferior', 'wm-lh-S_frontal_middle', 'wm-lh-S_frontal_superior', 'wm-lh-S_frontomarginal', 'wm-lh-S_intermedius_primus-Jensen', 'wm-lh-S_intraparietal-and_Parietal_transverse', 'wm-lh-S_occipital_anterior', 'wm-lh-S_occipital_middle_and_Lunatus', 'wm-lh-S_occipital_superior_and_transversalis', 'wm-lh-S_occipito-temporal_lateral', 'wm-lh-S_occipito-temporal_medial_and_S_Lingual', 'wm-lh-S_orbital-H_shapped', 'wm-lh-S_orbital_lateral', 'wm-lh-S_orbital_medial-Or_olfactory', 'wm-lh-S_paracentral', 'wm-lh-S_parieto_occipital', 'wm-lh-S_pericallosal', 'wm-lh-S_postcentral', 'wm-lh-S_precentral-Inferior-part', 'wm-lh-S_precentral-Superior-part', 'wm-lh-S_subcentral_ant', 'wm-lh-S_subcentral_post', 'wm-lh-S_suborbital', 'wm-lh-S_subparietal', 'wm-lh-S_supracingulate', 'wm-lh-S_temporal_inferior', 'wm-lh-S_temporal_superior', 'wm-lh-S_temporal_transverse', 'wm-rh-Unknown', 'wm-rh-Corpus_callosum', 'wm-rh-G_and_S_Insula_ONLY_AVERAGE', 'wm-rh-G_cingulate-Isthmus', 'wm-rh-G_cingulate-Main_part', 'wm-rh-G_cuneus', 'wm-rh-G_frontal_inf-Opercular_part', 'wm-rh-G_frontal_inf-Orbital_part', 'wm-rh-G_frontal_inf-Triangular_part', 'wm-rh-G_frontal_middle', 'wm-rh-G_frontal_superior', 'wm-rh-G_frontomarginal', 'wm-rh-G_insular_long', 'wm-rh-G_insular_short', 'wm-rh-G_and_S_occipital_inferior', 'wm-rh-G_occipital_middle', 'wm-rh-G_occipital_superior', 'wm-rh-G_occipit-temp_lat-Or_fusiform', 'wm-rh-G_occipit-temp_med-Lingual_part', 'wm-rh-G_occipit-temp_med-Parahippocampal_part', 'wm-rh-G_orbital', 'wm-rh-G_paracentral', 'wm-rh-G_parietal_inferior-Angular_part', 'wm-rh-G_parietal_inferior-Supramarginal_part', 'wm-rh-G_parietal_superior', 'wm-rh-G_postcentral', 'wm-rh-G_precentral', 'wm-rh-G_precuneus', 'wm-rh-G_rectus', 'wm-rh-G_subcallosal', 'wm-rh-G_subcentral', 'wm-rh-G_temporal_inferior', 'wm-rh-G_temporal_middle', 'wm-rh-G_temp_sup-G_temp_transv_and_interm_S', 'wm-rh-G_temp_sup-Lateral_aspect', 'wm-rh-G_temp_sup-Planum_polare', 'wm-rh-G_temp_sup-Planum_tempolare', 'wm-rh-G_and_S_transverse_frontopolar', 'wm-rh-Lat_Fissure-ant_sgt-ramus_horizontal', 'wm-rh-Lat_Fissure-ant_sgt-ramus_vertical', 'wm-rh-Lat_Fissure-post_sgt', 'wm-rh-Medial_wall', 'wm-rh-Pole_occipital', 'wm-rh-Pole_temporal', 'wm-rh-S_calcarine', 'wm-rh-S_central', 'wm-rh-S_central_insula', 'wm-rh-S_cingulate-Main_part_and_Intracingulate', 'wm-rh-S_cingulate-Marginalis_part', 'wm-rh-S_circular_insula_anterior', 'wm-rh-S_circular_insula_inferior', 'wm-rh-S_circular_insula_superior', 'wm-rh-S_collateral_transverse_ant', 'wm-rh-S_collateral_transverse_post', 'wm-rh-S_frontal_inferior', 'wm-rh-S_frontal_middle', 'wm-rh-S_frontal_superior', 'wm-rh-S_frontomarginal', 'wm-rh-S_intermedius_primus-Jensen', 'wm-rh-S_intraparietal-and_Parietal_transverse', 'wm-rh-S_occipital_anterior', 'wm-rh-S_occipital_middle_and_Lunatus', 'wm-rh-S_occipital_superior_and_transversalis', 'wm-rh-S_occipito-temporal_lateral', 'wm-rh-S_occipito-temporal_medial_and_S_Lingual', 'wm-rh-S_orbital-H_shapped', 'wm-rh-S_orbital_lateral', 'wm-rh-S_orbital_medial-Or_olfactory', 'wm-rh-S_paracentral', 'wm-rh-S_parieto_occipital', 'wm-rh-S_pericallosal', 'wm-rh-S_postcentral', 'wm-rh-S_precentral-Inferior-part', 'wm-rh-S_precentral-Superior-part', 'wm-rh-S_subcentral_ant', 'wm-rh-S_subcentral_post', 'wm-rh-S_suborbital', 'wm-rh-S_subparietal', 'wm-rh-S_supracingulate', 'wm-rh-S_temporal_inferior', 'wm-rh-S_temporal_superior', 'wm-rh-S_temporal_transverse', 'fmajor', 'fminor', 'lh.atr', 'lh.cab', 'lh.ccg', 'lh.cst', 'lh.ilf', 'lh.slfp', 'lh.slft', 'lh.unc', 'rh.atr', 'rh.cab', 'rh.ccg', 'rh.cst', 'rh.ilf', 'rh.slfp', 'rh.slft', 'rh.unc', 'CC-ForcepsMajor', 'CC-ForcepsMinor', 'LAntThalRadiation', 'LCingulumAngBundle', 'LCingulumCingGyrus', 'LCorticospinalTract', 'LInfLongFas', 'LSupLongFasParietal', 'LSupLongFasTemporal', 'LUncinateFas', 'RAntThalRadiation', 'RCingulumAngBundle', 'RCingulumCingGyrus', 'RCorticospinalTract', 'RInfLongFas', 'RSupLongFasParietal', 'RSupLongFasTemporal', 'RUncinateFas', 'CST-orig', 'CST-hammer', 'CST-CVS', 'CST-flirt', 'Left-SLF1', 'Right-SLF1', 'Left-SLF3', 'Right-SLF3', 'Left-CST', 'Right-CST', 'Left-SLF2', 'Right-SLF2', 'Lateral-nucleus', 'Basolateral-nucleus', 'Basal-nucleus', 'Centromedial-nucleus', 'Central-nucleus', 'Medial-nucleus', 'Cortical-nucleus', 'Accessory-Basal-nucleus', 'Corticoamygdaloid-transitio', 'Anterior-amygdaloid-area-AAA', 'Fusion-amygdala-HP-FAH', 'Hippocampal-amygdala-transition-HATA', 'Endopiriform-nucleus', 'Lateral-nucleus-olfactory-tract', 'Paralaminar-nucleus', 'Intercalated-nucleus', 'Prepiriform-cortex', 'Periamygdaloid-cortex', 'Envelope-Amygdala', 'Extranuclear-Amydala', 'Brainstem-inferior-colliculus', 'Brainstem-cochlear-nucleus', 'Thalamus-Anterior', 'Thalamus-Ventral-anterior', 'Thalamus-Lateral-dorsal', 'Thalamus-Lateral-posterior', 'Thalamus-Ventral-lateral', 'Thalamus-Ventral-posterior-medial', 'Thalamus-Ventral-posterior-lateral', 'Thalamus-intralaminar', 'Thalamus-centromedian', 'Thalamus-mediodorsal', 'Thalamus-medial', 'Thalamus-pulvinar', 'Thalamus-lateral-geniculate', 'Thalamus-medial-geniculate', 'ctx-lh-prefrontal', 'ctx-lh-primary-motor', 'ctx-lh-premotor', 'ctx-lh-temporal', 'ctx-lh-posterior-parietal', 'ctx-lh-prim-sec-somatosensory', 'ctx-lh-occipital', 'ctx-rh-prefrontal', 'ctx-rh-primary-motor', 'ctx-rh-premotor', 'ctx-rh-temporal', 'ctx-rh-posterior-parietal', 'ctx-rh-prim-sec-somatosensory', 'ctx-rh-occipital', 'ctx_lh_Unknown', 'ctx_lh_G_and_S_frontomargin', 'ctx_lh_G_and_S_occipital_inf', 'ctx_lh_G_and_S_paracentral', 'ctx_lh_G_and_S_subcentral', 'ctx_lh_G_and_S_transv_frontopol', 'ctx_lh_G_and_S_cingul-Ant', 'ctx_lh_G_and_S_cingul-Mid-Ant', 'ctx_lh_G_and_S_cingul-Mid-Post', 'ctx_lh_G_cingul-Post-dorsal', 'ctx_lh_G_cingul-Post-ventral', 'ctx_lh_G_cuneus', 'ctx_lh_G_front_inf-Opercular', 'ctx_lh_G_front_inf-Orbital', 'ctx_lh_G_front_inf-Triangul', 'ctx_lh_G_front_middle', 'ctx_lh_G_front_sup', 'ctx_lh_G_Ins_lg_and_S_cent_ins', 'ctx_lh_G_insular_short', 'ctx_lh_G_occipital_middle', 'ctx_lh_G_occipital_sup', 'ctx_lh_G_oc-temp_lat-fusifor', 'ctx_lh_G_oc-temp_med-Lingual', 'ctx_lh_G_oc-temp_med-Parahip', 'ctx_lh_G_orbital', 'ctx_lh_G_pariet_inf-Angular', 'ctx_lh_G_pariet_inf-Supramar', 'ctx_lh_G_parietal_sup', 'ctx_lh_G_postcentral', 'ctx_lh_G_precentral', 'ctx_lh_G_precuneus', 'ctx_lh_G_rectus', 'ctx_lh_G_subcallosal', 'ctx_lh_G_temp_sup-G_T_transv', 'ctx_lh_G_temp_sup-Lateral', 'ctx_lh_G_temp_sup-Plan_polar', 'ctx_lh_G_temp_sup-Plan_tempo', 'ctx_lh_G_temporal_inf', 'ctx_lh_G_temporal_middle', 'ctx_lh_Lat_Fis-ant-Horizont', 'ctx_lh_Lat_Fis-ant-Vertical', 'ctx_lh_Lat_Fis-post', 'ctx_lh_Medial_wall', 'ctx_lh_Pole_occipital', 'ctx_lh_Pole_temporal', 'ctx_lh_S_calcarine', 'ctx_lh_S_central', 'ctx_lh_S_cingul-Marginalis', 'ctx_lh_S_circular_insula_ant', 'ctx_lh_S_circular_insula_inf', 'ctx_lh_S_circular_insula_sup', 'ctx_lh_S_collat_transv_ant', 'ctx_lh_S_collat_transv_post', 'ctx_lh_S_front_inf', 'ctx_lh_S_front_middle', 'ctx_lh_S_front_sup', 'ctx_lh_S_interm_prim-Jensen', 'ctx_lh_S_intrapariet_and_P_trans', 'ctx_lh_S_oc_middle_and_Lunatus', 'ctx_lh_S_oc_sup_and_transversal', 'ctx_lh_S_occipital_ant', 'ctx_lh_S_oc-temp_lat', 'ctx_lh_S_oc-temp_med_and_Lingual', 'ctx_lh_S_orbital_lateral', 'ctx_lh_S_orbital_med-olfact', 'ctx_lh_S_orbital-H_Shaped', 'ctx_lh_S_parieto_occipital', 'ctx_lh_S_pericallosal', 'ctx_lh_S_postcentral', 'ctx_lh_S_precentral-inf-part', 'ctx_lh_S_precentral-sup-part', 'ctx_lh_S_suborbital', 'ctx_lh_S_subparietal', 'ctx_lh_S_temporal_inf', 'ctx_lh_S_temporal_sup', 'ctx_lh_S_temporal_transverse', 'ctx_rh_Unknown', 'ctx_rh_G_and_S_frontomargin', 'ctx_rh_G_and_S_occipital_inf', 'ctx_rh_G_and_S_paracentral', 'ctx_rh_G_and_S_subcentral', 'ctx_rh_G_and_S_transv_frontopol', 'ctx_rh_G_and_S_cingul-Ant', 'ctx_rh_G_and_S_cingul-Mid-Ant', 'ctx_rh_G_and_S_cingul-Mid-Post', 'ctx_rh_G_cingul-Post-dorsal', 'ctx_rh_G_cingul-Post-ventral', 'ctx_rh_G_cuneus', 'ctx_rh_G_front_inf-Opercular', 'ctx_rh_G_front_inf-Orbital', 'ctx_rh_G_front_inf-Triangul', 'ctx_rh_G_front_middle', 'ctx_rh_G_front_sup', 'ctx_rh_G_Ins_lg_and_S_cent_ins', 'ctx_rh_G_insular_short', 'ctx_rh_G_occipital_middle', 'ctx_rh_G_occipital_sup', 'ctx_rh_G_oc-temp_lat-fusifor', 'ctx_rh_G_oc-temp_med-Lingual', 'ctx_rh_G_oc-temp_med-Parahip', 'ctx_rh_G_orbital', 'ctx_rh_G_pariet_inf-Angular', 'ctx_rh_G_pariet_inf-Supramar', 'ctx_rh_G_parietal_sup', 'ctx_rh_G_postcentral', 'ctx_rh_G_precentral', 'ctx_rh_G_precuneus', 'ctx_rh_G_rectus', 'ctx_rh_G_subcallosal', 'ctx_rh_G_temp_sup-G_T_transv', 'ctx_rh_G_temp_sup-Lateral', 'ctx_rh_G_temp_sup-Plan_polar', 'ctx_rh_G_temp_sup-Plan_tempo', 'ctx_rh_G_temporal_inf', 'ctx_rh_G_temporal_middle', 'ctx_rh_Lat_Fis-ant-Horizont', 'ctx_rh_Lat_Fis-ant-Vertical', 'ctx_rh_Lat_Fis-post', 'ctx_rh_Medial_wall', 'ctx_rh_Pole_occipital', 'ctx_rh_Pole_temporal', 'ctx_rh_S_calcarine', 'ctx_rh_S_central', 'ctx_rh_S_cingul-Marginalis', 'ctx_rh_S_circular_insula_ant', 'ctx_rh_S_circular_insula_inf', 'ctx_rh_S_circular_insula_sup', 'ctx_rh_S_collat_transv_ant', 'ctx_rh_S_collat_transv_post', 'ctx_rh_S_front_inf', 'ctx_rh_S_front_middle', 'ctx_rh_S_front_sup', 'ctx_rh_S_interm_prim-Jensen', 'ctx_rh_S_intrapariet_and_P_trans', 'ctx_rh_S_oc_middle_and_Lunatus', 'ctx_rh_S_oc_sup_and_transversal', 'ctx_rh_S_occipital_ant', 'ctx_rh_S_oc-temp_lat', 'ctx_rh_S_oc-temp_med_and_Lingual', 'ctx_rh_S_orbital_lateral', 'ctx_rh_S_orbital_med-olfact', 'ctx_rh_S_orbital-H_Shaped', 'ctx_rh_S_parieto_occipital', 'ctx_rh_S_pericallosal', 'ctx_rh_S_postcentral', 'ctx_rh_S_precentral-inf-part', 'ctx_rh_S_precentral-sup-part', 'ctx_rh_S_suborbital', 'ctx_rh_S_subparietal', 'ctx_rh_S_temporal_inf', 'ctx_rh_S_temporal_sup', 'ctx_rh_S_temporal_transverse', 'wm_lh_Unknown', 'wm_lh_G_and_S_frontomargin', 'wm_lh_G_and_S_occipital_inf', 'wm_lh_G_and_S_paracentral', 'wm_lh_G_and_S_subcentral', 'wm_lh_G_and_S_transv_frontopol', 'wm_lh_G_and_S_cingul-Ant', 'wm_lh_G_and_S_cingul-Mid-Ant', 'wm_lh_G_and_S_cingul-Mid-Post', 'wm_lh_G_cingul-Post-dorsal', 'wm_lh_G_cingul-Post-ventral', 'wm_lh_G_cuneus', 'wm_lh_G_front_inf-Opercular', 'wm_lh_G_front_inf-Orbital', 'wm_lh_G_front_inf-Triangul', 'wm_lh_G_front_middle', 'wm_lh_G_front_sup', 'wm_lh_G_Ins_lg_and_S_cent_ins', 'wm_lh_G_insular_short', 'wm_lh_G_occipital_middle', 'wm_lh_G_occipital_sup', 'wm_lh_G_oc-temp_lat-fusifor', 'wm_lh_G_oc-temp_med-Lingual', 'wm_lh_G_oc-temp_med-Parahip', 'wm_lh_G_orbital', 'wm_lh_G_pariet_inf-Angular', 'wm_lh_G_pariet_inf-Supramar', 'wm_lh_G_parietal_sup', 'wm_lh_G_postcentral', 'wm_lh_G_precentral', 'wm_lh_G_precuneus', 'wm_lh_G_rectus', 'wm_lh_G_subcallosal', 'wm_lh_G_temp_sup-G_T_transv', 'wm_lh_G_temp_sup-Lateral', 'wm_lh_G_temp_sup-Plan_polar', 'wm_lh_G_temp_sup-Plan_tempo', 'wm_lh_G_temporal_inf', 'wm_lh_G_temporal_middle', 'wm_lh_Lat_Fis-ant-Horizont', 'wm_lh_Lat_Fis-ant-Vertical', 'wm_lh_Lat_Fis-post', 'wm_lh_Medial_wall', 'wm_lh_Pole_occipital', 'wm_lh_Pole_temporal', 'wm_lh_S_calcarine', 'wm_lh_S_central', 'wm_lh_S_cingul-Marginalis', 'wm_lh_S_circular_insula_ant', 'wm_lh_S_circular_insula_inf', 'wm_lh_S_circular_insula_sup', 'wm_lh_S_collat_transv_ant', 'wm_lh_S_collat_transv_post', 'wm_lh_S_front_inf', 'wm_lh_S_front_middle', 'wm_lh_S_front_sup', 'wm_lh_S_interm_prim-Jensen', 'wm_lh_S_intrapariet_and_P_trans', 'wm_lh_S_oc_middle_and_Lunatus', 'wm_lh_S_oc_sup_and_transversal', 'wm_lh_S_occipital_ant', 'wm_lh_S_oc-temp_lat', 'wm_lh_S_oc-temp_med_and_Lingual', 'wm_lh_S_orbital_lateral', 'wm_lh_S_orbital_med-olfact', 'wm_lh_S_orbital-H_Shaped', 'wm_lh_S_parieto_occipital', 'wm_lh_S_pericallosal', 'wm_lh_S_postcentral', 'wm_lh_S_precentral-inf-part', 'wm_lh_S_precentral-sup-part', 'wm_lh_S_suborbital', 'wm_lh_S_subparietal', 'wm_lh_S_temporal_inf', 'wm_lh_S_temporal_sup', 'wm_lh_S_temporal_transverse', 'wm_rh_Unknown', 'wm_rh_G_and_S_frontomargin', 'wm_rh_G_and_S_occipital_inf', 'wm_rh_G_and_S_paracentral', 'wm_rh_G_and_S_subcentral', 'wm_rh_G_and_S_transv_frontopol', 'wm_rh_G_and_S_cingul-Ant', 'wm_rh_G_and_S_cingul-Mid-Ant', 'wm_rh_G_and_S_cingul-Mid-Post', 'wm_rh_G_cingul-Post-dorsal', 'wm_rh_G_cingul-Post-ventral', 'wm_rh_G_cuneus', 'wm_rh_G_front_inf-Opercular', 'wm_rh_G_front_inf-Orbital', 'wm_rh_G_front_inf-Triangul', 'wm_rh_G_front_middle', 'wm_rh_G_front_sup', 'wm_rh_G_Ins_lg_and_S_cent_ins', 'wm_rh_G_insular_short', 'wm_rh_G_occipital_middle', 'wm_rh_G_occipital_sup', 'wm_rh_G_oc-temp_lat-fusifor', 'wm_rh_G_oc-temp_med-Lingual', 'wm_rh_G_oc-temp_med-Parahip', 'wm_rh_G_orbital', 'wm_rh_G_pariet_inf-Angular', 'wm_rh_G_pariet_inf-Supramar', 'wm_rh_G_parietal_sup', 'wm_rh_G_postcentral', 'wm_rh_G_precentral', 'wm_rh_G_precuneus', 'wm_rh_G_rectus', 'wm_rh_G_subcallosal', 'wm_rh_G_temp_sup-G_T_transv', 'wm_rh_G_temp_sup-Lateral', 'wm_rh_G_temp_sup-Plan_polar', 'wm_rh_G_temp_sup-Plan_tempo', 'wm_rh_G_temporal_inf', 'wm_rh_G_temporal_middle', 'wm_rh_Lat_Fis-ant-Horizont', 'wm_rh_Lat_Fis-ant-Vertical', 'wm_rh_Lat_Fis-post', 'wm_rh_Medial_wall', 'wm_rh_Pole_occipital', 'wm_rh_Pole_temporal', 'wm_rh_S_calcarine', 'wm_rh_S_central', 'wm_rh_S_cingul-Marginalis', 'wm_rh_S_circular_insula_ant', 'wm_rh_S_circular_insula_inf', 'wm_rh_S_circular_insula_sup', 'wm_rh_S_collat_transv_ant', 'wm_rh_S_collat_transv_post', 'wm_rh_S_front_inf', 'wm_rh_S_front_middle', 'wm_rh_S_front_sup', 'wm_rh_S_interm_prim-Jensen', 'wm_rh_S_intrapariet_and_P_trans', 'wm_rh_S_oc_middle_and_Lunatus', 'wm_rh_S_oc_sup_and_transversal', 'wm_rh_S_occipital_ant', 'wm_rh_S_oc-temp_lat', 'wm_rh_S_oc-temp_med_and_Lingual', 'wm_rh_S_orbital_lateral', 'wm_rh_S_orbital_med-olfact', 'wm_rh_S_orbital-H_Shaped', 'wm_rh_S_parieto_occipital', 'wm_rh_S_pericallosal', 'wm_rh_S_postcentral', 'wm_rh_S_precentral-inf-part', 'wm_rh_S_precentral-sup-part', 'wm_rh_S_suborbital', 'wm_rh_S_subparietal', 'wm_rh_S_temporal_inf', 'wm_rh_S_temporal_sup', 'wm_rh_S_temporal_transverse']¶

misc_numbers = [0, 1, 21, 22, 23, 29, 32, 33, 34, 35, 36, 37, 38, 39, 40, 61, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 77, 80, 81, 82, 83, 84, 86, 87, 88, 98, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 176, 180, 181, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 256, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 498, 499, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 701, 702, 703, 999, 1100, 1101, 1102, 1103, 1104, 1200, 1201, 1202, 1205, 1206, 1207, 1210, 1211, 1212, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2119, 2120, 2121, 2122, 2123, 2124, 2125, 2126, 2127, 2128, 2129, 2130, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2138, 2139, 2140, 2141, 2142, 2143, 2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2166, 2167, 2168, 2169, 2170, 2171, 2172, 2173, 2174, 2175, 2176, 2177, 2178, 2179, 2180, 2181, 2200, 2201, 2202, 2205, 2206, 2207, 2210, 2211, 2212, 3100, 3101, 3102, 3103, 3104, 3105, 3106, 3107, 3108, 3109, 3110, 3111, 3112, 3113, 3114, 3115, 3116, 3117, 3118, 3119, 3120, 3121, 3122, 3123, 3124, 3125, 3126, 3127, 3128, 3129, 3130, 3131, 3132, 3133, 3134, 3135, 3136, 3137, 3138, 3139, 3140, 3141, 3142, 3143, 3144, 3145, 3146, 3147, 3148, 3149, 3150, 3151, 3152, 3153, 3154, 3155, 3156, 3157, 3158, 3159, 3160, 3161, 3162, 3163, 3164, 3165, 3166, 3167, 3168, 3169, 3170, 3171, 3172, 3173, 3174, 3175, 3176, 3177, 3178, 3179, 3180, 3181, 4100, 4101, 4102, 4103, 4104, 4105, 4106, 4107, 4108, 4109, 4110, 4111, 4112, 4113, 4114, 4115, 4116, 4117, 4118, 4119, 4120, 4121, 4122, 4123, 4124, 4125, 4126, 4127, 4128, 4129, 4130, 4131, 4132, 4133, 4134, 4135, 4136, 4137, 4138, 4139, 4140, 4141, 4142, 4143, 4144, 4145, 4146, 4147, 4148, 4149, 4150, 4151, 4152, 4153, 4154, 4155, 4156, 4157, 4158, 4159, 4160, 4161, 4162, 4163, 4164, 4165, 4166, 4167, 4168, 4169, 4170, 4171, 4172, 4173, 4174, 4175, 4176, 4177, 4178, 4179, 4180, 4181, 5100, 5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5200, 5201, 5202, 5203, 5204, 5205, 5206, 5207, 5208, 5209, 5210, 5211, 5212, 5213, 5214, 5215, 5216, 5217, 6000, 6001, 6002, 6003, 6010, 6020, 6030, 6040, 6050, 6060, 6070, 6080, 7001, 7002, 7003, 7004, 7005, 7006, 7007, 7008, 7009, 7010, 7011, 7012, 7013, 7014, 7015, 7016, 7017, 7018, 7019, 7020, 7100, 7101, 8001, 8002, 8003, 8004, 8005, 8006, 8007, 8008, 8009, 8010, 8011, 8012, 8013, 8014, 9000, 9001, 9002, 9003, 9004, 9005, 9006, 9500, 9501, 9502, 9503, 9504, 9505, 9506, 11100, 11101, 11102, 11103, 11104, 11105, 11106, 11107, 11108, 11109, 11110, 11111, 11112, 11113, 11114, 11115, 11116, 11117, 11118, 11119, 11120, 11121, 11122, 11123, 11124, 11125, 11126, 11127, 11128, 11129, 11130, 11131, 11132, 11133, 11134, 11135, 11136, 11137, 11138, 11139, 11140, 11141, 11142, 11143, 11144, 11145, 11146, 11147, 11148, 11149, 11150, 11151, 11152, 11153, 11154, 11155, 11156, 11157, 11158, 11159, 11160, 11161, 11162, 11163, 11164, 11165, 11166, 11167, 11168, 11169, 11170, 11171, 11172, 11173, 11174, 11175, 12100, 12101, 12102, 12103, 12104, 12105, 12106, 12107, 12108, 12109, 12110, 12111, 12112, 12113, 12114, 12115, 12116, 12117, 12118, 12119, 12120, 12121, 12122, 12123, 12124, 12125, 12126, 12127, 12128, 12129, 12130, 12131, 12132, 12133, 12134, 12135, 12136, 12137, 12138, 12139, 12140, 12141, 12142, 12143, 12144, 12145, 12146, 12147, 12148, 12149, 12150, 12151, 12152, 12153, 12154, 12155, 12156, 12157, 12158, 12159, 12160, 12161, 12162, 12163, 12164, 12165, 12166, 12167, 12168, 12169, 12170, 12171, 12172, 12173, 12174, 12175, 13100, 13101, 13102, 13103, 13104, 13105, 13106, 13107, 13108, 13109, 13110, 13111, 13112, 13113, 13114, 13115, 13116, 13117, 13118, 13119, 13120, 13121, 13122, 13123, 13124, 13125, 13126, 13127, 13128, 13129, 13130, 13131, 13132, 13133, 13134, 13135, 13136, 13137, 13138, 13139, 13140, 13141, 13142, 13143, 13144, 13145, 13146, 13147, 13148, 13149, 13150, 13151, 13152, 13153, 13154, 13155, 13156, 13157, 13158, 13159, 13160, 13161, 13162, 13163, 13164, 13165, 13166, 13167, 13168, 13169, 13170, 13171, 13172, 13173, 13174, 13175, 14100, 14101, 14102, 14103, 14104, 14105, 14106, 14107, 14108, 14109, 14110, 14111, 14112, 14113, 14114, 14115, 14116, 14117, 14118, 14119, 14120, 14121, 14122, 14123, 14124, 14125, 14126, 14127, 14128, 14129, 14130, 14131, 14132, 14133, 14134, 14135, 14136, 14137, 14138, 14139, 14140, 14141, 14142, 14143, 14144, 14145, 14146, 14147, 14148, 14149, 14150, 14151, 14152, 14153, 14154, 14155, 14156, 14157, 14158, 14159, 14160, 14161, 14162, 14163, 14164, 14165, 14166, 14167, 14168, 14169, 14170, 14171, 14172, 14173, 14174, 14175]¶

n = 14175¶

names = ['Unknown', 'Left-Cerebral-Exterior', 'Left-Cerebral-White-Matter', 'Left-Cerebral-Cortex', 'Left-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Left-Cerebellum-Exterior', 'Left-Cerebellum-White-Matter', 'Left-Cerebellum-Cortex', 'Left-Thalamus', 'Left-Thalamus-Proper', 'Left-Caudate', 'Left-Putamen', 'Left-Pallidum', '3rd-Ventricle', '4th-Ventricle', 'Brain-Stem', 'Left-Hippocampus', 'Left-Amygdala', 'Left-Insula', 'Left-Operculum', 'Line-1', 'Line-2', 'Line-3', 'CSF', 'Left-Lesion', 'Left-Accumbens-area', 'Left-Substancia-Nigra', 'Left-VentralDC', 'Left-undetermined', 'Left-vessel', 'Left-choroid-plexus', 'Left-F3orb', 'Left-lOg', 'Left-aOg', 'Left-mOg', 'Left-pOg', 'Left-Stellate', 'Left-Porg', 'Left-Aorg', 'Right-Cerebral-Exterior', 'Right-Cerebral-White-Matter', 'Right-Cerebral-Cortex', 'Right-Lateral-Ventricle', 'Right-Inf-Lat-Vent', 'Right-Cerebellum-Exterior', 'Right-Cerebellum-White-Matter', 'Right-Cerebellum-Cortex', 'Right-Thalamus', 'Right-Thalamus-Proper', 'Right-Caudate', 'Right-Putamen', 'Right-Pallidum', 'Right-Hippocampus', 'Right-Amygdala', 'Right-Insula', 'Right-Operculum', 'Right-Lesion', 'Right-Accumbens-area', 'Right-Substancia-Nigra', 'Right-VentralDC', 'Right-undetermined', 'Right-vessel', 'Right-choroid-plexus', 'Right-F3orb', 'Right-lOg', 'Right-aOg', 'Right-mOg', 'Right-pOg', 'Right-Stellate', 'Right-Porg', 'Right-Aorg', '5th-Ventricle', 'Left-Interior', 'Right-Interior', 'WM-hypointensities', 'Left-WM-hypointensities', 'Right-WM-hypointensities', 'non-WM-hypointensities', 'Left-non-WM-hypointensities', 'Right-non-WM-hypointensities', 'Left-F1', 'Right-F1', 'Optic-Chiasm', 'Corpus_Callosum', 'Left_future_WMSA', 'Right_future_WMSA', 'future_WMSA', 'Left-Amygdala-Anterior', 'Right-Amygdala-Anterior', 'Dura', 'Left-wm-intensity-abnormality', 'Left-caudate-intensity-abnormality', 'Left-putamen-intensity-abnormality', 'Left-accumbens-intensity-abnormality', 'Left-pallidum-intensity-abnormality', 'Left-amygdala-intensity-abnormality', 'Left-hippocampus-intensity-abnormality', 'Left-thalamus-intensity-abnormality', 'Left-VDC-intensity-abnormality', 'Right-wm-intensity-abnormality', 'Right-caudate-intensity-abnormality', 'Right-putamen-intensity-abnormality', 'Right-accumbens-intensity-abnormality', 'Right-pallidum-intensity-abnormality', 'Right-amygdala-intensity-abnormality', 'Right-hippocampus-intensity-abnormality', 'Right-thalamus-intensity-abnormality', 'Right-VDC-intensity-abnormality', 'Epidermis', 'Conn-Tissue', 'SC-Fat-Muscle', 'Cranium', 'CSF-SA', 'Muscle', 'Ear', 'Adipose', 'Spinal-Cord', 'Soft-Tissue', 'Nerve', 'Bone', 'Air', 'Orbital-Fat', 'Tongue', 'Nasal-Structures', 'Globe', 'Teeth', 'Left-Caudate-Putamen', 'Right-Caudate-Putamen', 'Left-Claustrum', 'Right-Claustrum', 'Cornea', 'Diploe', 'Vitreous-Humor', 'Lens', 'Aqueous-Humor', 'Outer-Table', 'Inner-Table', 'Periosteum', 'Endosteum', 'R-C-S', 'Iris', 'SC-Adipose-Muscle', 'SC-Tissue', 'Orbital-Adipose', 'Left-IntCapsule-Ant', 'Right-IntCapsule-Ant', 'Left-IntCapsule-Pos', 'Right-IntCapsule-Pos', 'Left-Cerebral-WM-unmyelinated', 'Right-Cerebral-WM-unmyelinated', 'Left-Cerebral-WM-myelinated', 'Right-Cerebral-WM-myelinated', 'Left-Subcortical-Gray-Matter', 'Right-Subcortical-Gray-Matter', 'Skull', 'Posterior-fossa', 'Scalp', 'Hematoma', 'Left-Basal-Ganglia', 'Right-Basal-Ganglia', 'brainstem', 'DCG', 'Vermis', 'Midbrain', 'Pons', 'Medulla', 'Left-Cortical-Dysplasia', 'Right-Cortical-Dysplasia', 'Left-hippocampal_fissure', 'Left-CADG-head', 'Left-subiculum', 'Left-fimbria', 'Right-hippocampal_fissure', 'Right-CADG-head', 'Right-subiculum', 'Right-fimbria', 'alveus', 'perforant_pathway', 'parasubiculum', 'presubiculum', 'subiculum', 'CA1', 'CA2', 'CA3', 'CA4', 'GC-ML-DG', 'HATA', 'fimbria', 'lateral_ventricle', 'molecular_layer_HP', 'hippocampal_fissure', 'entorhinal_cortex', 'molecular_layer_subiculum', 'Amygdala', 'Cerebral_White_Matter', 'Cerebral_Cortex', 'Inf_Lat_Vent', 'Perirhinal', 'Cerebral_White_Matter_Edge', 'Background', 'Ectorhinal', 'HP_tail', 'Fornix', 'CC_Posterior', 'CC_Mid_Posterior', 'CC_Central', 'CC_Mid_Anterior', 'CC_Anterior', 'Voxel-Unchanged', 'Aorta', 'Left-Common-IliacA', 'Right-Common-IliacA', 'Left-External-IliacA', 'Right-External-IliacA', 'Left-Internal-IliacA', 'Right-Internal-IliacA', 'Left-Lateral-SacralA', 'Right-Lateral-SacralA', 'Left-ObturatorA', 'Right-ObturatorA', 'Left-Internal-PudendalA', 'Right-Internal-PudendalA', 'Left-UmbilicalA', 'Right-UmbilicalA', 'Left-Inf-RectalA', 'Right-Inf-RectalA', 'Left-Common-IliacV', 'Right-Common-IliacV', 'Left-External-IliacV', 'Right-External-IliacV', 'Left-Internal-IliacV', 'Right-Internal-IliacV', 'Left-ObturatorV', 'Right-ObturatorV', 'Left-Internal-PudendalV', 'Right-Internal-PudendalV', 'Pos-Lymph', 'Neg-Lymph', 'V1', 'V2', 'BA44', 'BA45', 'BA4a', 'BA4p', 'BA6', 'BA2', 'BA1_old', 'BAun2', 'BA1', 'BA2b', 'BA3a', 'BA3b', 'MT', 'AIPS_AIP_l', 'AIPS_AIP_r', 'AIPS_VIP_l', 'AIPS_VIP_r', 'IPL_PFcm_l', 'IPL_PFcm_r', 'IPL_PF_l', 'IPL_PFm_l', 'IPL_PFm_r', 'IPL_PFop_l', 'IPL_PFop_r', 'IPL_PF_r', 'IPL_PFt_l', 'IPL_PFt_r', 'IPL_PGa_l', 'IPL_PGa_r', 'IPL_PGp_l', 'IPL_PGp_r', 'Visual_V3d_l', 'Visual_V3d_r', 'Visual_V4_l', 'Visual_V4_r', 'Visual_V5_b', 'Visual_VP_l', 'Visual_VP_r', 'wmsa', 'other_wmsa', 'right_CA2_3', 'right_alveus', 'right_CA1', 'right_fimbria', 'right_presubiculum', 'right_hippocampal_fissure', 'right_CA4_DG', 'right_subiculum', 'right_fornix', 'left_CA2_3', 'left_alveus', 'left_CA1', 'left_fimbria', 'left_presubiculum', 'left_hippocampal_fissure', 'left_CA4_DG', 'left_subiculum', 'left_fornix', 'Tumor', 'Cbm_Left_I_IV', 'Cbm_Right_I_IV', 'Cbm_Left_V', 'Cbm_Right_V', 'Cbm_Left_VI', 'Cbm_Vermis_VI', 'Cbm_Right_VI', 'Cbm_Left_CrusI', 'Cbm_Vermis_CrusI', 'Cbm_Right_CrusI', 'Cbm_Left_CrusII', 'Cbm_Vermis_CrusII', 'Cbm_Right_CrusII', 'Cbm_Left_VIIb', 'Cbm_Vermis_VIIb', 'Cbm_Right_VIIb', 'Cbm_Left_VIIIa', 'Cbm_Vermis_VIIIa', 'Cbm_Right_VIIIa', 'Cbm_Left_VIIIb', 'Cbm_Vermis_VIIIb', 'Cbm_Right_VIIIb', 'Cbm_Left_IX', 'Cbm_Vermis_IX', 'Cbm_Right_IX', 'Cbm_Left_X', 'Cbm_Vermis_X', 'Cbm_Right_X', 'Cbm_Right_I_V_med', 'Cbm_Right_I_V_mid', 'Cbm_Right_VI_med', 'Cbm_Right_VI_mid', 'Cbm_Right_VI_lat', 'Cbm_Right_CrusI_med', 'Cbm_Right_CrusI_mid', 'Cbm_Right_CrusI_lat', 'Cbm_Right_CrusII_med', 'Cbm_Right_CrusII_mid', 'Cbm_Right_CrusII_lat', 'Cbm_Right_7med', 'Cbm_Right_7mid', 'Cbm_Right_7lat', 'Cbm_Right_8med', 'Cbm_Right_8mid', 'Cbm_Right_8lat', 'Cbm_Right_PUNs', 'Cbm_Right_TONs', 'Cbm_Right_FLOs', 'Cbm_Left_I_V_med', 'Cbm_Left_I_V_mid', 'Cbm_Left_VI_med', 'Cbm_Left_VI_mid', 'Cbm_Left_VI_lat', 'Cbm_Left_CrusI_med', 'Cbm_Left_CrusI_mid', 'Cbm_Left_CrusI_lat', 'Cbm_Left_CrusII_med', 'Cbm_Left_CrusII_mid', 'Cbm_Left_CrusII_lat', 'Cbm_Left_7med', 'Cbm_Left_7mid', 'Cbm_Left_7lat', 'Cbm_Left_8med', 'Cbm_Left_8mid', 'Cbm_Left_8lat', 'Cbm_Left_PUNs', 'Cbm_Left_TONs', 'Cbm_Left_FLOs', 'CSF-FSL-FAST', 'GrayMatter-FSL-FAST', 'WhiteMatter-FSL-FAST', 'SUSPICIOUS', 'ctx-lh-unknown', 'ctx-lh-bankssts', 'ctx-lh-caudalanteriorcingulate', 'ctx-lh-caudalmiddlefrontal', 'ctx-lh-corpuscallosum', 'ctx-lh-cuneus', 'ctx-lh-entorhinal', 'ctx-lh-fusiform', 'ctx-lh-inferiorparietal', 'ctx-lh-inferiortemporal', 'ctx-lh-isthmuscingulate', 'ctx-lh-lateraloccipital', 'ctx-lh-lateralorbitofrontal', 'ctx-lh-lingual', 'ctx-lh-medialorbitofrontal', 'ctx-lh-middletemporal', 'ctx-lh-parahippocampal', 'ctx-lh-paracentral', 'ctx-lh-parsopercularis', 'ctx-lh-parsorbitalis', 'ctx-lh-parstriangularis', 'ctx-lh-pericalcarine', 'ctx-lh-postcentral', 'ctx-lh-posteriorcingulate', 'ctx-lh-precentral', 'ctx-lh-precuneus', 'ctx-lh-rostralanteriorcingulate', 'ctx-lh-rostralmiddlefrontal', 'ctx-lh-superiorfrontal', 'ctx-lh-superiorparietal', 'ctx-lh-superiortemporal', 'ctx-lh-supramarginal', 'ctx-lh-frontalpole', 'ctx-lh-temporalpole', 'ctx-lh-transversetemporal', 'ctx-lh-insula', 'ctx-rh-unknown', 'ctx-rh-bankssts', 'ctx-rh-caudalanteriorcingulate', 'ctx-rh-caudalmiddlefrontal', 'ctx-rh-corpuscallosum', 'ctx-rh-cuneus', 'ctx-rh-entorhinal', 'ctx-rh-fusiform', 'ctx-rh-inferiorparietal', 'ctx-rh-inferiortemporal', 'ctx-rh-isthmuscingulate', 'ctx-rh-lateraloccipital', 'ctx-rh-lateralorbitofrontal', 'ctx-rh-lingual', 'ctx-rh-medialorbitofrontal', 'ctx-rh-middletemporal', 'ctx-rh-parahippocampal', 'ctx-rh-paracentral', 'ctx-rh-parsopercularis', 'ctx-rh-parsorbitalis', 'ctx-rh-parstriangularis', 'ctx-rh-pericalcarine', 'ctx-rh-postcentral', 'ctx-rh-posteriorcingulate', 'ctx-rh-precentral', 'ctx-rh-precuneus', 'ctx-rh-rostralanteriorcingulate', 'ctx-rh-rostralmiddlefrontal', 'ctx-rh-superiorfrontal', 'ctx-rh-superiorparietal', 'ctx-rh-superiortemporal', 'ctx-rh-supramarginal', 'ctx-rh-frontalpole', 'ctx-rh-temporalpole', 'ctx-rh-transversetemporal', 'ctx-rh-insula', 'wm-lh-unknown', 'wm-lh-bankssts', 'wm-lh-caudalanteriorcingulate', 'wm-lh-caudalmiddlefrontal', 'wm-lh-corpuscallosum', 'wm-lh-cuneus', 'wm-lh-entorhinal', 'wm-lh-fusiform', 'wm-lh-inferiorparietal', 'wm-lh-inferiortemporal', 'wm-lh-isthmuscingulate', 'wm-lh-lateraloccipital', 'wm-lh-lateralorbitofrontal', 'wm-lh-lingual', 'wm-lh-medialorbitofrontal', 'wm-lh-middletemporal', 'wm-lh-parahippocampal', 'wm-lh-paracentral', 'wm-lh-parsopercularis', 'wm-lh-parsorbitalis', 'wm-lh-parstriangularis', 'wm-lh-pericalcarine', 'wm-lh-postcentral', 'wm-lh-posteriorcingulate', 'wm-lh-precentral', 'wm-lh-precuneus', 'wm-lh-rostralanteriorcingulate', 'wm-lh-rostralmiddlefrontal', 'wm-lh-superiorfrontal', 'wm-lh-superiorparietal', 'wm-lh-superiortemporal', 'wm-lh-supramarginal', 'wm-lh-frontalpole', 'wm-lh-temporalpole', 'wm-lh-transversetemporal', 'wm-lh-insula', 'wm-rh-unknown', 'wm-rh-bankssts', 'wm-rh-caudalanteriorcingulate', 'wm-rh-caudalmiddlefrontal', 'wm-rh-corpuscallosum', 'wm-rh-cuneus', 'wm-rh-entorhinal', 'wm-rh-fusiform', 'wm-rh-inferiorparietal', 'wm-rh-inferiortemporal', 'wm-rh-isthmuscingulate', 'wm-rh-lateraloccipital', 'wm-rh-lateralorbitofrontal', 'wm-rh-lingual', 'wm-rh-medialorbitofrontal', 'wm-rh-middletemporal', 'wm-rh-parahippocampal', 'wm-rh-paracentral', 'wm-rh-parsopercularis', 'wm-rh-parsorbitalis', 'wm-rh-parstriangularis', 'wm-rh-pericalcarine', 'wm-rh-postcentral', 'wm-rh-posteriorcingulate', 'wm-rh-precentral', 'wm-rh-precuneus', 'wm-rh-rostralanteriorcingulate', 'wm-rh-rostralmiddlefrontal', 'wm-rh-superiorfrontal', 'wm-rh-superiorparietal', 'wm-rh-superiortemporal', 'wm-rh-supramarginal', 'wm-rh-frontalpole', 'wm-rh-temporalpole', 'wm-rh-transversetemporal', 'wm-rh-insula', 'ctx-lh-Unknown', 'ctx-lh-Corpus_callosum', 'ctx-lh-G_and_S_Insula_ONLY_AVERAGE', 'ctx-lh-G_cingulate-Isthmus', 'ctx-lh-G_cingulate-Main_part', 'ctx-lh-G_cingulate-caudal_ACC', 'ctx-lh-G_cingulate-rostral_ACC', 'ctx-lh-G_cingulate-posterior', 'ctx-lh-S_cingulate-caudal_ACC', 'ctx-lh-S_cingulate-rostral_ACC', 'ctx-lh-S_cingulate-posterior', 'ctx-lh-S_pericallosal-caudal', 'ctx-lh-S_pericallosal-rostral', 'ctx-lh-S_pericallosal-posterior', 'ctx-lh-G_cuneus', 'ctx-lh-G_frontal_inf-Opercular_part', 'ctx-lh-G_frontal_inf-Orbital_part', 'ctx-lh-G_frontal_inf-Triangular_part', 'ctx-lh-G_frontal_middle', 'ctx-lh-G_frontal_superior', 'ctx-lh-G_frontomarginal', 'ctx-lh-G_insular_long', 'ctx-lh-G_insular_short', 'ctx-lh-G_and_S_occipital_inferior', 'ctx-lh-G_occipital_middle', 'ctx-lh-G_occipital_superior', 'ctx-lh-G_occipit-temp_lat-Or_fusiform', 'ctx-lh-G_occipit-temp_med-Lingual_part', 'ctx-lh-G_occipit-temp_med-Parahippocampal_part', 'ctx-lh-G_orbital', 'ctx-lh-G_paracentral', 'ctx-lh-G_parietal_inferior-Angular_part', 'ctx-lh-G_parietal_inferior-Supramarginal_part', 'ctx-lh-G_parietal_superior', 'ctx-lh-G_postcentral', 'ctx-lh-G_precentral', 'ctx-lh-G_precuneus', 'ctx-lh-G_rectus', 'ctx-lh-G_subcallosal', 'ctx-lh-G_subcentral', 'ctx-lh-G_temporal_inferior', 'ctx-lh-G_temporal_middle', 'ctx-lh-G_temp_sup-G_temp_transv_and_interm_S', 'ctx-lh-G_temp_sup-Lateral_aspect', 'ctx-lh-G_temp_sup-Planum_polare', 'ctx-lh-G_temp_sup-Planum_tempolare', 'ctx-lh-G_and_S_transverse_frontopolar', 'ctx-lh-Lat_Fissure-ant_sgt-ramus_horizontal', 'ctx-lh-Lat_Fissure-ant_sgt-ramus_vertical', 'ctx-lh-Lat_Fissure-post_sgt', 'ctx-lh-Medial_wall', 'ctx-lh-Pole_occipital', 'ctx-lh-Pole_temporal', 'ctx-lh-S_calcarine', 'ctx-lh-S_central', 'ctx-lh-S_central_insula', 'ctx-lh-S_cingulate-Main_part_and_Intracingulate', 'ctx-lh-S_cingulate-Marginalis_part', 'ctx-lh-S_circular_insula_anterior', 'ctx-lh-S_circular_insula_inferior', 'ctx-lh-S_circular_insula_superior', 'ctx-lh-S_collateral_transverse_ant', 'ctx-lh-S_collateral_transverse_post', 'ctx-lh-S_frontal_inferior', 'ctx-lh-S_frontal_middle', 'ctx-lh-S_frontal_superior', 'ctx-lh-S_frontomarginal', 'ctx-lh-S_intermedius_primus-Jensen', 'ctx-lh-S_intraparietal-and_Parietal_transverse', 'ctx-lh-S_occipital_anterior', 'ctx-lh-S_occipital_middle_and_Lunatus', 'ctx-lh-S_occipital_superior_and_transversalis', 'ctx-lh-S_occipito-temporal_lateral', 'ctx-lh-S_occipito-temporal_medial_and_S_Lingual', 'ctx-lh-S_orbital-H_shapped', 'ctx-lh-S_orbital_lateral', 'ctx-lh-S_orbital_medial-Or_olfactory', 'ctx-lh-S_paracentral', 'ctx-lh-S_parieto_occipital', 'ctx-lh-S_pericallosal', 'ctx-lh-S_postcentral', 'ctx-lh-S_precentral-Inferior-part', 'ctx-lh-S_precentral-Superior-part', 'ctx-lh-S_subcentral_ant', 'ctx-lh-S_subcentral_post', 'ctx-lh-S_suborbital', 'ctx-lh-S_subparietal', 'ctx-lh-S_supracingulate', 'ctx-lh-S_temporal_inferior', 'ctx-lh-S_temporal_superior', 'ctx-lh-S_temporal_transverse', 'ctx-rh-Unknown', 'ctx-rh-Corpus_callosum', 'ctx-rh-G_and_S_Insula_ONLY_AVERAGE', 'ctx-rh-G_cingulate-Isthmus', 'ctx-rh-G_cingulate-Main_part', 'ctx-rh-G_cuneus', 'ctx-rh-G_frontal_inf-Opercular_part', 'ctx-rh-G_frontal_inf-Orbital_part', 'ctx-rh-G_frontal_inf-Triangular_part', 'ctx-rh-G_frontal_middle', 'ctx-rh-G_frontal_superior', 'ctx-rh-G_frontomarginal', 'ctx-rh-G_insular_long', 'ctx-rh-G_insular_short', 'ctx-rh-G_and_S_occipital_inferior', 'ctx-rh-G_occipital_middle', 'ctx-rh-G_occipital_superior', 'ctx-rh-G_occipit-temp_lat-Or_fusiform', 'ctx-rh-G_occipit-temp_med-Lingual_part', 'ctx-rh-G_occipit-temp_med-Parahippocampal_part', 'ctx-rh-G_orbital', 'ctx-rh-G_paracentral', 'ctx-rh-G_parietal_inferior-Angular_part', 'ctx-rh-G_parietal_inferior-Supramarginal_part', 'ctx-rh-G_parietal_superior', 'ctx-rh-G_postcentral', 'ctx-rh-G_precentral', 'ctx-rh-G_precuneus', 'ctx-rh-G_rectus', 'ctx-rh-G_subcallosal', 'ctx-rh-G_subcentral', 'ctx-rh-G_temporal_inferior', 'ctx-rh-G_temporal_middle', 'ctx-rh-G_temp_sup-G_temp_transv_and_interm_S', 'ctx-rh-G_temp_sup-Lateral_aspect', 'ctx-rh-G_temp_sup-Planum_polare', 'ctx-rh-G_temp_sup-Planum_tempolare', 'ctx-rh-G_and_S_transverse_frontopolar', 'ctx-rh-Lat_Fissure-ant_sgt-ramus_horizontal', 'ctx-rh-Lat_Fissure-ant_sgt-ramus_vertical', 'ctx-rh-Lat_Fissure-post_sgt', 'ctx-rh-Medial_wall', 'ctx-rh-Pole_occipital', 'ctx-rh-Pole_temporal', 'ctx-rh-S_calcarine', 'ctx-rh-S_central', 'ctx-rh-S_central_insula', 'ctx-rh-S_cingulate-Main_part_and_Intracingulate', 'ctx-rh-S_cingulate-Marginalis_part', 'ctx-rh-S_circular_insula_anterior', 'ctx-rh-S_circular_insula_inferior', 'ctx-rh-S_circular_insula_superior', 'ctx-rh-S_collateral_transverse_ant', 'ctx-rh-S_collateral_transverse_post', 'ctx-rh-S_frontal_inferior', 'ctx-rh-S_frontal_middle', 'ctx-rh-S_frontal_superior', 'ctx-rh-S_frontomarginal', 'ctx-rh-S_intermedius_primus-Jensen', 'ctx-rh-S_intraparietal-and_Parietal_transverse', 'ctx-rh-S_occipital_anterior', 'ctx-rh-S_occipital_middle_and_Lunatus', 'ctx-rh-S_occipital_superior_and_transversalis', 'ctx-rh-S_occipito-temporal_lateral', 'ctx-rh-S_occipito-temporal_medial_and_S_Lingual', 'ctx-rh-S_orbital-H_shapped', 'ctx-rh-S_orbital_lateral', 'ctx-rh-S_orbital_medial-Or_olfactory', 'ctx-rh-S_paracentral', 'ctx-rh-S_parieto_occipital', 'ctx-rh-S_pericallosal', 'ctx-rh-S_postcentral', 'ctx-rh-S_precentral-Inferior-part', 'ctx-rh-S_precentral-Superior-part', 'ctx-rh-S_subcentral_ant', 'ctx-rh-S_subcentral_post', 'ctx-rh-S_suborbital', 'ctx-rh-S_subparietal', 'ctx-rh-S_supracingulate', 'ctx-rh-S_temporal_inferior', 'ctx-rh-S_temporal_superior', 'ctx-rh-S_temporal_transverse', 'ctx-rh-G_cingulate-caudal_ACC', 'ctx-rh-G_cingulate-rostral_ACC', 'ctx-rh-G_cingulate-posterior', 'ctx-rh-S_cingulate-caudal_ACC', 'ctx-rh-S_cingulate-rostral_ACC', 'ctx-rh-S_cingulate-posterior', 'ctx-rh-S_pericallosal-caudal', 'ctx-rh-S_pericallosal-rostral', 'ctx-rh-S_pericallosal-posterior', 'wm-lh-Unknown', 'wm-lh-Corpus_callosum', 'wm-lh-G_and_S_Insula_ONLY_AVERAGE', 'wm-lh-G_cingulate-Isthmus', 'wm-lh-G_cingulate-Main_part', 'wm-lh-G_cuneus', 'wm-lh-G_frontal_inf-Opercular_part', 'wm-lh-G_frontal_inf-Orbital_part', 'wm-lh-G_frontal_inf-Triangular_part', 'wm-lh-G_frontal_middle', 'wm-lh-G_frontal_superior', 'wm-lh-G_frontomarginal', 'wm-lh-G_insular_long', 'wm-lh-G_insular_short', 'wm-lh-G_and_S_occipital_inferior', 'wm-lh-G_occipital_middle', 'wm-lh-G_occipital_superior', 'wm-lh-G_occipit-temp_lat-Or_fusiform', 'wm-lh-G_occipit-temp_med-Lingual_part', 'wm-lh-G_occipit-temp_med-Parahippocampal_part', 'wm-lh-G_orbital', 'wm-lh-G_paracentral', 'wm-lh-G_parietal_inferior-Angular_part', 'wm-lh-G_parietal_inferior-Supramarginal_part', 'wm-lh-G_parietal_superior', 'wm-lh-G_postcentral', 'wm-lh-G_precentral', 'wm-lh-G_precuneus', 'wm-lh-G_rectus', 'wm-lh-G_subcallosal', 'wm-lh-G_subcentral', 'wm-lh-G_temporal_inferior', 'wm-lh-G_temporal_middle', 'wm-lh-G_temp_sup-G_temp_transv_and_interm_S', 'wm-lh-G_temp_sup-Lateral_aspect', 'wm-lh-G_temp_sup-Planum_polare', 'wm-lh-G_temp_sup-Planum_tempolare', 'wm-lh-G_and_S_transverse_frontopolar', 'wm-lh-Lat_Fissure-ant_sgt-ramus_horizontal', 'wm-lh-Lat_Fissure-ant_sgt-ramus_vertical', 'wm-lh-Lat_Fissure-post_sgt', 'wm-lh-Medial_wall', 'wm-lh-Pole_occipital', 'wm-lh-Pole_temporal', 'wm-lh-S_calcarine', 'wm-lh-S_central', 'wm-lh-S_central_insula', 'wm-lh-S_cingulate-Main_part_and_Intracingulate', 'wm-lh-S_cingulate-Marginalis_part', 'wm-lh-S_circular_insula_anterior', 'wm-lh-S_circular_insula_inferior', 'wm-lh-S_circular_insula_superior', 'wm-lh-S_collateral_transverse_ant', 'wm-lh-S_collateral_transverse_post', 'wm-lh-S_frontal_inferior', 'wm-lh-S_frontal_middle', 'wm-lh-S_frontal_superior', 'wm-lh-S_frontomarginal', 'wm-lh-S_intermedius_primus-Jensen', 'wm-lh-S_intraparietal-and_Parietal_transverse', 'wm-lh-S_occipital_anterior', 'wm-lh-S_occipital_middle_and_Lunatus', 'wm-lh-S_occipital_superior_and_transversalis', 'wm-lh-S_occipito-temporal_lateral', 'wm-lh-S_occipito-temporal_medial_and_S_Lingual', 'wm-lh-S_orbital-H_shapped', 'wm-lh-S_orbital_lateral', 'wm-lh-S_orbital_medial-Or_olfactory', 'wm-lh-S_paracentral', 'wm-lh-S_parieto_occipital', 'wm-lh-S_pericallosal', 'wm-lh-S_postcentral', 'wm-lh-S_precentral-Inferior-part', 'wm-lh-S_precentral-Superior-part', 'wm-lh-S_subcentral_ant', 'wm-lh-S_subcentral_post', 'wm-lh-S_suborbital', 'wm-lh-S_subparietal', 'wm-lh-S_supracingulate', 'wm-lh-S_temporal_inferior', 'wm-lh-S_temporal_superior', 'wm-lh-S_temporal_transverse', 'wm-rh-Unknown', 'wm-rh-Corpus_callosum', 'wm-rh-G_and_S_Insula_ONLY_AVERAGE', 'wm-rh-G_cingulate-Isthmus', 'wm-rh-G_cingulate-Main_part', 'wm-rh-G_cuneus', 'wm-rh-G_frontal_inf-Opercular_part', 'wm-rh-G_frontal_inf-Orbital_part', 'wm-rh-G_frontal_inf-Triangular_part', 'wm-rh-G_frontal_middle', 'wm-rh-G_frontal_superior', 'wm-rh-G_frontomarginal', 'wm-rh-G_insular_long', 'wm-rh-G_insular_short', 'wm-rh-G_and_S_occipital_inferior', 'wm-rh-G_occipital_middle', 'wm-rh-G_occipital_superior', 'wm-rh-G_occipit-temp_lat-Or_fusiform', 'wm-rh-G_occipit-temp_med-Lingual_part', 'wm-rh-G_occipit-temp_med-Parahippocampal_part', 'wm-rh-G_orbital', 'wm-rh-G_paracentral', 'wm-rh-G_parietal_inferior-Angular_part', 'wm-rh-G_parietal_inferior-Supramarginal_part', 'wm-rh-G_parietal_superior', 'wm-rh-G_postcentral', 'wm-rh-G_precentral', 'wm-rh-G_precuneus', 'wm-rh-G_rectus', 'wm-rh-G_subcallosal', 'wm-rh-G_subcentral', 'wm-rh-G_temporal_inferior', 'wm-rh-G_temporal_middle', 'wm-rh-G_temp_sup-G_temp_transv_and_interm_S', 'wm-rh-G_temp_sup-Lateral_aspect', 'wm-rh-G_temp_sup-Planum_polare', 'wm-rh-G_temp_sup-Planum_tempolare', 'wm-rh-G_and_S_transverse_frontopolar', 'wm-rh-Lat_Fissure-ant_sgt-ramus_horizontal', 'wm-rh-Lat_Fissure-ant_sgt-ramus_vertical', 'wm-rh-Lat_Fissure-post_sgt', 'wm-rh-Medial_wall', 'wm-rh-Pole_occipital', 'wm-rh-Pole_temporal', 'wm-rh-S_calcarine', 'wm-rh-S_central', 'wm-rh-S_central_insula', 'wm-rh-S_cingulate-Main_part_and_Intracingulate', 'wm-rh-S_cingulate-Marginalis_part', 'wm-rh-S_circular_insula_anterior', 'wm-rh-S_circular_insula_inferior', 'wm-rh-S_circular_insula_superior', 'wm-rh-S_collateral_transverse_ant', 'wm-rh-S_collateral_transverse_post', 'wm-rh-S_frontal_inferior', 'wm-rh-S_frontal_middle', 'wm-rh-S_frontal_superior', 'wm-rh-S_frontomarginal', 'wm-rh-S_intermedius_primus-Jensen', 'wm-rh-S_intraparietal-and_Parietal_transverse', 'wm-rh-S_occipital_anterior', 'wm-rh-S_occipital_middle_and_Lunatus', 'wm-rh-S_occipital_superior_and_transversalis', 'wm-rh-S_occipito-temporal_lateral', 'wm-rh-S_occipito-temporal_medial_and_S_Lingual', 'wm-rh-S_orbital-H_shapped', 'wm-rh-S_orbital_lateral', 'wm-rh-S_orbital_medial-Or_olfactory', 'wm-rh-S_paracentral', 'wm-rh-S_parieto_occipital', 'wm-rh-S_pericallosal', 'wm-rh-S_postcentral', 'wm-rh-S_precentral-Inferior-part', 'wm-rh-S_precentral-Superior-part', 'wm-rh-S_subcentral_ant', 'wm-rh-S_subcentral_post', 'wm-rh-S_suborbital', 'wm-rh-S_subparietal', 'wm-rh-S_supracingulate', 'wm-rh-S_temporal_inferior', 'wm-rh-S_temporal_superior', 'wm-rh-S_temporal_transverse', 'Left-UnsegmentedWhiteMatter', 'Right-UnsegmentedWhiteMatter', 'fmajor', 'fminor', 'lh.atr', 'lh.cab', 'lh.ccg', 'lh.cst', 'lh.ilf', 'lh.slfp', 'lh.slft', 'lh.unc', 'rh.atr', 'rh.cab', 'rh.ccg', 'rh.cst', 'rh.ilf', 'rh.slfp', 'rh.slft', 'rh.unc', 'CC-ForcepsMajor', 'CC-ForcepsMinor', 'LAntThalRadiation', 'LCingulumAngBundle', 'LCingulumCingGyrus', 'LCorticospinalTract', 'LInfLongFas', 'LSupLongFasParietal', 'LSupLongFasTemporal', 'LUncinateFas', 'RAntThalRadiation', 'RCingulumAngBundle', 'RCingulumCingGyrus', 'RCorticospinalTract', 'RInfLongFas', 'RSupLongFasParietal', 'RSupLongFasTemporal', 'RUncinateFas', 'CST-orig', 'CST-hammer', 'CST-CVS', 'CST-flirt', 'Left-SLF1', 'Right-SLF1', 'Left-SLF3', 'Right-SLF3', 'Left-CST', 'Right-CST', 'Left-SLF2', 'Right-SLF2', 'Lateral-nucleus', 'Basolateral-nucleus', 'Basal-nucleus', 'Centromedial-nucleus', 'Central-nucleus', 'Medial-nucleus', 'Cortical-nucleus', 'Accessory-Basal-nucleus', 'Corticoamygdaloid-transitio', 'Anterior-amygdaloid-area-AAA', 'Fusion-amygdala-HP-FAH', 'Hippocampal-amygdala-transition-HATA', 'Endopiriform-nucleus', 'Lateral-nucleus-olfactory-tract', 'Paralaminar-nucleus', 'Intercalated-nucleus', 'Prepiriform-cortex', 'Periamygdaloid-cortex', 'Envelope-Amygdala', 'Extranuclear-Amydala', 'Brainstem-inferior-colliculus', 'Brainstem-cochlear-nucleus', 'Thalamus-Anterior', 'Thalamus-Ventral-anterior', 'Thalamus-Lateral-dorsal', 'Thalamus-Lateral-posterior', 'Thalamus-Ventral-lateral', 'Thalamus-Ventral-posterior-medial', 'Thalamus-Ventral-posterior-lateral', 'Thalamus-intralaminar', 'Thalamus-centromedian', 'Thalamus-mediodorsal', 'Thalamus-medial', 'Thalamus-pulvinar', 'Thalamus-lateral-geniculate', 'Thalamus-medial-geniculate', 'ctx-lh-prefrontal', 'ctx-lh-primary-motor', 'ctx-lh-premotor', 'ctx-lh-temporal', 'ctx-lh-posterior-parietal', 'ctx-lh-prim-sec-somatosensory', 'ctx-lh-occipital', 'ctx-rh-prefrontal', 'ctx-rh-primary-motor', 'ctx-rh-premotor', 'ctx-rh-temporal', 'ctx-rh-posterior-parietal', 'ctx-rh-prim-sec-somatosensory', 'ctx-rh-occipital', 'ctx_lh_Unknown', 'ctx_lh_G_and_S_frontomargin', 'ctx_lh_G_and_S_occipital_inf', 'ctx_lh_G_and_S_paracentral', 'ctx_lh_G_and_S_subcentral', 'ctx_lh_G_and_S_transv_frontopol', 'ctx_lh_G_and_S_cingul-Ant', 'ctx_lh_G_and_S_cingul-Mid-Ant', 'ctx_lh_G_and_S_cingul-Mid-Post', 'ctx_lh_G_cingul-Post-dorsal', 'ctx_lh_G_cingul-Post-ventral', 'ctx_lh_G_cuneus', 'ctx_lh_G_front_inf-Opercular', 'ctx_lh_G_front_inf-Orbital', 'ctx_lh_G_front_inf-Triangul', 'ctx_lh_G_front_middle', 'ctx_lh_G_front_sup', 'ctx_lh_G_Ins_lg_and_S_cent_ins', 'ctx_lh_G_insular_short', 'ctx_lh_G_occipital_middle', 'ctx_lh_G_occipital_sup', 'ctx_lh_G_oc-temp_lat-fusifor', 'ctx_lh_G_oc-temp_med-Lingual', 'ctx_lh_G_oc-temp_med-Parahip', 'ctx_lh_G_orbital', 'ctx_lh_G_pariet_inf-Angular', 'ctx_lh_G_pariet_inf-Supramar', 'ctx_lh_G_parietal_sup', 'ctx_lh_G_postcentral', 'ctx_lh_G_precentral', 'ctx_lh_G_precuneus', 'ctx_lh_G_rectus', 'ctx_lh_G_subcallosal', 'ctx_lh_G_temp_sup-G_T_transv', 'ctx_lh_G_temp_sup-Lateral', 'ctx_lh_G_temp_sup-Plan_polar', 'ctx_lh_G_temp_sup-Plan_tempo', 'ctx_lh_G_temporal_inf', 'ctx_lh_G_temporal_middle', 'ctx_lh_Lat_Fis-ant-Horizont', 'ctx_lh_Lat_Fis-ant-Vertical', 'ctx_lh_Lat_Fis-post', 'ctx_lh_Medial_wall', 'ctx_lh_Pole_occipital', 'ctx_lh_Pole_temporal', 'ctx_lh_S_calcarine', 'ctx_lh_S_central', 'ctx_lh_S_cingul-Marginalis', 'ctx_lh_S_circular_insula_ant', 'ctx_lh_S_circular_insula_inf', 'ctx_lh_S_circular_insula_sup', 'ctx_lh_S_collat_transv_ant', 'ctx_lh_S_collat_transv_post', 'ctx_lh_S_front_inf', 'ctx_lh_S_front_middle', 'ctx_lh_S_front_sup', 'ctx_lh_S_interm_prim-Jensen', 'ctx_lh_S_intrapariet_and_P_trans', 'ctx_lh_S_oc_middle_and_Lunatus', 'ctx_lh_S_oc_sup_and_transversal', 'ctx_lh_S_occipital_ant', 'ctx_lh_S_oc-temp_lat', 'ctx_lh_S_oc-temp_med_and_Lingual', 'ctx_lh_S_orbital_lateral', 'ctx_lh_S_orbital_med-olfact', 'ctx_lh_S_orbital-H_Shaped', 'ctx_lh_S_parieto_occipital', 'ctx_lh_S_pericallosal', 'ctx_lh_S_postcentral', 'ctx_lh_S_precentral-inf-part', 'ctx_lh_S_precentral-sup-part', 'ctx_lh_S_suborbital', 'ctx_lh_S_subparietal', 'ctx_lh_S_temporal_inf', 'ctx_lh_S_temporal_sup', 'ctx_lh_S_temporal_transverse', 'ctx_rh_Unknown', 'ctx_rh_G_and_S_frontomargin', 'ctx_rh_G_and_S_occipital_inf', 'ctx_rh_G_and_S_paracentral', 'ctx_rh_G_and_S_subcentral', 'ctx_rh_G_and_S_transv_frontopol', 'ctx_rh_G_and_S_cingul-Ant', 'ctx_rh_G_and_S_cingul-Mid-Ant', 'ctx_rh_G_and_S_cingul-Mid-Post', 'ctx_rh_G_cingul-Post-dorsal', 'ctx_rh_G_cingul-Post-ventral', 'ctx_rh_G_cuneus', 'ctx_rh_G_front_inf-Opercular', 'ctx_rh_G_front_inf-Orbital', 'ctx_rh_G_front_inf-Triangul', 'ctx_rh_G_front_middle', 'ctx_rh_G_front_sup', 'ctx_rh_G_Ins_lg_and_S_cent_ins', 'ctx_rh_G_insular_short', 'ctx_rh_G_occipital_middle', 'ctx_rh_G_occipital_sup', 'ctx_rh_G_oc-temp_lat-fusifor', 'ctx_rh_G_oc-temp_med-Lingual', 'ctx_rh_G_oc-temp_med-Parahip', 'ctx_rh_G_orbital', 'ctx_rh_G_pariet_inf-Angular', 'ctx_rh_G_pariet_inf-Supramar', 'ctx_rh_G_parietal_sup', 'ctx_rh_G_postcentral', 'ctx_rh_G_precentral', 'ctx_rh_G_precuneus', 'ctx_rh_G_rectus', 'ctx_rh_G_subcallosal', 'ctx_rh_G_temp_sup-G_T_transv', 'ctx_rh_G_temp_sup-Lateral', 'ctx_rh_G_temp_sup-Plan_polar', 'ctx_rh_G_temp_sup-Plan_tempo', 'ctx_rh_G_temporal_inf', 'ctx_rh_G_temporal_middle', 'ctx_rh_Lat_Fis-ant-Horizont', 'ctx_rh_Lat_Fis-ant-Vertical', 'ctx_rh_Lat_Fis-post', 'ctx_rh_Medial_wall', 'ctx_rh_Pole_occipital', 'ctx_rh_Pole_temporal', 'ctx_rh_S_calcarine', 'ctx_rh_S_central', 'ctx_rh_S_cingul-Marginalis', 'ctx_rh_S_circular_insula_ant', 'ctx_rh_S_circular_insula_inf', 'ctx_rh_S_circular_insula_sup', 'ctx_rh_S_collat_transv_ant', 'ctx_rh_S_collat_transv_post', 'ctx_rh_S_front_inf', 'ctx_rh_S_front_middle', 'ctx_rh_S_front_sup', 'ctx_rh_S_interm_prim-Jensen', 'ctx_rh_S_intrapariet_and_P_trans', 'ctx_rh_S_oc_middle_and_Lunatus', 'ctx_rh_S_oc_sup_and_transversal', 'ctx_rh_S_occipital_ant', 'ctx_rh_S_oc-temp_lat', 'ctx_rh_S_oc-temp_med_and_Lingual', 'ctx_rh_S_orbital_lateral', 'ctx_rh_S_orbital_med-olfact', 'ctx_rh_S_orbital-H_Shaped', 'ctx_rh_S_parieto_occipital', 'ctx_rh_S_pericallosal', 'ctx_rh_S_postcentral', 'ctx_rh_S_precentral-inf-part', 'ctx_rh_S_precentral-sup-part', 'ctx_rh_S_suborbital', 'ctx_rh_S_subparietal', 'ctx_rh_S_temporal_inf', 'ctx_rh_S_temporal_sup', 'ctx_rh_S_temporal_transverse', 'wm_lh_Unknown', 'wm_lh_G_and_S_frontomargin', 'wm_lh_G_and_S_occipital_inf', 'wm_lh_G_and_S_paracentral', 'wm_lh_G_and_S_subcentral', 'wm_lh_G_and_S_transv_frontopol', 'wm_lh_G_and_S_cingul-Ant', 'wm_lh_G_and_S_cingul-Mid-Ant', 'wm_lh_G_and_S_cingul-Mid-Post', 'wm_lh_G_cingul-Post-dorsal', 'wm_lh_G_cingul-Post-ventral', 'wm_lh_G_cuneus', 'wm_lh_G_front_inf-Opercular', 'wm_lh_G_front_inf-Orbital', 'wm_lh_G_front_inf-Triangul', 'wm_lh_G_front_middle', 'wm_lh_G_front_sup', 'wm_lh_G_Ins_lg_and_S_cent_ins', 'wm_lh_G_insular_short', 'wm_lh_G_occipital_middle', 'wm_lh_G_occipital_sup', 'wm_lh_G_oc-temp_lat-fusifor', 'wm_lh_G_oc-temp_med-Lingual', 'wm_lh_G_oc-temp_med-Parahip', 'wm_lh_G_orbital', 'wm_lh_G_pariet_inf-Angular', 'wm_lh_G_pariet_inf-Supramar', 'wm_lh_G_parietal_sup', 'wm_lh_G_postcentral', 'wm_lh_G_precentral', 'wm_lh_G_precuneus', 'wm_lh_G_rectus', 'wm_lh_G_subcallosal', 'wm_lh_G_temp_sup-G_T_transv', 'wm_lh_G_temp_sup-Lateral', 'wm_lh_G_temp_sup-Plan_polar', 'wm_lh_G_temp_sup-Plan_tempo', 'wm_lh_G_temporal_inf', 'wm_lh_G_temporal_middle', 'wm_lh_Lat_Fis-ant-Horizont', 'wm_lh_Lat_Fis-ant-Vertical', 'wm_lh_Lat_Fis-post', 'wm_lh_Medial_wall', 'wm_lh_Pole_occipital', 'wm_lh_Pole_temporal', 'wm_lh_S_calcarine', 'wm_lh_S_central', 'wm_lh_S_cingul-Marginalis', 'wm_lh_S_circular_insula_ant', 'wm_lh_S_circular_insula_inf', 'wm_lh_S_circular_insula_sup', 'wm_lh_S_collat_transv_ant', 'wm_lh_S_collat_transv_post', 'wm_lh_S_front_inf', 'wm_lh_S_front_middle', 'wm_lh_S_front_sup', 'wm_lh_S_interm_prim-Jensen', 'wm_lh_S_intrapariet_and_P_trans', 'wm_lh_S_oc_middle_and_Lunatus', 'wm_lh_S_oc_sup_and_transversal', 'wm_lh_S_occipital_ant', 'wm_lh_S_oc-temp_lat', 'wm_lh_S_oc-temp_med_and_Lingual', 'wm_lh_S_orbital_lateral', 'wm_lh_S_orbital_med-olfact', 'wm_lh_S_orbital-H_Shaped', 'wm_lh_S_parieto_occipital', 'wm_lh_S_pericallosal', 'wm_lh_S_postcentral', 'wm_lh_S_precentral-inf-part', 'wm_lh_S_precentral-sup-part', 'wm_lh_S_suborbital', 'wm_lh_S_subparietal', 'wm_lh_S_temporal_inf', 'wm_lh_S_temporal_sup', 'wm_lh_S_temporal_transverse', 'wm_rh_Unknown', 'wm_rh_G_and_S_frontomargin', 'wm_rh_G_and_S_occipital_inf', 'wm_rh_G_and_S_paracentral', 'wm_rh_G_and_S_subcentral', 'wm_rh_G_and_S_transv_frontopol', 'wm_rh_G_and_S_cingul-Ant', 'wm_rh_G_and_S_cingul-Mid-Ant', 'wm_rh_G_and_S_cingul-Mid-Post', 'wm_rh_G_cingul-Post-dorsal', 'wm_rh_G_cingul-Post-ventral', 'wm_rh_G_cuneus', 'wm_rh_G_front_inf-Opercular', 'wm_rh_G_front_inf-Orbital', 'wm_rh_G_front_inf-Triangul', 'wm_rh_G_front_middle', 'wm_rh_G_front_sup', 'wm_rh_G_Ins_lg_and_S_cent_ins', 'wm_rh_G_insular_short', 'wm_rh_G_occipital_middle', 'wm_rh_G_occipital_sup', 'wm_rh_G_oc-temp_lat-fusifor', 'wm_rh_G_oc-temp_med-Lingual', 'wm_rh_G_oc-temp_med-Parahip', 'wm_rh_G_orbital', 'wm_rh_G_pariet_inf-Angular', 'wm_rh_G_pariet_inf-Supramar', 'wm_rh_G_parietal_sup', 'wm_rh_G_postcentral', 'wm_rh_G_precentral', 'wm_rh_G_precuneus', 'wm_rh_G_rectus', 'wm_rh_G_subcallosal', 'wm_rh_G_temp_sup-G_T_transv', 'wm_rh_G_temp_sup-Lateral', 'wm_rh_G_temp_sup-Plan_polar', 'wm_rh_G_temp_sup-Plan_tempo', 'wm_rh_G_temporal_inf', 'wm_rh_G_temporal_middle', 'wm_rh_Lat_Fis-ant-Horizont', 'wm_rh_Lat_Fis-ant-Vertical', 'wm_rh_Lat_Fis-post', 'wm_rh_Medial_wall', 'wm_rh_Pole_occipital', 'wm_rh_Pole_temporal', 'wm_rh_S_calcarine', 'wm_rh_S_central', 'wm_rh_S_cingul-Marginalis', 'wm_rh_S_circular_insula_ant', 'wm_rh_S_circular_insula_inf', 'wm_rh_S_circular_insula_sup', 'wm_rh_S_collat_transv_ant', 'wm_rh_S_collat_transv_post', 'wm_rh_S_front_inf', 'wm_rh_S_front_middle', 'wm_rh_S_front_sup', 'wm_rh_S_interm_prim-Jensen', 'wm_rh_S_intrapariet_and_P_trans', 'wm_rh_S_oc_middle_and_Lunatus', 'wm_rh_S_oc_sup_and_transversal', 'wm_rh_S_occipital_ant', 'wm_rh_S_oc-temp_lat', 'wm_rh_S_oc-temp_med_and_Lingual', 'wm_rh_S_orbital_lateral', 'wm_rh_S_orbital_med-olfact', 'wm_rh_S_orbital-H_Shaped', 'wm_rh_S_parieto_occipital', 'wm_rh_S_pericallosal', 'wm_rh_S_postcentral', 'wm_rh_S_precentral-inf-part', 'wm_rh_S_precentral-sup-part', 'wm_rh_S_suborbital', 'wm_rh_S_subparietal', 'wm_rh_S_temporal_inf', 'wm_rh_S_temporal_sup', 'wm_rh_S_temporal_transverse']¶

numbers = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 77, 78, 79, 80, 81, 82, 83, 84, 85, 192, 86, 87, 88, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 176, 170, 171, 172, 173, 174, 175, 180, 181, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 250, 251, 252, 253, 254, 255, 256, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 550, 551, 552, 553, 554, 555, 556, 557, 558, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 701, 702, 703, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033, 4034, 4035, 1100, 1101, 1102, 1103, 1104, 1200, 1201, 1202, 1205, 1206, 1207, 1210, 1211, 1212, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2119, 2120, 2121, 2122, 2123, 2124, 2125, 2126, 2127, 2128, 2129, 2130, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2138, 2139, 2140, 2141, 2142, 2143, 2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2166, 2167, 2168, 2169, 2170, 2171, 2172, 2173, 2174, 2175, 2176, 2177, 2178, 2179, 2180, 2181, 2200, 2201, 2202, 2205, 2206, 2207, 2210, 2211, 2212, 3100, 3101, 3102, 3103, 3104, 3105, 3106, 3107, 3108, 3109, 3110, 3111, 3112, 3113, 3114, 3115, 3116, 3117, 3118, 3119, 3120, 3121, 3122, 3123, 3124, 3125, 3126, 3127, 3128, 3129, 3130, 3131, 3132, 3133, 3134, 3135, 3136, 3137, 3138, 3139, 3140, 3141, 3142, 3143, 3144, 3145, 3146, 3147, 3148, 3149, 3150, 3151, 3152, 3153, 3154, 3155, 3156, 3157, 3158, 3159, 3160, 3161, 3162, 3163, 3164, 3165, 3166, 3167, 3168, 3169, 3170, 3171, 3172, 3173, 3174, 3175, 3176, 3177, 3178, 3179, 3180, 3181, 4100, 4101, 4102, 4103, 4104, 4105, 4106, 4107, 4108, 4109, 4110, 4111, 4112, 4113, 4114, 4115, 4116, 4117, 4118, 4119, 4120, 4121, 4122, 4123, 4124, 4125, 4126, 4127, 4128, 4129, 4130, 4131, 4132, 4133, 4134, 4135, 4136, 4137, 4138, 4139, 4140, 4141, 4142, 4143, 4144, 4145, 4146, 4147, 4148, 4149, 4150, 4151, 4152, 4153, 4154, 4155, 4156, 4157, 4158, 4159, 4160, 4161, 4162, 4163, 4164, 4165, 4166, 4167, 4168, 4169, 4170, 4171, 4172, 4173, 4174, 4175, 4176, 4177, 4178, 4179, 4180, 4181, 5001, 5002, 5100, 5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5200, 5201, 5202, 5203, 5204, 5205, 5206, 5207, 5208, 5209, 5210, 5211, 5212, 5213, 5214, 5215, 5216, 5217, 6000, 6001, 6002, 6003, 6010, 6020, 6030, 6040, 6050, 6060, 6070, 6080, 7001, 7002, 7003, 7004, 7005, 7006, 7007, 7008, 7009, 7010, 7011, 7012, 7013, 7014, 7015, 7016, 7017, 7018, 7019, 7020, 7100, 7101, 8001, 8002, 8003, 8004, 8005, 8006, 8007, 8008, 8009, 8010, 8011, 8012, 8013, 8014, 9000, 9001, 9002, 9003, 9004, 9005, 9006, 9500, 9501, 9502, 9503, 9504, 9505, 9506, 11100, 11101, 11102, 11103, 11104, 11105, 11106, 11107, 11108, 11109, 11110, 11111, 11112, 11113, 11114, 11115, 11116, 11117, 11118, 11119, 11120, 11121, 11122, 11123, 11124, 11125, 11126, 11127, 11128, 11129, 11130, 11131, 11132, 11133, 11134, 11135, 11136, 11137, 11138, 11139, 11140, 11141, 11142, 11143, 11144, 11145, 11146, 11147, 11148, 11149, 11150, 11151, 11152, 11153, 11154, 11155, 11156, 11157, 11158, 11159, 11160, 11161, 11162, 11163, 11164, 11165, 11166, 11167, 11168, 11169, 11170, 11171, 11172, 11173, 11174, 11175, 12100, 12101, 12102, 12103, 12104, 12105, 12106, 12107, 12108, 12109, 12110, 12111, 12112, 12113, 12114, 12115, 12116, 12117, 12118, 12119, 12120, 12121, 12122, 12123, 12124, 12125, 12126, 12127, 12128, 12129, 12130, 12131, 12132, 12133, 12134, 12135, 12136, 12137, 12138, 12139, 12140, 12141, 12142, 12143, 12144, 12145, 12146, 12147, 12148, 12149, 12150, 12151, 12152, 12153, 12154, 12155, 12156, 12157, 12158, 12159, 12160, 12161, 12162, 12163, 12164, 12165, 12166, 12167, 12168, 12169, 12170, 12171, 12172, 12173, 12174, 12175, 13100, 13101, 13102, 13103, 13104, 13105, 13106, 13107, 13108, 13109, 13110, 13111, 13112, 13113, 13114, 13115, 13116, 13117, 13118, 13119, 13120, 13121, 13122, 13123, 13124, 13125, 13126, 13127, 13128, 13129, 13130, 13131, 13132, 13133, 13134, 13135, 13136, 13137, 13138, 13139, 13140, 13141, 13142, 13143, 13144, 13145, 13146, 13147, 13148, 13149, 13150, 13151, 13152, 13153, 13154, 13155, 13156, 13157, 13158, 13159, 13160, 13161, 13162, 13163, 13164, 13165, 13166, 13167, 13168, 13169, 13170, 13171, 13172, 13173, 13174, 13175, 14100, 14101, 14102, 14103, 14104, 14105, 14106, 14107, 14108, 14109, 14110, 14111, 14112, 14113, 14114, 14115, 14116, 14117, 14118, 14119, 14120, 14121, 14122, 14123, 14124, 14125, 14126, 14127, 14128, 14129, 14130, 14131, 14132, 14133, 14134, 14135, 14136, 14137, 14138, 14139, 14140, 14141, 14142, 14143, 14144, 14145, 14146, 14147, 14148, 14149, 14150, 14151, 14152, 14153, 14154, 14155, 14156, 14157, 14158, 14159, 14160, 14161, 14162, 14163, 14164, 14165, 14166, 14167, 14168, 14169, 14170, 14171, 14172, 14173, 14174, 14175]¶

pair = [7, 13]¶

pair_list = [[6, 7], [7, 16], [7, 13]]¶

pair_lists = [[[12, 28]], [[3, 28], [27, 28]], [[3, 18], [3, 19], [3, 20], [18, 27], [19, 27], [20, 27]], [[24, 28], [3, 24], [24, 27], [18, 24], [19, 24], [20, 24]], [[22, 24]], [[22, 29], [22, 31]], [[29, 31], [8, 29]], [[8, 31]], [[30, 31]], [[8, 11], [11, 29]], [[11, 15], [9, 11]], [[15, 30]], [[9, 15]], [[12, 35], [30, 35], [34, 35], [2, 35], [10, 35], [23, 35], [26, 35], [22, 35], [24, 35], [31, 35]], [[30, 34]], [[2, 14], [10, 14], [14, 23], [14, 26], [2, 28], [10, 28], [23, 28], [26, 28], [2, 17], [10, 17], [17, 23], [17, 26], [17, 25]], [[17, 28]], [[5, 25]], [[13, 25], [2, 13], [10, 13], [13, 23], [13, 26]], [[14, 28]], [[3, 12], [12, 27], [12, 18], [12, 19], [12, 20]], [[12, 14]], [[7, 9], [7, 11]], [[6, 7], [7, 16], [7, 13]]]¶

relabel = True¶

return_numbers_names_colors()¶

Return lists of numbers, names, and colors representing anatomical brain regions derived from FreeSurfer’s FreeSurferColorLUT.txt lookup file.

Returns:	numbers (list of integers) – numbers representing anatomical labels from FreeSurferColorLUT.txt names (list of integers) – names for anatomical regions from FreeSurferColorLUT.txt colors (list of integers) – colors associated with anatomical labels from FreeSurferColorLUT.txt

Examples

>>> import numpy as np
>>> from mindboggle.mio.labels import return_numbers_names_colors
>>> numbers, names, colors = return_numbers_names_colors()
>>> len(np.unique(numbers))
1266
>>> names[0:3]
['Unknown', 'Left-Cerebral-Exterior', 'Left-Cerebral-White-Matter']
>>> colors[0:3]
[[0, 0, 0], [70, 130, 180], [245, 245, 245]]

right_cerebellum_cortex_colors = [[0, 148, 0], [230, 148, 34]]¶

right_cerebellum_cortex_list = [45, 47]¶

right_cerebellum_cortex_names = ['Right-Cerebellum-Exterior', 'Right-Cerebellum-Cortex']¶

right_cerebellum_cortex_numbers = [45, 47]¶

right_cerebellum_names = ['Right-Cerebellum-Exterior', 'Right-Cerebellum-Cortex', 'Right-Cerebellum-White-Matter']¶

right_cerebellum_noncortex_colors = [[220, 248, 164]]¶

right_cerebellum_noncortex_list = [46]¶

right_cerebellum_noncortex_names = ['Right-Cerebellum-White-Matter']¶

right_cerebellum_noncortex_numbers = [46]¶

right_cerebellum_numbers = [45, 47, 46]¶

right_cerebrum_colors = [[205, 62, 78], [80, 196, 98], [60, 58, 210], [25, 5, 25], [25, 100, 40], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [100, 0, 100], [70, 70, 70], [150, 150, 200], [255, 192, 32], [0, 225, 0], [120, 18, 134], [196, 58, 250], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [13, 48, 255], [220, 216, 20], [103, 255, 255], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [160, 32, 240], [0, 200, 221], [255, 148, 10], [205, 10, 125], [124, 140, 178], [125, 140, 178], [126, 140, 178], [127, 140, 178], [124, 141, 178], [124, 142, 178], [124, 143, 178], [124, 144, 178], [124, 140, 179], [238, 59, 59], [62, 10, 205], [17, 85, 136], [119, 187, 102], [204, 68, 34], [204, 0, 255], [221, 187, 17], [153, 221, 238], [51, 17, 17], [0, 119, 85], [20, 100, 200], [120, 70, 50], [230, 250, 230], [230, 155, 215], [130, 155, 95], [155, 230, 255], [135, 185, 205], [35, 235, 155], [35, 235, 245], [75, 35, 115], [35, 195, 35], [75, 215, 135], [115, 235, 115], [235, 225, 115], [220, 180, 205], [30, 115, 115], [55, 220, 180], [95, 155, 205], [235, 35, 195], [195, 35, 195], [35, 75, 115], [235, 155, 205], [35, 195, 235], [135, 155, 195], [35, 235, 235], [35, 75, 35], [195, 235, 35], [95, 115, 75], [175, 235, 115], [180, 205, 130], [235, 35, 95], [235, 75, 115], [115, 35, 35], [175, 95, 235], [155, 255, 155], [185, 185, 185], [105, 105, 55], [254, 191, 31], [20, 30, 40]]¶

right_cerebrum_cortex_DKT31_colors = [[125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [150, 150, 200], [255, 192, 32]]¶

right_cerebrum_cortex_DKT31_list = [2002, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2034, 2035]¶

right_cerebrum_cortex_DKT31_names = ['ctx-rh-caudalanteriorcingulate', 'ctx-rh-caudalmiddlefrontal', 'ctx-rh-cuneus', 'ctx-rh-entorhinal', 'ctx-rh-fusiform', 'ctx-rh-inferiorparietal', 'ctx-rh-inferiortemporal', 'ctx-rh-isthmuscingulate', 'ctx-rh-lateraloccipital', 'ctx-rh-lateralorbitofrontal', 'ctx-rh-lingual', 'ctx-rh-medialorbitofrontal', 'ctx-rh-middletemporal', 'ctx-rh-parahippocampal', 'ctx-rh-paracentral', 'ctx-rh-parsopercularis', 'ctx-rh-parsorbitalis', 'ctx-rh-parstriangularis', 'ctx-rh-pericalcarine', 'ctx-rh-postcentral', 'ctx-rh-posteriorcingulate', 'ctx-rh-precentral', 'ctx-rh-precuneus', 'ctx-rh-rostralanteriorcingulate', 'ctx-rh-rostralmiddlefrontal', 'ctx-rh-superiorfrontal', 'ctx-rh-superiorparietal', 'ctx-rh-superiortemporal', 'ctx-rh-supramarginal', 'ctx-rh-transversetemporal', 'ctx-rh-insula']¶

right_cerebrum_cortex_DKT31_numbers = [2002, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2034, 2035]¶

right_cerebrum_cortex_colors = [[205, 62, 78], [80, 196, 98], [60, 58, 210], [25, 5, 25], [25, 100, 40], [125, 100, 160], [100, 25, 0], [220, 20, 100], [220, 20, 10], [180, 220, 140], [220, 60, 220], [180, 40, 120], [140, 20, 140], [20, 30, 140], [35, 75, 50], [225, 140, 140], [200, 35, 75], [160, 100, 50], [20, 220, 60], [60, 220, 60], [220, 180, 140], [20, 100, 50], [220, 60, 20], [120, 100, 60], [220, 20, 20], [220, 180, 220], [60, 20, 220], [160, 140, 180], [80, 20, 140], [75, 50, 125], [20, 220, 160], [20, 180, 140], [140, 220, 220], [80, 160, 20], [100, 0, 100], [70, 70, 70], [150, 150, 200], [255, 192, 32]]¶

right_cerebrum_cortex_list = [2002, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2034, 2035, 42, 55, 56, 2000, 2001, 2032, 2033]¶

right_cerebrum_cortex_names = ['Right-Cerebral-Cortex', 'Right-Insula', 'Right-Operculum', 'ctx-rh-unknown', 'ctx-rh-bankssts', 'ctx-rh-caudalanteriorcingulate', 'ctx-rh-caudalmiddlefrontal', 'ctx-rh-cuneus', 'ctx-rh-entorhinal', 'ctx-rh-fusiform', 'ctx-rh-inferiorparietal', 'ctx-rh-inferiortemporal', 'ctx-rh-isthmuscingulate', 'ctx-rh-lateraloccipital', 'ctx-rh-lateralorbitofrontal', 'ctx-rh-lingual', 'ctx-rh-medialorbitofrontal', 'ctx-rh-middletemporal', 'ctx-rh-parahippocampal', 'ctx-rh-paracentral', 'ctx-rh-parsopercularis', 'ctx-rh-parsorbitalis', 'ctx-rh-parstriangularis', 'ctx-rh-pericalcarine', 'ctx-rh-postcentral', 'ctx-rh-posteriorcingulate', 'ctx-rh-precentral', 'ctx-rh-precuneus', 'ctx-rh-rostralanteriorcingulate', 'ctx-rh-rostralmiddlefrontal', 'ctx-rh-superiorfrontal', 'ctx-rh-superiorparietal', 'ctx-rh-superiortemporal', 'ctx-rh-supramarginal', 'ctx-rh-frontalpole', 'ctx-rh-temporalpole', 'ctx-rh-transversetemporal', 'ctx-rh-insula']¶

right_cerebrum_cortex_numbers = [42, 55, 56, 2000, 2001, 2002, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035]¶

right_cerebrum_names = ['Right-Cerebral-Cortex', 'Right-Insula', 'Right-Operculum', 'ctx-rh-unknown', 'ctx-rh-bankssts', 'ctx-rh-caudalanteriorcingulate', 'ctx-rh-caudalmiddlefrontal', 'ctx-rh-cuneus', 'ctx-rh-entorhinal', 'ctx-rh-fusiform', 'ctx-rh-inferiorparietal', 'ctx-rh-inferiortemporal', 'ctx-rh-isthmuscingulate', 'ctx-rh-lateraloccipital', 'ctx-rh-lateralorbitofrontal', 'ctx-rh-lingual', 'ctx-rh-medialorbitofrontal', 'ctx-rh-middletemporal', 'ctx-rh-parahippocampal', 'ctx-rh-paracentral', 'ctx-rh-parsopercularis', 'ctx-rh-parsorbitalis', 'ctx-rh-parstriangularis', 'ctx-rh-pericalcarine', 'ctx-rh-postcentral', 'ctx-rh-posteriorcingulate', 'ctx-rh-precentral', 'ctx-rh-precuneus', 'ctx-rh-rostralanteriorcingulate', 'ctx-rh-rostralmiddlefrontal', 'ctx-rh-superiorfrontal', 'ctx-rh-superiorparietal', 'ctx-rh-superiortemporal', 'ctx-rh-supramarginal', 'ctx-rh-frontalpole', 'ctx-rh-temporalpole', 'ctx-rh-transversetemporal', 'ctx-rh-insula', 'Right-Cerebral-White-Matter', 'Right-Lateral-Ventricle', 'Right-Inf-Lat-Vent', 'Right-Thalamus', 'Right-Thalamus-Proper', 'Right-Caudate', 'Right-Putamen', 'Right-Pallidum', 'Right-Hippocampus', 'Right-Amygdala', 'Right-Lesion', 'Right-Accumbens-area', 'Right-Substancia-Nigra', 'Right-VentralDC', 'Right-vessel', 'Right-choroid-plexus', 'Right-WM-hypointensities', 'Right-Amygdala-Anterior', 'Right-wm-intensity-abnormality', 'Right-caudate-intensity-abnormality', 'Right-putamen-intensity-abnormality', 'Right-accumbens-intensity-abnormality', 'Right-pallidum-intensity-abnormality', 'Right-amygdala-intensity-abnormality', 'Right-hippocampus-intensity-abnormality', 'Right-thalamus-intensity-abnormality', 'Right-VDC-intensity-abnormality', 'Right-IntCapsule-Ant', 'Right-IntCapsule-Pos', 'right_CA2_3', 'right_alveus', 'right_CA1', 'right_fimbria', 'right_presubiculum', 'right_hippocampal_fissure', 'right_CA4_DG', 'right_subiculum', 'right_fornix', 'ctx-rh-corpuscallosum', 'wm-rh-unknown', 'wm-rh-bankssts', 'wm-rh-caudalanteriorcingulate', 'wm-rh-caudalmiddlefrontal', 'wm-rh-corpuscallosum', 'wm-rh-cuneus', 'wm-rh-entorhinal', 'wm-rh-fusiform', 'wm-rh-inferiorparietal', 'wm-rh-inferiortemporal', 'wm-rh-isthmuscingulate', 'wm-rh-lateraloccipital', 'wm-rh-lateralorbitofrontal', 'wm-rh-lingual', 'wm-rh-medialorbitofrontal', 'wm-rh-middletemporal', 'wm-rh-parahippocampal', 'wm-rh-paracentral', 'wm-rh-parsopercularis', 'wm-rh-parsorbitalis', 'wm-rh-parstriangularis', 'wm-rh-pericalcarine', 'wm-rh-postcentral', 'wm-rh-posteriorcingulate', 'wm-rh-precentral', 'wm-rh-precuneus', 'wm-rh-rostralanteriorcingulate', 'wm-rh-rostralmiddlefrontal', 'wm-rh-superiorfrontal', 'wm-rh-superiorparietal', 'wm-rh-superiortemporal', 'wm-rh-supramarginal', 'wm-rh-frontalpole', 'wm-rh-temporalpole', 'wm-rh-transversetemporal', 'wm-rh-insula', 'Right-UnsegmentedWhiteMatter']¶

right_cerebrum_noncortex_colors = [[0, 225, 0], [120, 18, 134], [196, 58, 250], [0, 118, 14], [0, 118, 14], [122, 186, 220], [236, 13, 176], [13, 48, 255], [220, 216, 20], [103, 255, 255], [255, 165, 0], [255, 165, 0], [0, 255, 127], [165, 42, 42], [160, 32, 240], [0, 200, 221], [255, 148, 10], [205, 10, 125], [124, 140, 178], [125, 140, 178], [126, 140, 178], [127, 140, 178], [124, 141, 178], [124, 142, 178], [124, 143, 178], [124, 144, 178], [124, 140, 179], [238, 59, 59], [62, 10, 205], [17, 85, 136], [119, 187, 102], [204, 68, 34], [204, 0, 255], [221, 187, 17], [153, 221, 238], [51, 17, 17], [0, 119, 85], [20, 100, 200], [120, 70, 50], [230, 250, 230], [230, 155, 215], [130, 155, 95], [155, 230, 255], [135, 185, 205], [35, 235, 155], [35, 235, 245], [75, 35, 115], [35, 195, 35], [75, 215, 135], [115, 235, 115], [235, 225, 115], [220, 180, 205], [30, 115, 115], [55, 220, 180], [95, 155, 205], [235, 35, 195], [195, 35, 195], [35, 75, 115], [235, 155, 205], [35, 195, 235], [135, 155, 195], [35, 235, 235], [35, 75, 35], [195, 235, 35], [95, 115, 75], [175, 235, 115], [180, 205, 130], [235, 35, 95], [235, 75, 115], [115, 35, 35], [175, 95, 235], [155, 255, 155], [185, 185, 185], [105, 105, 55], [254, 191, 31], [20, 30, 40]]¶

right_cerebrum_noncortex_list = [41, 48, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 63, 79, 92, 97, 109, 110, 111, 112, 113, 114, 115, 116, 117, 156, 158, 500, 501, 502, 503, 504, 505, 506, 507, 508, 2004, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033, 4034, 4035, 5002, 43, 44]¶

right_cerebrum_noncortex_names = ['Right-Cerebral-White-Matter', 'Right-Lateral-Ventricle', 'Right-Inf-Lat-Vent', 'Right-Thalamus', 'Right-Thalamus-Proper', 'Right-Caudate', 'Right-Putamen', 'Right-Pallidum', 'Right-Hippocampus', 'Right-Amygdala', 'Right-Lesion', 'Right-Accumbens-area', 'Right-Substancia-Nigra', 'Right-VentralDC', 'Right-vessel', 'Right-choroid-plexus', 'Right-WM-hypointensities', 'Right-Amygdala-Anterior', 'Right-wm-intensity-abnormality', 'Right-caudate-intensity-abnormality', 'Right-putamen-intensity-abnormality', 'Right-accumbens-intensity-abnormality', 'Right-pallidum-intensity-abnormality', 'Right-amygdala-intensity-abnormality', 'Right-hippocampus-intensity-abnormality', 'Right-thalamus-intensity-abnormality', 'Right-VDC-intensity-abnormality', 'Right-IntCapsule-Ant', 'Right-IntCapsule-Pos', 'right_CA2_3', 'right_alveus', 'right_CA1', 'right_fimbria', 'right_presubiculum', 'right_hippocampal_fissure', 'right_CA4_DG', 'right_subiculum', 'right_fornix', 'ctx-rh-corpuscallosum', 'wm-rh-unknown', 'wm-rh-bankssts', 'wm-rh-caudalanteriorcingulate', 'wm-rh-caudalmiddlefrontal', 'wm-rh-corpuscallosum', 'wm-rh-cuneus', 'wm-rh-entorhinal', 'wm-rh-fusiform', 'wm-rh-inferiorparietal', 'wm-rh-inferiortemporal', 'wm-rh-isthmuscingulate', 'wm-rh-lateraloccipital', 'wm-rh-lateralorbitofrontal', 'wm-rh-lingual', 'wm-rh-medialorbitofrontal', 'wm-rh-middletemporal', 'wm-rh-parahippocampal', 'wm-rh-paracentral', 'wm-rh-parsopercularis', 'wm-rh-parsorbitalis', 'wm-rh-parstriangularis', 'wm-rh-pericalcarine', 'wm-rh-postcentral', 'wm-rh-posteriorcingulate', 'wm-rh-precentral', 'wm-rh-precuneus', 'wm-rh-rostralanteriorcingulate', 'wm-rh-rostralmiddlefrontal', 'wm-rh-superiorfrontal', 'wm-rh-superiorparietal', 'wm-rh-superiortemporal', 'wm-rh-supramarginal', 'wm-rh-frontalpole', 'wm-rh-temporalpole', 'wm-rh-transversetemporal', 'wm-rh-insula', 'Right-UnsegmentedWhiteMatter']¶

right_cerebrum_noncortex_numbers = [41, 43, 44, 48, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 63, 79, 97, 109, 110, 111, 112, 113, 114, 115, 116, 117, 156, 158, 500, 501, 502, 503, 504, 505, 506, 507, 508, 2004, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033, 4034, 4035, 5002]¶

right_cerebrum_numbers = [42, 55, 56, 2000, 2001, 2002, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 41, 43, 44, 48, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 63, 79, 97, 109, 110, 111, 112, 113, 114, 115, 116, 117, 156, 158, 500, 501, 502, 503, 504, 505, 506, 507, 508, 2004, 4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009, 4010, 4011, 4012, 4013, 4014, 4015, 4016, 4017, 4018, 4019, 4020, 4021, 4022, 4023, 4024, 4025, 4026, 4027, 4028, 4029, 4030, 4031, 4032, 4033, 4034, 4035, 5002]¶

right_pair = [2007, 2013]¶

right_pairs = [[2006, 2007], [2007, 2016], [2007, 2013]]¶

right_sulcus_label_pair_lists = [[[2012, 2028]], [[2003, 2028], [2027, 2028]], [[2003, 2018], [2003, 2019], [2003, 2020], [2018, 2027], [2019, 2027], [2020, 2027]], [[2024, 2028], [2003, 2024], [2024, 2027], [2018, 2024], [2019, 2024], [2020, 2024]], [[2022, 2024]], [[2022, 2029], [2022, 2031]], [[2029, 2031], [2008, 2029]], [[2008, 2031]], [[2030, 2031]], [[2008, 2011], [2011, 2029]], [[2011, 2015], [2009, 2011]], [[2015, 2030]], [[2009, 2015]], [[2012, 2035], [2030, 2035], [2034, 2035], [2002, 2035], [2010, 2035], [2023, 2035], [2026, 2035], [2022, 2035], [2024, 2035], [2031, 2035]], [[2030, 2034]], [[2002, 2014], [2010, 2014], [2014, 2023], [2014, 2026], [2002, 2028], [2010, 2028], [2023, 2028], [2026, 2028], [2002, 2017], [2010, 2017], [2017, 2023], [2017, 2026], [2017, 2025]], [[2017, 2028]], [[2005, 2025]], [[2013, 2025], [2002, 2013], [2010, 2013], [2013, 2023], [2013, 2026]], [[2014, 2028]], [[2003, 2012], [2012, 2027], [2012, 2018], [2012, 2019], [2012, 2020]], [[2012, 2014]], [[2007, 2009], [2007, 2011]], [[2006, 2007], [2007, 2016], [2007, 2013]]]¶

right_ventricle_colors = [[120, 18, 134], [196, 58, 250]]¶

right_ventricle_list = [43, 44]¶

right_ventricle_names = ['Right-Lateral-Ventricle', 'Right-Inf-Lat-Vent']¶

right_ventricle_numbers = [43, 44]¶

sulcus_label_pair_lists = [[[1012, 1028]], [[1003, 1028], [1027, 1028]], [[1003, 1018], [1003, 1019], [1003, 1020], [1018, 1027], [1019, 1027], [1020, 1027]], [[1024, 1028], [1003, 1024], [1024, 1027], [1018, 1024], [1019, 1024], [1020, 1024]], [[1022, 1024]], [[1022, 1029], [1022, 1031]], [[1029, 1031], [1008, 1029]], [[1008, 1031]], [[1030, 1031]], [[1008, 1011], [1011, 1029]], [[1011, 1015], [1009, 1011]], [[1015, 1030]], [[1009, 1015]], [[1012, 1035], [1030, 1035], [1034, 1035], [1002, 1035], [1010, 1035], [1023, 1035], [1026, 1035], [1022, 1035], [1024, 1035], [1031, 1035]], [[1030, 1034]], [[1002, 1014], [1010, 1014], [1014, 1023], [1014, 1026], [1002, 1028], [1010, 1028], [1023, 1028], [1026, 1028], [1002, 1017], [1010, 1017], [1017, 1023], [1017, 1026], [1017, 1025]], [[1017, 1028]], [[1005, 1025]], [[1013, 1025], [1002, 1013], [1010, 1013], [1013, 1023], [1013, 1026]], [[1014, 1028]], [[1003, 1012], [1012, 1027], [1012, 1018], [1012, 1019], [1012, 1020]], [[1012, 1014]], [[1007, 1009], [1007, 1011]], [[1006, 1007], [1007, 1016], [1007, 1013]], [[2012, 2028]], [[2003, 2028], [2027, 2028]], [[2003, 2018], [2003, 2019], [2003, 2020], [2018, 2027], [2019, 2027], [2020, 2027]], [[2024, 2028], [2003, 2024], [2024, 2027], [2018, 2024], [2019, 2024], [2020, 2024]], [[2022, 2024]], [[2022, 2029], [2022, 2031]], [[2029, 2031], [2008, 2029]], [[2008, 2031]], [[2030, 2031]], [[2008, 2011], [2011, 2029]], [[2011, 2015], [2009, 2011]], [[2015, 2030]], [[2009, 2015]], [[2012, 2035], [2030, 2035], [2034, 2035], [2002, 2035], [2010, 2035], [2023, 2035], [2026, 2035], [2022, 2035], [2024, 2035], [2031, 2035]], [[2030, 2034]], [[2002, 2014], [2010, 2014], [2014, 2023], [2014, 2026], [2002, 2028], [2010, 2028], [2023, 2028], [2026, 2028], [2002, 2017], [2010, 2017], [2017, 2023], [2017, 2026], [2017, 2025]], [[2017, 2028]], [[2005, 2025]], [[2013, 2025], [2002, 2013], [2010, 2013], [2013, 2023], [2013, 2026]], [[2014, 2028]], [[2003, 2012], [2012, 2027], [2012, 2018], [2012, 2019], [2012, 2020]], [[2012, 2014]], [[2007, 2009], [2007, 2011]], [[2006, 2007], [2007, 2016], [2007, 2013]]]¶

sulcus_names = ['frontomarginal sulcus', 'superior frontal sulcus', 'inferior frontal sulcus', 'precentral sulcus', 'central sulcus', 'postcentral sulcus', 'intraparietal sulcus', 'primary intermediate sulcus/1st segment of post. sup. temporal sulcus', 'sylvian fissure', 'lateral occipital sulcus', 'anterior occipital sulcus', 'superior temporal sulcus', 'inferior temporal sulcus', 'circular sulcus', '1st transverse temporal sulcus and Heschl sulcus', 'cingulate sulcus', 'paracentral sulcus', 'parietooccipital fissure', 'calcarine fissure', 'superior rostral sulcus', 'lateral H-shaped orbital sulcus', 'olfactory sulcus', 'occipitotemporal sulcus', 'collateral sulcus']¶

sulcus_names_abbr = ['fms', 'sfrs', 'ifrs', 'prcs', 'cs', 'pocs', 'itps', 'pis/csts1', 'ls', 'locs', 'aocs', 'sts', 'its', 'crs', 'ftts/hs', 'cgs', 'pcs', 'pos', 'ccs', 'sros', 'lhos', 'olfs', 'ots', 'cos']¶

sulcus_numbers = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]¶

unique_sulcus_label_pairs = [[1012, 1028], [2012, 2028], [1003, 1028], [2003, 2028], [1027, 1028], [2027, 2028], [1003, 1018], [2003, 2018], [1003, 1019], [2003, 2019], [1003, 1020], [2003, 2020], [1018, 1027], [2018, 2027], [1019, 1027], [2019, 2027], [1020, 1027], [2020, 2027], [1024, 1028], [2024, 2028], [1003, 1024], [2003, 2024], [1024, 1027], [2024, 2027], [1018, 1024], [2018, 2024], [1019, 1024], [2019, 2024], [1020, 1024], [2020, 2024], [1022, 1024], [2022, 2024], [1022, 1029], [2022, 2029], [1022, 1031], [2022, 2031], [1029, 1031], [2029, 2031], [1008, 1029], [2008, 2029], [1008, 1031], [2008, 2031], [1030, 1031], [2030, 2031], [1008, 1011], [2008, 2011], [1011, 1029], [2011, 2029], [1011, 1015], [2011, 2015], [1009, 1011], [2009, 2011], [1015, 1030], [2015, 2030], [1009, 1015], [2009, 2015], [1012, 1035], [2012, 2035], [1030, 1035], [2030, 2035], [1034, 1035], [2034, 2035], [1002, 1035], [2002, 2035], [1010, 1035], [2010, 2035], [1023, 1035], [2023, 2035], [1026, 1035], [2026, 2035], [1022, 1035], [2022, 2035], [1024, 1035], [2024, 2035], [1031, 1035], [2031, 2035], [1030, 1034], [2030, 2034], [1002, 1014], [2002, 2014], [1010, 1014], [2010, 2014], [1014, 1023], [2014, 2023], [1014, 1026], [2014, 2026], [1002, 1028], [2002, 2028], [1010, 1028], [2010, 2028], [1023, 1028], [2023, 2028], [1026, 1028], [2026, 2028], [1002, 1017], [2002, 2017], [1010, 1017], [2010, 2017], [1017, 1023], [2017, 2023], [1017, 1026], [2017, 2026], [1017, 1025], [2017, 2025], [1017, 1028], [2017, 2028], [1005, 1025], [2005, 2025], [1013, 1025], [2013, 2025], [1002, 1013], [2002, 2013], [1010, 1013], [2010, 2013], [1013, 1023], [2013, 2023], [1013, 1026], [2013, 2026], [1014, 1028], [2014, 2028], [1003, 1012], [2003, 2012], [1012, 1027], [2012, 2027], [1012, 1018], [2012, 2018], [1012, 1019], [2012, 2019], [1012, 1020], [2012, 2020], [1012, 1014], [2012, 2014], [1007, 1009], [2007, 2009], [1007, 1011], [2007, 2011], [1006, 1007], [2006, 2007], [1007, 1016], [2007, 2016], [1007, 1013], [2007, 2013]]¶

ventricle_colors = [[120, 18, 134], [196, 58, 250], [120, 18, 134], [196, 58, 250], [204, 182, 142], [42, 204, 164], [120, 190, 150]]¶

ventricle_names = ['Left-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Right-Lateral-Ventricle', 'Right-Inf-Lat-Vent', '3rd-Ventricle', '4th-Ventricle', '5th-Ventricle']¶

ventricle_numbers = [4, 5, 43, 44, 14, 15, 72]¶

x = [234, 169, 30]¶

extract_numbers_names_colors(FreeSurferColorLUT='')¶

Extract lists of numbers, names, and colors representing anatomical brain regions from FreeSurfer’s FreeSurferColorLUT.txt lookup table file.

Parameters:	FreeSurferColorLUT (string) – full path to FreeSurferColorLUT.txt file (else uses local Python file)
Returns:	numbers (list of integers) – numbers representing anatomical labels from FreeSurferColorLUT.txt names (list of integers) – names for anatomical regions from FreeSurferColorLUT.txt colors (list of integers) – colors associated with anatomical labels from FreeSurferColorLUT.txt

Examples

>>> from mindboggle.mio.labels import extract_numbers_names_colors # doctest: +SKIP
>>> ennc = extract_numbers_names_colors # doctest: +SKIP
>>> en1,en2,ec = ennc('/Applications/freesurfer/FreeSurferColorLUT.txt') # doctest: +SKIP

return_numbers_names_colors()¶

Return lists of numbers, names, and colors representing anatomical brain regions derived from FreeSurfer’s FreeSurferColorLUT.txt lookup file.

Returns:	numbers (list of integers) – numbers representing anatomical labels from FreeSurferColorLUT.txt names (list of integers) – names for anatomical regions from FreeSurferColorLUT.txt colors (list of integers) – colors associated with anatomical labels from FreeSurferColorLUT.txt

Examples

>>> import numpy as np
>>> from mindboggle.mio.labels import return_numbers_names_colors
>>> numbers, names, colors = return_numbers_names_colors()
>>> len(np.unique(numbers))
1266
>>> names[0:3]
['Unknown', 'Left-Cerebral-Exterior', 'Left-Cerebral-White-Matter']
>>> colors[0:3]
[[0, 0, 0], [70, 130, 180], [245, 245, 245]]

mindboggle.mio.plots module¶

Plotting functions.

Authors:

Arno Klein, 2012-2013 (arno@mindboggle.info) http://binarybottle.com

Contributors:

Hal Canary <http://cs.unc.edu/~hal>: vtkviewer.py called by vtkviewer()

boxplots_of_lists(columns, xlabel='', ylabel='', ylimit=None, title='')¶

Construct a box plot for each table column.

Parameters:	columns (list of lists) – list of lists of floats or integers xlabel (str) – x-axis label ylabel (str) – y-axis label ylimit (float) – maximum y-value title (str) – title

Examples

>>> from mindboggle.mio.plots import boxplots_of_lists
>>> columns = [[1,1,2,2,2,2,2,2,3,3,3,4,4,8],[2,2,3,3,3,3,5,6,7],
...            [2,2,2.5,2,2,2,3,3,3,3,5,6,7]]
>>> xlabel = 'xlabel'
>>> ylabel = 'ylabel'
>>> ylimit = None
>>> title = 'title'
>>> boxplots_of_lists(columns, xlabel, ylabel, ylimit, title) # doctest: +SKIP

histogram_of_vtk_scalars(vtk_file, nbins=100)¶

Plot histogram of VTK surface mesh scalar values.

Parameters:	vtk_file (string) – name of VTK file with scalar values to plot nbins (integer) – number of histogram bins

Examples

>>> import os
>>> from mindboggle.mio.plots import histogram_of_vtk_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> os.rename(vtk_file, vtk_file + '.nii.gz')
>>> vtk_file = vtk_file + '.nii.gz'
>>> histogram_of_vtk_scalars(vtk_file, nbins=500) # doctest: +SKIP

histograms_of_lists(columns, column_name='', ignore_columns=[], nbins=100, axis_limits=[], titles=[])¶

Construct a histogram for each table column.

Parameters:

columns (list of lists) – list of lists of floats or integers
column_name (string) – column name
ignore_columns (list of integers) – indices to table columns or sublists to exclude
nbins (integer) – number of histogram bins
axis_limits (list of four integers) – range of x- and y-axis ranges: [x-low, x-high, y-low, y-high]
y_lim (list of two integers) – range of y-axis values
titles (list of strings (length = number of columns - 1)) – histogram titles (if empty, use column headers)

Examples

>>> from mindboggle.mio.plots import histograms_of_lists
>>> columns = [[1,1,2,2,2,2,2,2,3,3,3,4,4,8],[2,2,3,3,3,3,5,6,7]]
>>> column_name = 'label: thickness: median (weighted)'
>>> ignore_columns = []
>>> nbins = 100
>>> axis_limits = []
>>> titles = ['title1','title2']
>>> histograms_of_lists(columns, column_name, ignore_columns, nbins,
...                     axis_limits, titles) # doctest: +SKIP

plot_mask_surface(vtk_file, mask_file='', nonmask_value=-1, masked_output='', remove_nonmask=False, program='vtkviewer', use_colormap=False, colormap_file='', background_value=-1)¶

Use vtkviewer or mayavi2 to visualize VTK surface mesh data.

If a mask_file is provided, a temporary masked file is saved, and it is this file that is viewed.

If using vtkviewer, optionally provide colormap file or set $COLORMAP environment variable.

Parameters:

vtk_file (string) – name of VTK surface mesh file
mask_file (string) – name of VTK surface mesh file to mask vtk_file vertices
nonmask_value (integer) – nonmask (usually background) value
masked_output (string) – temporary masked output file name
remove_nonmask (bool) – remove vertices that are not in mask? (otherwise assign nonmask_value)
program (string {'vtkviewer', 'mayavi2'}) – program to visualize VTK file
use_colormap (bool) – use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file (string) – use colormap in given file if use_colormap==True? if empty and use_colormap==True, use file set by $COLORMAP environment variable
background_value (integer or float) – background value

Examples

>>> import os
>>> from mindboggle.mio.plots import plot_mask_surface
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['freesurfer_labels'], '', '.vtk')
>>> os.rename(vtk_file, vtk_file + '.nii.gz')
>>> vtk_file = vtk_file + '.nii.gz'
>>> mask_file = ''
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = ''
>>> background_value = -1
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output,
...     remove_nonmask, program, use_colormap, colormap_file,
...     background_value) # doctest: +SKIP

plot_surfaces(vtk_files, use_colormap=False, colormap_file='')¶

Use vtkviewer to visualize one or more VTK surface files.

Optionally provide colormap file or set $COLORMAP environment variable.

Parameters:	vtk_files (string or list of strings) – name of VTK surface mesh file or list of file names use_colormap (bool) – use Paraview-style XML colormap file? colormap_file (string) – use colormap in given file if use_colormap==True? if empty and use_colormap==True, use file set by $COLORMAP environment variable

Examples

>>> from mindboggle.mio.plots import plot_surfaces
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> labels_file = fetch_data(urls['left_manual_labels'], '', '.vtk') #'left_freesurfer_labels'
>>> use_colormap = True
>>> colormap_file = '/software/vtk_cpp_tools/colormap.xml' # doctest: +SKIP
>>> plot_surfaces(labels_file, use_colormap, colormap_file) # doctest: +SKIP

Plot manual labels on folds of the left hemisphere:

>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> from mindboggle.mio.vtks import rewrite_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> urls, fetch_data = prep_tests()
>>> labels_file = fetch_data(urls['left_manual_labels'], '', '.vtk') #'left_freesurfer_labels'
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> labels, name = read_scalars(labels_file, True, True)
>>> folds, name = read_scalars(folds_file, True, True)
>>> background_value = -1
>>> # Limit number of folds to speed up the test:
>>> limit_folds = False
>>> if limit_folds:
...     fold_numbers = [4] #[4, 6]
...     indices = [i for i,x in enumerate(folds) if x in fold_numbers]
...     i0 = [i for i,x in enumerate(folds) if x not in fold_numbers]
...     folds[i0] = background_value
... else:
...     indices = [i for i,x in enumerate(folds) if x != background_value]
>>> folds[indices] = labels[indices]
>>> rewrite_scalars(labels_file, 'labeled_folds.vtk',
...                 folds, 'skeleton', folds, background_value) # doctest: +SKIP
>>> plot_surfaces('labeled_folds.vtk') # doctest: +SKIP

plot_volumes(volume_files, command='fslview')¶

Use fslview to visualize image volume data.

Parameters:	volume_files (list of strings) – names of image volume files command (string) – plotting software command

Examples

>>> import os
>>> from mindboggle.mio.plots import plot_volumes
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> label_file1 = fetch_data(urls['freesurfer_labels'], '', '.vtk')
>>> label_file2 = fetch_data(urls['freesurfer_labels'], '', '.vtk')
>>> volume_files = [label_file1, label_file2]
>>> command = 'fslview'
>>> command = '/Applications/ITK-SNAP.app/Contents/MacOS/InsightSNAP'
>>> plot_volumes(volume_files, command=command) # doctest: +SKIP

scatterplot_list_pairs(columns, ignore_first_column=False, plot_line=True, connect_markers=True, mstyle='o', msize=1, mcolor='', title='', x_label='', y_label='', limit=None, legend=True, legend_labels=[])¶

Scatter plot pairs of columns.

Parameters:

columns (list of lists of numbers) – alternating columns of data (all of the same length)
ignore_first_column (bool) – exclude first column?
plot_line (bool) – plot identity line?
connect_markers (bool) – connect markers?
mstyle (string) – marker style
msize (integer) – marker size
mcolor (string) – marker color (if empty, generate range of colors)
title (string) – title
x_label (string) – description of x_column
y_label (string) – description of other columns
limit (float) – x- and y-axis extent
legend (bool) – plot legend?
legend_labels (list of strings (length = number of columns)) – legend labels

Examples

>>> from mindboggle.mio.plots import scatterplot_list_pairs
>>> columns = [['labels'], [1,1,2,2,2,3,3,4,4,8],[2,2,3,3,3,3,5,6,7,7],
...            [1,1.5,2.1,1.8,2.2,3,3.1,5,7,6],
...            [1.2,0.5,2,1.3,1.2,3,1,5.2,4,4.5]]
>>> ignore_first_column = True
>>> plot_line = True
>>> connect_markers = True
>>> mstyle = 'o'
>>> msize = 10
>>> mcolor = ''
>>> title = 'title'
>>> x_label = 'xlabel'
>>> y_label = 'ylabel'
>>> limit = None
>>> legend = True
>>> legend_labels = ['mark1','mark2']
>>> scatterplot_list_pairs(columns, ignore_first_column, plot_line,
...                        connect_markers, mstyle, msize, mcolor, title,
...                        x_label, y_label, limit, legend, legend_labels) # doctest: +SKIP

scatterplot_lists(y_columns, x_column, ignore_columns=[], plot_line=True, connect_markers=True, mstyle='o', msize=1, title='', x_label='', y_label='', legend=True, legend_labels=[])¶

Scatter plot columns against the values of one of the columns.

Parameters:

y_columns (list of lists of numbers) – columns of data (all of the same length)
x_column (list of numbers) – column of numbers against which other columns are plotted
ignore_columns (list of integers) – indices to y_columns to exclude
plot_line (bool) – plot identity line?
connect_markers (bool) – connect markers?
mstyle (string) – marker style
msize (integer) – marker size
title (string) – title
x_label (string) – description of x_column
y_label (string) – description of y_columns
legend (bool) – plot legend?
legend_labels (list of strings (length = number of y_columns)) – legend labels

Examples

>>> from mindboggle.mio.plots import scatterplot_lists
>>> y_columns = [[1,1,2,2,2,3,3,4,4,8],[2,2,3,3,3,3,5,6,7,7]]
>>> x_column = [1,1.5,2.1,1.8,2.2,3,3.1,5,7,6]
>>> ignore_columns = []
>>> plot_line = True
>>> connect_markers = True
>>> mstyle = 'o'
>>> msize = 10
>>> title = 'title'
>>> x_label = 'xlabel'
>>> y_label = 'ylabel'
>>> legend = True
>>> legend_labels = ['mark1','mark2']
>>> scatterplot_lists(y_columns, x_column, ignore_columns, plot_line,
...                   connect_markers, mstyle, msize, title, x_label,
...                   y_label, legend, legend_labels) # doctest: +SKIP

mindboggle.mio.tables module¶

Functions for creating tables.

Authors:

Arno Klein, 2012-2016 (arno@mindboggle.info) http://binarybottle.com

collate_participant_tables(subject_ids, base_dir)¶

Generate a pandas dataframe across all subjects

Parameters:	subject_ids (list) – a list of subject identifiers in base_dir (str) – path to a mindboggle output base directory (mindboggled)
Returns:	collated_table – rows of subject_ids, and columns of shape measures
Return type:	pandas DataFrame

Examples

>>> from mindboggle.mio.tables import collate_participant_tables
>>> subject_ids = ['arno', 'arno'] # normally two different subjects
>>> base_dir = os.environ['MINDBOGGLE_DATA'] # doctest: +SKIP
>>> dft = collate_participant_tables(subject_ids, base_dir) # doctest: +SKIP
>>> dft['lcsfs-sylvian fissure-area'] # doctest: +SKIP
arno    4.641015
arno    4.641015
Name: lcsfs-sylvian fissure-area, dtype: float64

explode_mindboggle_tables(subject_path='', output_path='.', break_column='label ID', verbose=False)¶

Given the path to a subject’s Mindboggle output data, break up each surface’s shape table into separate tables, one for each label/sulcus index.

Parameters:	subject_path (string) – path to subject directory output_path (string) – output path/directory break_column (string) – column header that contains the integers to break up into tables verbose (bool) – print statements?

Examples

>>> import os
>>> from mindboggle.mio.tables import explode_mindboggle_tables
>>> subject_path = os.environ['MINDBOGGLE_DATA'] # doctest: +SKIP
>>> output_path = '.'
>>> break_column = 'label ID'
>>> verbose = False
>>> explode_mindboggle_tables(subject_path, output_path, break_column,
...                           verbose) # doctest: +SKIP

explode_table(input_table='', column_headers=[], output_path=None, output_stem='', break_column='label ID', verbose=False)¶

Break up a table into separate tables, one for each index value for a given column.

Parameters:	input_table (string) – path to input table to be broken up into separate tables column_headers (list of strings) – headers for columns to break up by break_column indices output_path (string) – output path/directory output_stem (string) – append string to output file name break_column (string) – column header that contains the integers to break up into tables verbose (bool) – print statements?
Returns:	output_tables – paths to output tables
Return type:	list of strings

Examples

>>> import os
>>> from mindboggle.mio.tables import explode_table
>>> input_table = os.path.join(os.environ['MINDBOGGLE_DATA'],
...     'left_cortical_surface', 'vertices.csv') # doctest: +SKIP
>>> column_headers = ['travel depth', 'geodesic depth', 'mean curvature',
...                   'freesurfer curvature', 'freesurfer thickness',
...                   'freesurfer convexity (sulc)']
>>> output_path = '.'
>>> output_stem = 'label'
>>> break_column = 'label ID'
>>> verbose = False
>>> output_tables = explode_table(input_table, column_headers,
...                               output_path, output_stem, break_column,
...                               verbose) # doctest: +SKIP

fname2df(fname)¶

Read a single csv into a single dataframe row

Parameters:	fname (str) – a path to a mindboggle output file

select_column_from_mindboggle_tables(subjects, hemi, index, tables_dir, table_name, is_surface_table=True, write_table=True, output_table='')¶

Select column from Mindboggle shape tables and make a new table.

For example, extract the median travel depth column for the label regions across a set of subjects, and make a new table.

Expects::: <tables_dir>/<subject>/tables/[‘left’,’right’]_cortical_surface/<table_name>

Parameters:

subjects (list of strings) – names of subjects processed by Mindboggle
hemi (string) – hemisphere in {‘left’, ‘right}
index (integer) – index for column to select
tables_dir (string) – name of Mindboggle tables directory
table_name (string) – name of Mindboggle table file
is_surface_table (bool) – if True, use path to surface tables
write_table (bool) – write output table?
output_table (string) – output table file name

Returns:

tables (list of strings) – input table files (full paths)
columns (list of lists of floats or integers) – columns of data
output_table (string) – output table file name

Examples

>>> import os
>>> from mindboggle.mio.tables import select_column_from_mindboggle_tables
>>> path = os.environ['MINDBOGGLE_DATA'] # doctest: +SKIP
>>> subject1 = os.path.basename(path) # doctest: +SKIP
>>> subject2 = os.path.basename(path) # doctest: +SKIP
>>> subjects = [subject1, subject2] # doctest: +SKIP
>>> hemi = 'left'
>>> index = 2
>>> tables_dir = os.path.dirname(path) # doctest: +SKIP
>>> table_name = "label_shapes.csv"
>>> label_name = 'Label name'
>>> is_surface_table = True
>>> write_table = True
>>> output_table = ''
>>> tables, cols, output = select_column_from_mindboggle_tables(subjects,
...     hemi, index, tables_dir, table_name, is_surface_table,
...     write_table, output_table) # doctest: +SKIP
>>> cols[0][0] # doctest: +SKIP
878.03969839999979
>>> cols[0][1] # doctest: +SKIP
3085.6236725000008
>>> cols[0][2] # doctest: +SKIP
1761.2330760000002

select_column_from_tables(tables, index=0, write_table=True, output_table='')¶

Select column from list of tables, make a new table.

Note: If more than one table, column must be of the same length.

Parameters:

tables (list of strings) – table files (full paths)
index (integer) – index for column to select (from each table)
write_table (bool) – write output table?
output_table (string) – output table file name

Returns:

tables (list of strings) – input table files (full paths)
columns (list of lists of floats or integers) – columns of data
column_name (string) – column name to select
row_names (list of strings) – row labels (common strings in the label column of tables)
row_names_title (string) – row_names column header
row_stats (list) – row statistics
row_stats_names (list) – names of column statistics
output_table (string) – output table file name

Examples

>>> from mindboggle.mio.tables import select_column_from_tables
>>> from mindboggle.mio.fetch_data import prep_tests
>>> import numpy as np
>>> urls, fetch_data = prep_tests()
>>> tables = [fetch_data(urls['thickinthehead_freesurfer_labels_table'], '', '.csv'),
...           fetch_data(urls['thickinthehead_freesurfer_labels_table'], '', '.csv')]
>>> index = 2
>>> write_table = True
>>> output_table = ''
>>> output = select_column_from_tables(tables, index, write_table,
...                                    output_table)
>>> columns = output[1][0]
>>> np.allclose(columns[:3], [2.801, 3.943, 4.028])
True

short_name(filepath)¶

Generate a short name for a given branch of the mindboggle output

Parameters:	filepath (str) – a path to a mindboggle output file

write_average_face_values_per_label(input_indices_vtk, input_values_vtk='', area_file='', output_stem='', exclude_values=[-1], background_value=-1, verbose=False)¶

Write out a separate csv table file for each integer in (the first) scalar list of an input VTK file. Optionally write the values drawn from a second VTK file.

Parameters:

input_indices_vtk (string) – path of the input VTK file that contains indices as scalars
input_values_vtk (string) – path of the input VTK file that contains values as scalars
output_stem (string) – path and stem of the output VTK file
exclude_values (list or array) – values to exclude
background_value (integer or float) – background value in output VTK files
scalar_name (string) – name of a lookup table of scalars values
verbose (bool) – print statements?

Examples

>>> import os
>>> from mindboggle.mio.tables import write_average_face_values_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_indices_vtk = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> input_values_vtk = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> output_stem = 'labels_thickness'
>>> exclude_values = [-1]
>>> background_value = -1
>>> verbose = False
>>> write_average_face_values_per_label(input_indices_vtk,
...     input_values_vtk, area_file, output_stem, exclude_values,
...     background_value, verbose)

View vtk file (skip test):

>>> from mindboggle.mio.plots import plot_surfaces
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> plot_surfaces(example_vtk) # doctest: +SKIP

write_face_vertex_averages(input_file, output_table='', area_file='')¶

Make table of average vertex values per face (divided by face area if area_file provided).

Parameters:	input_file (string) – name of VTK file with scalars to average area_file (string) – name of VTK file with surface area scalar values output_table (string) – output table filename
Returns:	output_table – output table filename
Return type:	string

Examples

>>> from mindboggle.mio.tables import write_face_vertex_averages
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> output_table = ''
>>> output_table = write_face_vertex_averages(input_file, output_table,
...                                           area_file)

write_shape_stats(labels_or_file=[], sulci=[], fundi=[], affine_transform_files=[], inverse_booleans=[], transform_format='itk', area_file='', normalize_by_area=False, mean_curvature_file='', travel_depth_file='', geodesic_depth_file='', freesurfer_thickness_file='', freesurfer_curvature_file='', freesurfer_sulc_file='', labels_spectra=[], labels_spectra_IDs=[], sulci_spectra=[], sulci_spectra_IDs=[], labels_zernike=[], labels_zernike_IDs=[], sulci_zernike=[], sulci_zernike_IDs=[], exclude_labels=[-1], verbose=False)¶

Make tables of shape statistics per label, sulcus, and/or fundus.

There can be thousands of vertices in a single feature such as a gyrus, sulcus, or fundus, and for per-vertex shape measures, it makes sense to characterize their collective shape as a distribution of shape values. Mindboggle’s stats_per_label function generates tables of summary statistical measures for these distributions, and includes the shape measures computed on cortical features as well.

Note ::: This function is tailored for Mindboggle outputs.

Parameters:

labels_or_file (list or string) – label number for each vertex or name of VTK file with index scalars
sulci (list of integers) – indices to sulci, one per vertex, with -1 indicating no sulcus
fundi (list of integers) – indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files (list of strings) – affine transform files to standard space
inverse_booleans (list of of zeros and ones) – for each transform, 1 to take the inverse, else 0
transform_format (string) – format for transform file Ex: ‘txt’ for text, ‘itk’ for ITK, and ‘mat’ for Matlab format
area_file (string) – name of VTK file with surface area scalar values
normalize_by_area (bool) – normalize all shape measures by area of label/feature? (UNTESTED)
mean_curvature_file (string) – name of VTK file with mean curvature scalar values
travel_depth_file (string) – name of VTK file with travel depth scalar values
geodesic_depth_file (string) – name of VTK file with geodesic depth scalar values
freesurfer_thickness_file (string) – name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file (string) – name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file (string) – name of VTK file with FreeSurfer convexity (sulc) scalar values
labels_spectra (list of lists of floats) – Laplace-Beltrami spectra for each labeled region
labels_spectra_IDs (list of integers) – unique labels for labels_spectra
sulci_spectra (list of lists of floats) – Laplace-Beltrami spectra for each sulcus
sulci_spectra_IDs (list of integers) – unique sulcus IDs for sulci_spectra
labels_zernike (list of lists of floats) – Zernike moments for each labeled region
labels_zernike_IDs (list of integers) – unique labels for labels_zernike
sulci_zernike (list of lists of floats) – Zernike moments for each sulcus
sulci_zernike_IDs (list of integers) – unique sulcus IDs for sulci_zernike
exclude_labels (list of lists of integers) – indices to be excluded (in addition to -1)
verbose (bool) – print statements?

Returns:

label_table (string) – output table filename for label shapes
sulcus_table (string) – output table filename for sulcus shapes
fundus_table (string) – output table filename for fundus shapes

Examples

>>> from mindboggle.mio.tables import write_shape_stats
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> label_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> sulci_file = fetch_data(urls['left_sulci'], '', '.vtk')
>>> fundi_file = fetch_data(urls['left_fundus_per_sulcus'], '', '.vtk')
>>> mean_curvature_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> travel_depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> geodesic_depth_file = fetch_data(urls['left_geodesic_depth'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> freesurfer_thickness_file = ''
>>> freesurfer_curvature_file = ''
>>> freesurfer_sulc_file = ''
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_files = []
>>> inverse_booleans = []
>>> transform_format = 'itk'
>>> normalize_by_area = False
>>> labels, name = read_scalars(label_file)
>>> labels_spectra = []
>>> labels_spectra_IDs = []
>>> sulci_spectra = []
>>> sulci_spectra_IDs = []
>>> labels_zernike = []
>>> labels_zernike_IDs = []
>>> sulci_zernike = []
>>> sulci_zernike_IDs = []
>>> exclude_labels = [-1]
>>> verbose = False
>>> label_table, sulcus_table, fundus_table = write_shape_stats(label_file,
...     sulci, fundi, affine_transform_files, inverse_booleans,
...     transform_format, area_file, normalize_by_area,
...     mean_curvature_file, travel_depth_file, geodesic_depth_file,
...     freesurfer_thickness_file, freesurfer_curvature_file,
...     freesurfer_sulc_file, labels_spectra, labels_spectra_IDs,
...     sulci_spectra, sulci_spectra_IDs, labels_zernike,
...     labels_zernike_IDs, sulci_zernike, sulci_zernike_IDs,
...     exclude_labels, verbose)

write_vertex_measures(output_table, labels_or_file, sulci=[], fundi=[], affine_transform_files=[], inverse_booleans=[], transform_format='itk', area_file='', mean_curvature_file='', travel_depth_file='', geodesic_depth_file='', freesurfer_thickness_file='', freesurfer_curvature_file='', freesurfer_sulc_file='')¶

Make a table of shape values per vertex.

Note ::: This function is tailored for Mindboggle outputs.

Parameters:	output_table (string) – output file (full path) labels_or_file (list or string) – label number for each vertex or name of VTK file with index scalars sulci (list of integers) – indices to sulci, one per vertex, with -1 indicating no sulcus fundi (list of integers) – indices to fundi, one per vertex, with -1 indicating no fundus affine_transform_files (list of strings) – affine transform files to standard space inverse_booleans (list of of zeros and ones) – for each transform, 1 to take the inverse, else 0 transform_format (string) – format for transform file Ex: ‘txt’ for text, ‘itk’ for ITK, and ‘mat’ for Matlab format area_file (string) – name of VTK file with surface area scalar values mean_curvature_file (string) – name of VTK file with mean curvature scalar values travel_depth_file (string) – name of VTK file with travel depth scalar values geodesic_depth_file (string) – name of VTK file with geodesic depth scalar values freesurfer_thickness_file (string) – name of VTK file with FreeSurfer thickness scalar values freesurfer_curvature_file (string) – name of VTK file with FreeSurfer curvature (curv) scalar values freesurfer_sulc_file (string) – name of VTK file with FreeSurfer convexity (sulc) scalar values
Returns:	output_table
Return type:	table file name for vertex shape values

Examples

>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.tables import write_vertex_measures
>>> output_table = '' #vertex_shapes.csv'
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> labels_or_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> sulci_file = fetch_data(urls['left_sulci'], '', '.vtk')
>>> fundi_file = fetch_data(urls['left_fundus_per_sulcus'], '', '.vtk')
>>> mean_curvature_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> travel_depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> geodesic_depth_file = fetch_data(urls['left_geodesic_depth'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> freesurfer_thickness_file = fetch_data(urls['left_freesurfer_thickness'], '', '.vtk')
>>> freesurfer_curvature_file = fetch_data(urls['left_freesurfer_curvature'], '', '.vtk')
>>> freesurfer_sulc_file = fetch_data(urls['left_freesurfer_sulc'], '', '.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> if fundi_file:
...     fundi, name = read_scalars(fundi_file)
... else:
...     fundi = []
>>> affine_transform_file = fetch_data(urls['affine_mni_transform'], '', '.txt')
>>> inverse_booleans = [1]
>>> transform_format = 'itk'
>>> swap_xy = True
>>> affine_rename = affine_transform_file + '.txt'
>>> os.rename(affine_transform_file, affine_rename)
>>> os.rename(labels_or_file, labels_or_file + '.vtk')
>>> os.rename(area_file, area_file + '.vtk')
>>> os.rename(mean_curvature_file, mean_curvature_file + '.vtk')
>>> os.rename(travel_depth_file, travel_depth_file + '.vtk')
>>> os.rename(geodesic_depth_file, geodesic_depth_file + '.vtk')
>>> os.rename(freesurfer_thickness_file, freesurfer_thickness_file + '.vtk')
>>> os.rename(freesurfer_curvature_file, freesurfer_curvature_file + '.vtk')
>>> os.rename(freesurfer_sulc_file, freesurfer_sulc_file + '.vtk')
>>> labels_or_file = labels_or_file + '.vtk'
>>> area_file = area_file + '.vtk'
>>> mean_curvature_file = mean_curvature_file + '.vtk'
>>> travel_depth_file = travel_depth_file + '.vtk'
>>> geodesic_depth_file = geodesic_depth_file + '.vtk'
>>> freesurfer_thickness_file = freesurfer_thickness_file + '.vtk'
>>> freesurfer_curvature_file = freesurfer_curvature_file + '.vtk'
>>> freesurfer_sulc_file = freesurfer_sulc_file + '.vtk'
>>> affine_transform_files = [] # [affine_rename] # requires ANTs to test
>>> output_table = write_vertex_measures(output_table, labels_or_file,
...     sulci, fundi, affine_transform_files, inverse_booleans,
...     transform_format, area_file, mean_curvature_file,
...     travel_depth_file, geodesic_depth_file, freesurfer_thickness_file,
...     freesurfer_curvature_file, freesurfer_sulc_file)

mindboggle.mio.vtks module¶

Functions related to reading and writing VTK format files.

Authors:

Forrest Sheng Bao, 2012-2013 (forrest.bao@gmail.com) http://fsbao.net
Arno Klein, 2012-2016 (arno@mindboggle.info) http://binarybottle.com
Oliver Hinds, 2013 (ohinds@gmail.com)
Daniel Haehn, 2013 (daniel.haehn@childrens.harvard.edu)

apply_affine_transforms(transform_files, inverse_booleans, transform_format='itk', vtk_or_points=[], vtk_file_stem='affine_')¶

Transform coordinates using an affine matrix.

For use with ANTs, x and y columns are multiplied by -1 before and after applying the inverse affine transform because ITK uses a different coordinate system than the NIfTI coordinate system.

Parameters:

files (transform) – names of affine transform files
inverse_booleans (list of of zeros and ones) – for each transform, 1 to take the inverse, else 0
transform_format (string) – format for transform file (currently ‘itk’); complications arise with other formats, such as ‘txt’ for text, or ‘mat’ for Matlab format, since software-specific assignment of parameters such as the origin need to be taken into account
vtk_or_points (string or list of lists of three integers) – name of VTK file containing point coordinate data, or the data (if vtk file, assumes scalars are a list of floats or integers)
vtk_file_stem (string) – save transformed coordinates in a vtk file with this file prepend (empty string if vtk_or_points is points)

Returns:

affine_points (list of lists of floats) – transformed coordinates
output_file (string or None (if not vtk_file_stem or vtk_or_points is points)) – name of VTK file containing transformed point data

Examples

>>> import numpy as np
>>> from mindboggle.mio.vtks import apply_affine_transforms
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_or_points = fetch_data(urls['left_pial'], '', '.vtk')
>>> transform_files = [fetch_data(urls['ants_affine_subject2template'], '', '.txt')]
>>> inverse_booleans = [1]
>>> transform_format = 'itk'
>>> vtk_file_stem = 'affine_'
>>> affine_points, output_file = apply_affine_transforms(transform_files,
...     inverse_booleans, transform_format, vtk_or_points, vtk_file_stem) # doctest: +SKIP
>>> print(np.array_str(np.array(affine_points[0:5]),
...       precision=5, suppress_small=True)) # doctest: +SKIP
[[-123.02735 -228.19407 -101.14381]
 [-123.46156 -228.39812 -101.24258]
 [-124.22058 -228.39971 -101.15211]
 [-121.66154 -228.72653 -102.39572]
 [-122.55212 -228.56696 -101.82275]]

View resulting vtk file (skip test):

>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(output_file, vtk_or_points) # doctest: +SKIP

explode_scalars(input_indices_vtk, input_values_vtk='', output_stem='', exclude_values=[-1], background_value=-1, output_scalar_name='scalars', remove_background_faces=True, reindex=True, verbose=False)¶

Write out a separate VTK file for each integer (not in exclude_values) in (the first) scalar list of an input VTK file. Optionally write the values drawn from a second VTK file, remove background values, and reindex indices.

Parameters:	input_indices_vtk (string) – path of the input VTK file that contains indices as scalars (assumes that the scalars are a list of floats or integers) input_values_vtk (string) – path of the input VTK file that contains values as scalars output_stem (string) – path and stem of the output VTK file exclude_values (list or array) – values to exclude background_value (integer or float) – background value in output VTK files remove_background_faces (bool) – remove all faces whose three vertices are not all a given index? reindex (bool) – reindex all indices in faces? verbose (bool) – print statements?
Returns:	output_files – paths to output VTK files
Return type:	list of strings

Examples

>>> # Example 1:  explode sulci with thickness values
>>> import os
>>> from mindboggle.mio.vtks import explode_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_indices_vtk = fetch_data(urls['left_sulci'], '', '.vtk')
>>> input_values_vtk = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> output_stem = 'explode_scalars_sulcus_depth'
>>> exclude_values = [-1]
>>> background_value = -1,
>>> output_scalar_name = 'sulcus_depth'
>>> remove_background_faces = True
>>> reindex = True
>>> verbose = False
>>> output_files = explode_scalars(input_indices_vtk, input_values_vtk,
...     output_stem, exclude_values, background_value, output_scalar_name,
...     remove_background_faces, reindex, verbose)
>>> os.path.basename(output_files[0])
'explode_scalars_sulcus_depth1.vtk'

View Example 1 results (skip test):

>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(output_stem + '1.vtk') # doctest: +SKIP

Example 2: explode labels

>>> input_indices_vtk = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> input_values_vtk = ''
>>> output_stem = 'explode_scalars_label'
>>> output_scalar_name = 'labels'
>>> output_files = explode_scalars(input_indices_vtk, input_values_vtk,
...     output_stem, exclude_values, background_value, output_scalar_name,
...     remove_background_faces, reindex, verbose)
>>> os.path.basename(output_files[0])
'explode_scalars_label999.vtk'

View Example 2 results (skip test):

>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(output_stem + '1.vtk') # doctest: +SKIP

explode_scalars_mindboggle(subject_path, output_path='', pieces='labels', background_value=-1, verbose=False)¶

Given the path to a subject’s Mindboggle output data, break up each shape surface VTK file into separate VTK files, one for each label/sulcus index.

Parameters:	subject_path (string) – path to subject directory output_path (string) – output path/directory pieces (string) – name of structures to explode (e.g., ‘labels’, ‘sulci’) background_value (integer or float) – background value verbose (bool) – print statements?

Examples

>>> # Explode surface shape files by label values:
>>> import os
>>> from mindboggle.mio.vtks import explode_scalars_mindboggle
>>> subject_path = '/Users/arno/mindboggled/Twins-2-1'
>>> output_path = os.getcwd()
>>> pieces = 'labels'
>>> background_value = -1
>>> verbose = False
>>> explode_scalars_mindboggle(subject_path, output_path, pieces,
...                            background_value, verbose) # doctest: +SKIP

View example result (skip test):

>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces('left_exploded/travel_depth_1035.vtk') # doctest: +SKIP

freesurfer_annot_to_vtk(annot_file, vtk_file, output_vtk='', background_value=-1)¶

Load a FreeSurfer .annot file and save as a VTK format file.

Parameters:

annot_file (string) – name of FreeSurfer .annot file
vtk_file (string) – name of VTK surface file
output_vtk (string) – name of output VTK file, where each vertex is assigned the corresponding annot value
background_value (integer or float) – background value

Returns:

labels (list) – integers (one label per vertex)
output_vtk (string) – name of output VTK file, where each vertex is assigned the corresponding shape value

Examples

>>> import numpy as np
>>> from mindboggle.mio.vtks import freesurfer_annot_to_vtk
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> annot_file = fetch_data(urls['left_freesurfer_aparc_annot'], '', '.annot')
>>> vtk_file = fetch_data(urls['left_pial'], '', '.vtk')
>>> output_vtk = 'freesurfer_annot_to_vtk.vtk'
>>> background_value = -1
>>> labels, output_vtk = freesurfer_annot_to_vtk(annot_file,
...                          vtk_file, output_vtk, background_value)
>>> nlabels = [len(np.where(labels == x)[0]) for x in np.unique(labels)]
>>> nlabels[0:10]
[8305, 1414, 1171, 4096, 2213, 633, 5002, 6524, 4852, 1823]

View output vtk file (skip test):

>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(output_vtk) # doctest: +SKIP

freesurfer_curvature_to_vtk(surface_file, vtk_file, output_vtk='', background_value=-1)¶

Convert FreeSurfer curvature, thickness, or convexity file to VTK format.

Parameters:	surface_file (string) – name of FreeSurfer surface file vtk_file (string) – name of VTK surface file output_vtk (string) – name of output VTK file background_value (integer or float) – background value
Returns:	output_vtk – name of output VTK file, where each vertex is assigned the corresponding shape value
Return type:	string

Examples

>>> from mindboggle.mio.vtks import freesurfer_curvature_to_vtk
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> surface_file = fetch_data(urls['left_freesurfer_thickness'], '', '.vtk')
>>> vtk_file = fetch_data(urls['left_pial'], '', '.vtk')
>>> output_vtk = 'freesurfer_curvature_to_vtk.vtk'
>>> background_value = -1
>>> output_vtk = freesurfer_curvature_to_vtk(surface_file, vtk_file,
...                                          output_vtk, background_value)

View output vtk file (skip test):

>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(output_vtk) # doctest: +SKIP

freesurfer_surface_to_vtk(surface_file, orig_file='', output_vtk='')¶

Convert FreeSurfer surface file to VTK format.

If a file named orig.mgz exists in ‘../mri’, the surface coordinates are transformed into scanner RAS space during format conversion according to the vox2ras transform in that file.

Parameters:	surface_file (string) – name of FreeSurfer surface file orig_file (string) – name of FreeSurfer mri/orig.mgz file output_vtk (string) – name of output VTK file; if blank, appends “.vtk” to surface_file and saves to the current working directory.
Returns:	output_vtk – name of output VTK file
Return type:	string

Examples

>>> import os
>>> from mindboggle.mio.vtks import freesurfer_surface_to_vtk
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> surface_file = fetch_data(urls['left_freesurfer_pial'], '', '.pial')
>>> urls, fetch_data = prep_tests()
>>> orig_file = fetch_data(urls['freesurfer_orig_mgz'], '', '.mgz')
>>> output_vtk = 'freesurfer_surface_to_vtk.vtk'
>>> os.rename(surface_file, surface_file + '.pial')
>>> os.rename(orig_file, orig_file + '.mgz')
>>> surface_file = surface_file + '.pial'
>>> orig_file = orig_file + '.mgz'
>>> output_vtk = freesurfer_surface_to_vtk(surface_file, orig_file,
...                                        output_vtk)

View output vtk file (skip test):

>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces(output_vtk) # doctest: +SKIP

read_faces_points(filename)¶

Load points and faces of a VTK surface file.

Parameters:	filename (string) – path/filename of a VTK format file
Returns:	faces (list of lists of integers) – each element is list of 3 indices of vertices that form a face on a surface mesh points (list of lists of floats) – each element is a list of 3-D coordinates of a surface mesh vertex npoints (integer) – number of points

Examples

>>> import numpy as np
>>> from mindboggle.mio.vtks import read_faces_points
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> faces, points, npoints  = read_faces_points(depth_file)
>>> npoints
145069
>>> faces[0:5]
[[0, 1, 4], [5, 4, 1], [0, 48, 49], [0, 49, 1], [0, 4, 48]]
>>> print(np.array_str(np.array(points[0:5]),
...       precision=5, suppress_small=True))
[[-13.7924  -76.0973   -2.57594]
 [-14.2225  -76.2362   -2.73425]
 [-14.9617  -76.2497   -2.62924]
 [-12.4807  -76.1401   -3.98634]
 [-13.3426  -76.1914   -3.3657 ]]

read_lines(filename)¶

Load LINES from a VTK file, along with the scalar values.

The line that extracts vertices from a VTK iterates from 1 to Vrts.GetSize(), rather than from 0.

Not currently in use by Mindboggle. This was intended for use with the fundus line extraction algorithm.

Parameters:	filename (string) – the path/filename of a VTK format file
Returns:	lines (list of 2-tuple of integers) – each element is a 2-tuple of IDs (i.e., indices) of two points defined in the POINTS section of the VTK file scalars (list of floats) – each element is a scalar value corresponding to a vertex

Examples

>>> from mindboggle.mio.vtks import read_lines
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> fundus_file = fetch_data(urls['left_fundus_per_fold'], '', '.vtk')
>>> lines, scalars  = read_lines(fundus_file) # doctest: +SKIP

read_points(filename)¶

Load points of a VTK surface file.

Not currently in use by Mindboggle.

Parameters:	filename (string) – path/filename of a VTK format file
Returns:	points – each element is a list of 3-D coordinates of a surface mesh vertex
Return type:	list of lists of floats

Examples

>>> import numpy as np
>>> from mindboggle.mio.vtks import read_points
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> points  = read_points(depth_file)
>>> print(np.array_str(np.array(points[0:5]),
...       precision=5, suppress_small=True))
[[-13.7924  -76.0973   -2.57594]
 [-14.2225  -76.2362   -2.73425]
 [-14.9617  -76.2497   -2.62924]
 [-12.4807  -76.1401   -3.98634]
 [-13.3426  -76.1914   -3.3657 ]]

read_scalars(filename, return_first=True, return_array=False)¶

Load all scalar lookup tables from a VTK file.

Parameters:

filename (string) – The path/filename of a VTK format file.
return_first (bool) – Return only the first list of scalar values?
return_array (bool (only if return_first)) – Return first list of scalars as a numpy array?

Returns:

scalars (list of lists of integers or floats) – each element is a list of scalar values for the vertices of a mesh
scalar_name(s) (list of strings) – each element is the name of a lookup table

Examples

>>> import numpy as np
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> depths, name = read_scalars(depth_file)
>>> name
'scalars'
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in depths[0:5]]
[0.02026, 0.06009, 0.12859, 0.04564, 0.00774]

read_vertices(filename)¶

Load VERTICES segment from a VTK file (actually indices to vertices).

Parameters: filename (string) – The path/filename of a VTK format file.

Returns:

indices (a list of integers) – Each element is an integer defined in the VTK file’s VERTICES segment. The integer is an index referring to a point defined in the POINTS segment of the VTK file.
Notes :: – – Not currently in use by Mindboggle.
We assume that the VERTICES segment is organized as one line, the first column of which is the number of vertices. Vertices here are as vertices in VTK terminology. It may not be the vertices in your 3-D surface.

Examples

>>> from mindboggle.mio.vtks import read_vertices
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> indices = read_vertices(depth_file) # doctest: +SKIP

read_vtk(input_vtk, return_first=True, return_array=False)¶

Load faces, lines, indices, points, #points, and all scalar lookup tables from a VTK file.

Note

1. This supports copying lines, vertices (indices of points),
   and triangular faces from one surface to another.
2. We assume that all vertices are written in one line
   in the VERTICES segment.

Parameters:

input_vtk (string) – path/filename of a VTK format file
return_first (bool) – Return only the first list of scalar values?
return_array (bool (only if return_first)) – Return first list of scalars as a numpy array?

Returns:

points (list of 3-tuples of floats) – each element has 3 numbers representing the coordinates of the points
indices (list of integers) – indices of vertices
lines (list of 2-tuples of integers) – each element is an edge on the mesh, consisting of 2 integers representing the 2 vertices of the edge
faces (list of lists of integers) – each element is list of 3 indices of vertices that form a face on a surface mesh
scalars (list or list of lists of floats or integers) – scalar values for the vertices of a mesh
scalar_names (string or list of strings) – name(s) of lookup table(s)
npoints (int) – number of vertices in the mesh
input_vtk (string) – path/filename of the input VTK format file

Examples

>>> import numpy as np
>>> from mindboggle.mio.vtks import read_vtk
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> points, indices, lines, faces, scalars, scalar_names, npoints, input_vtk = read_vtk(depth_file)
>>> npoints
145069
>>> print(np.array_str(np.array(points[0:5]),
...       precision=5, suppress_small=True))
[[-13.7924  -76.0973   -2.57594]
 [-14.2225  -76.2362   -2.73425]
 [-14.9617  -76.2497   -2.62924]
 [-12.4807  -76.1401   -3.98634]
 [-13.3426  -76.1914   -3.3657 ]]
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in scalars[0:5]]
[0.02026, 0.06009, 0.12859, 0.04564, 0.00774]
>>> faces[0:5]
[[0, 1, 4], [5, 4, 1], [0, 48, 49], [0, 49, 1], [0, 4, 48]]

rewrite_scalars(input_vtk, output_vtk, new_scalars, new_scalar_names=['scalars'], filter_scalars=[], background_value=-1)¶

Load VTK format file and save a subset of scalars into a new file.

Parameters:

input_vtk (string) – input VTK file name
output_vtk (string) – output VTK file name
new_scalars (list of lists (or single list or numpy array) of N floats) – each list contains new values to assign to the vertices
new_scalar_names (string or list of strings) – each element is the new name for the corresponding scalars
filter_scalars (list or numpy array of N elements (optional)) – scalar values used to filter faces (foreground values retained)
background_value (integer) – background value

Examples

>>> # Write vtk file with depth values on sulci
>>> from mindboggle.mio.vtks import rewrite_scalars
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_vtk = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> sulci_file = fetch_data(urls['left_sulci'], '', '.vtk')
>>> output_vtk = 'rewrite_scalars.vtk'
>>> curvs, name = read_scalars(input_vtk, True,True)
>>> sulci, name = read_scalars(sulci_file)
>>> new_scalars = [curvs, sulci]
>>> new_scalar_names = ['curvs', 'sulci']
>>> filter_scalars = sulci
>>> background_value = -1
>>> rewrite_scalars(input_vtk, output_vtk, new_scalars,
...     new_scalar_names, filter_scalars, background_value)

View resulting vtk file (skip test):

>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(output_vtk) # doctest: +SKIP

scalars_checker(scalars, scalar_names)¶

Check whether input scalars and scalar_names are in acceptable format. If not, reformat.

Parameters:

scalars (list of lists of floats (or single list or 1-/2-D array)) –
scalar_names (string or list of strings) –

Returns:

scalars (list of lists of floats or integers)
scalar_names (list of strings)

Examples

>>> import numpy as np
>>> from mindboggle.mio.vtks import scalars_checker
>>> scalars_checker([[1,2],[3,4]], ["list1", "list2"])
([[1, 2], [3, 4]], ['list1', 'list2'])
>>> scalars_checker([[1,2],[3,4]], "")
([[1, 2], [3, 4]], ['', ''])
>>> scalars_checker([[1,2],[3,4]], ["list1", "list2", "list3"])
([[1, 2], [3, 4]], ['list1', 'list2', 'list3'])
>>> scalars_checker([[1,2],[3,4]], "list1")
([[1, 2], [3, 4]], ['list1', 'list1'])
>>> scalars_checker([1,2,3,4], ["123"])
([[1, 2, 3, 4]], ['123'])
>>> scalars_checker(1, ["123"]) # doctest: +SKIP
Error: scalars is neither a list nor a numpy array. # doctest: +SKIP
>>> scalars_checker(np.array([1,2,3]), ["123"])
([[1, 2, 3]], ['123'])
>>> scalars_checker(np.array([[1,2,3]]), ["123"])
([[1, 2, 3]], ['123'])
>>> scalars_checker(np.array([[1,2,3],[4,5,6]]), ["123"])
([[1, 2, 3], [4, 5, 6]], ['123', '123'])
>>> scalars_checker(np.array([[[1,2,3]]]), ["123"]) # doctest: +SKIP
Error: Dimension of new_scalars is too high. # doctest: +SKIP
>>> scalars_checker(np.array([np.array([0,7,9]),[1,2,3]]), ["123"])
([[0, 7, 9], [1, 2, 3]], ['123', '123'])

Notes

This function does not check all possible cases of scalars and scalar_names, but only those that are likely to occur when using Mindboggle.

write_faces(Fp, faces)¶: Write indices to vertices forming triangular meshes or lines, the POLYGONS section in DATASET POLYDATA section:

POLYGONS 301978 1207912 3 0 1 4 …

write_header(Fp, Header='# vtk DataFile Version 2.0', Title='Generated by Mindboggle (www.mindboggle.info)', fileType='ASCII', dataType='POLYDATA')¶

Write header information for a VTK-format file:

# vtk DataFile Version 2.0
Generated by Mindboggle (www.mindboggle.info)
ASCII
DATASET POLYDATA

This part matches three things in the VTK 4.2 File Formats doc:

Part 1: Header
Part 2: Title (256 characters maximum, ending with newline character)
Part 3: Data type, either ASCII or BINARY
Part 4: Geometry/topology. dataType is one of:
- STRUCTURED_POINTS
- STRUCTURED_GRID
- UNSTRUCTURED_GRID
- POLYDATA
- RECTILINEAR_GRID
- FIELD

write_lines(Fp, lines)¶

Save connected line segments to a VTK file.

Parameters:	Fp (pointer to a file) – pointer to the file to write lines lines (list of 2-tuples of integers) – each element is an edge on the mesh, consisting of 2 integers representing the 2 vertices of the edge

write_points(Fp, points, dataType='float')¶

Write coordinates of points, the POINTS section in DATASET POLYDATA:

POINTS 150991 float
-7.62268877029 -81.2403945923 -1.44539153576
...

Indices are 0-offset. Thus the first point is point id 0:

POINTS n dataType
p0x p0y p0z
...
p(n-1)x p(n-1)y p(n-1)z

write_scalars(Fp, scalars, scalar_name, begin_scalars=True, scalar_type='float')¶

Write per-VERTEX values as a scalar lookup table into a VTK file:

POINT_DATA 150991
SCALARS Max_(majority_labels) int
LOOKUP_TABLE default
11
11
11
11
.
.
.

Parameters:	Fp (string) – name of VTK surface mesh file scalars (list of integers or floats) – scalar values, one per vertex of mesh scalar_name (string) – name for scalars (use unbroken string) begin_scalars (bool) – True if the first vertex lookup table in a VTK file scalar_type (string) – type of scalars (‘float’ or ‘int’)

write_vertices(Fp, indices)¶

Write indices to vertices, the VERTICES section in the DATASET POLYDATA section:

VERTICES 140200 420600
3 130239 2779 10523
...

Indices are 0-offset. Thus the first point is point id 0:

VERTICES n size
numPoints0 i0 j0 k0
...
numPoints_[n-1] i_[n-1] j_[n-1] k_[n-1]

Note:

Currently we write all vertices in one line.

write_vtk(output_vtk, points, indices=[], lines=[], faces=[], scalars=[], scalar_names=['scalars'], scalar_type='float')¶

Save lists of scalars into the lookup table of a VTK-format file.

Scalar definition includes specification of a lookup table. The definition of a lookup table is optional. If not specified, the default VTK table will be used (and tableName should be “default”).

SCALARS dataName dataType numComp LOOKUP_TABLE tableName

Parameters:

output_vtk (string) – path of the output VTK file
points (list of 3-tuples of floats) – each element has 3 numbers representing the coordinates of the points
indices (list of integers) – indices of vertices
lines (list of 2-tuples of integers) – Each element is an edge on the mesh, consisting of 2 integers representing the 2 vertices of the edge
faces (list of 3-tuples of integers) – indices to the three vertices of a face on the mesh
scalars (list of floats, or list of lists of floats;) – each list (lookup table) contains values assigned to the vertices
scalar_names (string or list of strings) – each element is the name of a scalar list (lookup table)
scalar_type (string) – type of scalars (‘float’ or ‘int’)

Examples

>>> # Toy example
>>> import random, os
>>> from mindboggle.mio.vtks import write_vtk
>>> points = [[random.random() for i in [1,2,3]] for j in range(4)]
>>> indices = [1,2,3,0]
>>> lines = [[1,2],[3,4]]
>>> faces = [[1,2,3],[0,1,3]]
>>> #scalars = [[random.random() for i in range(4)] for j in [1,2]]
>>> scalars = [[1,3,5,7],[2,4,6,8]]
>>> scalar_names = ['curv','depth']
>>> output_vtk = 'write_vtk_toy.vtk'
>>> scalar_type = 'float'
>>> write_vtk(output_vtk, points, indices, lines, faces, scalars,
...           scalar_names, scalar_type) # doctest: +SKIP

View resulting vtk file (skip test):

>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces(output_vtk) # doctest: +SKIP

Write vtk file with depth values and view (skip plot in test):

>>> from mindboggle.mio.vtks import read_vtk
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_vtk = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> points, indices, lines, faces, scalars, scalar_names, npoints, input_vtk = read_vtk(input_vtk)
>>> output_vtk = 'write_vtk.vtk'
>>> scalar_type = 'float'
>>> write_vtk(output_vtk, points, indices, lines, faces, scalars,
...           scalar_names, scalar_type)

View resulting vtk file (skip test):

>>> plot_surfaces(output_vtk) # doctest: +SKIP

mindboggle.mio package¶

Submodules¶

mindboggle.mio.colors module¶

mindboggle.mio.convert_volumes module¶

mindboggle.mio.fetch_data module¶

mindboggle.mio.labels module¶

Numbers, names, and colormaps for the DKT and FreeSurfer labeling protocols¶

mindboggle.mio.plots module¶

mindboggle.mio.tables module¶

mindboggle.mio.vtks module¶

Module contents¶

Mindboggle

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