Usage (Python)

In [1]:

import pandas as pd
import numpy as np
import random
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors

from subcortex_visualization.plotting import plot_subcortical_data

import pandas as pd import numpy as np import random import matplotlib import matplotlib.pyplot as plt import matplotlib.colors as mcolors from subcortex_visualization.plotting import plot_subcortical_data

Example data just to show the different regions¶

First, let's create a simple dataframe with the left hemisphere cortical regions, assigning a different value to each region for visual clarity. Note that all data passed to plot_subcortical_data needs to follow the same three-column structure as shown below: region (the name of the subcortical region with the same nomenclature as shown), value (the value to be plotted in the subcortex map), and Hemisphere (either 'L', 'R', or 'both').

In [2]:

example_subcortex_data = pd.DataFrame({"region": ["accumbens", "amygdala", "caudate", "hippocampus", "pallidum", "putamen", "thalamus"], 
                                       "value": range(7)}).assign(Hemisphere = "L")

example_subcortex_data

example_subcortex_data = pd.DataFrame({"region": ["accumbens", "amygdala", "caudate", "hippocampus", "pallidum", "putamen", "thalamus"], "value": range(7)}).assign(Hemisphere = "L") example_subcortex_data

Out[2]:

	region	value	Hemisphere
0	accumbens	0	L
1	amygdala	1	L
2	caudate	2	L
3	hippocampus	3	L
4	pallidum	4	L
5	putamen	5	L
6	thalamus	6	L

Now, we can plot this data with the plasma colormap as an example in the left cortex. Note the following arguments:

• subcortex_data: The three-column dataframe shown above
• atlas: The name of the atlas to plot (currently, only 'aseg' is supported)
• line_thickness: How thick the lines around each subcortical region should be drawn, in mm (default is 1.5)
• line_color: What color the lines around each subcortical region should be (default is 'black')
• hemisphere: Which hemisphere ('L' or 'R') the subcortex_data is from; can also be 'both' (default is 'L')
• fill_title: Name to add to legend
• cmap: Name of colormap (e.g., 'plasma' or 'viridis') or a matplotlib.colors.Colormap (default is 'viridis')
• vmin: Min fill value; this is optional, and you would only want to use this to manually constrain the fill range to match another figure
• vmax: Max fill value; this is optional, and you would only want to use this to manually constrain the fill range to match another figure

In [3]:

plot_subcortical_data(subcortex_data=example_subcortex_data, atlas = 'aseg',
                      line_thickness=1.5, line_color='black',
                          hemisphere='L', fill_title = "aseg region index", cmap='plasma', 
                          vmin=None, vmax=None)

plot_subcortical_data(subcortex_data=example_subcortex_data, atlas = 'aseg', line_thickness=1.5, line_color='black', hemisphere='L', fill_title = "aseg region index", cmap='plasma', vmin=None, vmax=None)

If we wanted to plot this with the inferno color palette instead, just swap out the cmap argument values:

In [4]:

plot_subcortical_data(subcortex_data=example_subcortex_data, atlas = 'aseg', 
                      hemisphere='L', fill_title = "aseg region index", cmap='inferno')

plot_subcortical_data(subcortex_data=example_subcortex_data, atlas = 'aseg', hemisphere='L', fill_title = "aseg region index", cmap='inferno')

By default, plot_subcortical_data will plot the index values of each region, so we actually don't need to pass in a dataframe for this visualization purpose. Here, we can plot the region indices for the Melbourne Subcortex S1 atlas in right hemisphere without passing in any data:

In [5]:

plot_subcortical_data(atlas = 'Melbourne_S1', hemisphere='R', 
                      fill_title = "Melbourne Subcortex S1 atlas region", 
                      cmap='viridis')

plot_subcortical_data(atlas = 'Melbourne_S1', hemisphere='R', fill_title = "Melbourne Subcortex S1 atlas region", cmap='viridis')

To plot both hemispheres using gray lines of 2mm thickness, we can set the hemisphere argument to 'both' and adjust the line_thickness and line_color arguments accordingly:

In [6]:

plot_subcortical_data(atlas = 'Melbourne_S1', hemisphere='both', 
                      line_color='gray', line_thickness=2,
                      fill_title = "Melbourne Subcortex S1 atlas region", cmap='viridis')

plot_subcortical_data(atlas = 'Melbourne_S1', hemisphere='both', line_color='gray', line_thickness=2, fill_title = "Melbourne Subcortex S1 atlas region", cmap='viridis')

We can also use the S2 level of granularity from the Melbourne Subcortex (Tian 2020) Atlas:

In [7]:

plot_subcortical_data(atlas = 'Melbourne_S2', hemisphere='both', 
                      fill_title = "Melbourne Subcortex S2 atlas", cmap='hsv')

plot_subcortical_data(atlas = 'Melbourne_S2', hemisphere='both', fill_title = "Melbourne Subcortex S2 atlas", cmap='hsv')

Lastly, let's view the atlas for (1) AICHA subcortex; (2) Brainnetome subcortex; and (3) SUIT cerebellum:

In [8]:

# AICHA
plot_subcortical_data(atlas = 'AICHA', hemisphere='both', 
                      fill_title = "AICHA subcortex atlas", cmap='hsv')

# AICHA plot_subcortical_data(atlas = 'AICHA', hemisphere='both', fill_title = "AICHA subcortex atlas", cmap='hsv')

In [9]:

# Brainnetome
plot_subcortical_data(atlas = 'Brainnetome', hemisphere='both', 
                      fill_title = "Brainnetome subcortex atlas", cmap='hsv')

# Brainnetome plot_subcortical_data(atlas = 'Brainnetome', hemisphere='both', fill_title = "Brainnetome subcortex atlas", cmap='hsv')

In [10]:

# SUIT cerebellum
plot_subcortical_data(atlas = 'SUIT', hemisphere='both', 
                      fill_title = "SUIT cerebellum atlas", cmap='hsv')

# SUIT cerebellum plot_subcortical_data(atlas = 'SUIT', hemisphere='both', fill_title = "SUIT cerebellum atlas", cmap='hsv')

Let's simulate and visualize some continuous data¶

In [11]:

np.random.seed(127)

example_continuous_data_L = pd.DataFrame({"region": ["accumbens", "amygdala", "caudate", "hippocampus", "pallidum", "putamen", "thalamus"],
                                          "value": np.random.normal(0, 1, 7)}).assign(Hemisphere = "L")
example_continuous_data_R = pd.DataFrame({"region": ["accumbens", "amygdala", "caudate", "hippocampus", "pallidum", "putamen", "thalamus"],
                                            "value": np.random.normal(0, 1, 7)}).assign(Hemisphere = "R")
example_continuous_data = pd.concat([example_continuous_data_L, example_continuous_data_R], axis=0)

# See what the left hemisphere data, randomly sampled from a normal distribution, looks like
example_continuous_data_L

np.random.seed(127) example_continuous_data_L = pd.DataFrame({"region": ["accumbens", "amygdala", "caudate", "hippocampus", "pallidum", "putamen", "thalamus"], "value": np.random.normal(0, 1, 7)}).assign(Hemisphere = "L") example_continuous_data_R = pd.DataFrame({"region": ["accumbens", "amygdala", "caudate", "hippocampus", "pallidum", "putamen", "thalamus"], "value": np.random.normal(0, 1, 7)}).assign(Hemisphere = "R") example_continuous_data = pd.concat([example_continuous_data_L, example_continuous_data_R], axis=0) # See what the left hemisphere data, randomly sampled from a normal distribution, looks like example_continuous_data_L

Out[11]:

	region	value	Hemisphere
0	accumbens	-0.571809	L
1	amygdala	0.029624	L
2	caudate	0.562592	L
3	hippocampus	-0.647652	L
4	pallidum	-0.845436	L
5	putamen	0.106565	L
6	thalamus	1.732392	L

Left hemisphere:

In [12]:

plot_subcortical_data(subcortex_data=example_continuous_data_L, atlas = 'aseg',
                      hemisphere='L', fill_title = "Normal distribution sample, aseg atlas", 
                      cmap='viridis')

plot_subcortical_data(subcortex_data=example_continuous_data_L, atlas = 'aseg', hemisphere='L', fill_title = "Normal distribution sample, aseg atlas", cmap='viridis')

Right hemisphere:

In [13]:

plot_subcortical_data(subcortex_data=example_continuous_data_R, atlas = 'aseg',
                      hemisphere='R', fill_title = "Normal distribution sample, aseg atlas", 
                      cmap='viridis')

plot_subcortical_data(subcortex_data=example_continuous_data_R, atlas = 'aseg', hemisphere='R', fill_title = "Normal distribution sample, aseg atlas", cmap='viridis')

Both hemispheres together:

In [14]:

plot_subcortical_data(subcortex_data=example_continuous_data, atlas = 'aseg', 
                      hemisphere='both', fill_title = "Normal distribution sample, aseg atlas", 
                      cmap='viridis')

plot_subcortical_data(subcortex_data=example_continuous_data, atlas = 'aseg', hemisphere='both', fill_title = "Normal distribution sample, aseg atlas", cmap='viridis')

You can pass in a custom colormap too! For example, if you want to fill in with a gradient ranging from white to red:

In [15]:

white_to_purple_cmap = mcolors.LinearSegmentedColormap.from_list("white_to_purple", ["white", "purple"])

plot_subcortical_data(subcortex_data=example_continuous_data, atlas = 'aseg', 
                      hemisphere='both', fill_title = "Normal distribution sample", 
                      cmap=white_to_purple_cmap)

white_to_purple_cmap = mcolors.LinearSegmentedColormap.from_list("white_to_purple", ["white", "purple"]) plot_subcortical_data(subcortex_data=example_continuous_data, atlas = 'aseg', hemisphere='both', fill_title = "Normal distribution sample", cmap=white_to_purple_cmap)

Since this data has positive and negative values, we can pass in a color palette from blue (negative) to white (0) to red (positive). plot_subcortical_data takes an argument midpoint that specifies the center point for the color palette. Setting this to 0 here enforces the center value to be white. Without setting vmin/vmax explicitly, the color range will be defined symmetrically around midpoint to capture the full range of the data.

In [16]:

white_blue_red_cmap = mcolors.LinearSegmentedColormap.from_list("BlueWhiteRed", ["blue", "white", "red"])

plot_subcortical_data(subcortex_data=example_continuous_data, atlas = 'aseg',  
                      hemisphere='both', fill_title = "Normal distribution sample", 
                      cmap=white_blue_red_cmap, midpoint=0)

white_blue_red_cmap = mcolors.LinearSegmentedColormap.from_list("BlueWhiteRed", ["blue", "white", "red"]) plot_subcortical_data(subcortex_data=example_continuous_data, atlas = 'aseg', hemisphere='both', fill_title = "Normal distribution sample", cmap=white_blue_red_cmap, midpoint=0)