Netplotbrain with spring layout subplot¶

While networks on brains are useful as they show the geographical locations of nodes, there are other useful ways to visualize networks. One such example is a spring layout visualizaiton that plots based on the weight of edges. Netplotbrain integrates with networkx to easily plot spring layout plots with all the same high-level visualizaitons.

The key argument is specifying view='s' (note lowercase).

In the below example we see view='LSs' to plot left, superior and spring-layout subplots.

Open interactive notebothh in Binder

In [1]:

# Import all necessary files
import pandas as pd
import numpy as np
import netplotbrain
import matplotlib.pyplot as plt
import templateflow.api as tf
import itertools
# Set random seed for reproducibility
np.random.seed(2022)

# Import all necessary files import pandas as pd import numpy as np import netplotbrain import matplotlib.pyplot as plt import templateflow.api as tf import itertools # Set random seed for reproducibility np.random.seed(2022)

We are going to download 100 nodes from the Schaefer atlas from templateflow.

We are then going to set the between community edges to be around 0 and the within community edges to be around 0.5.

In [2]:

atlas = {'template': 'MNI152NLin2009cAsym',
         'atlas': 'Schaefer2018',
        'desc': '100Parcels7Networks'}
atlasinfo = tf.get(extension='.tsv', **atlas)
atlasinfo = pd.read_csv(atlasinfo, sep='\t')
# Resolution argument needed in atlas later nii.gz file
atlas['resolution'] = 1
# Parse the info in to get network names
networks = list(map(lambda x: x.split('_')[2], atlasinfo.name.values))
atlasinfo['yeo7networks'] = networks
# Rename longer network names
atlasinfo['yeo7networks'].replace('DorsAttn', 'DA', inplace=True)
atlasinfo['yeo7networks'].replace('SalVentAttn', 'VA', inplace=True)
atlasinfo['yeo7networks'].replace('Default', 'DMN', inplace=True)
atlasinfo['yeo7networks'].replace('SomMot', 'SM', inplace=True)

atlas = {'template': 'MNI152NLin2009cAsym', 'atlas': 'Schaefer2018', 'desc': '100Parcels7Networks'} atlasinfo = tf.get(extension='.tsv', **atlas) atlasinfo = pd.read_csv(atlasinfo, sep='\t') # Resolution argument needed in atlas later nii.gz file atlas['resolution'] = 1 # Parse the info in to get network names networks = list(map(lambda x: x.split('_')[2], atlasinfo.name.values)) atlasinfo['yeo7networks'] = networks # Rename longer network names atlasinfo['yeo7networks'].replace('DorsAttn', 'DA', inplace=True) atlasinfo['yeo7networks'].replace('SalVentAttn', 'VA', inplace=True) atlasinfo['yeo7networks'].replace('Default', 'DMN', inplace=True) atlasinfo['yeo7networks'].replace('SomMot', 'SM', inplace=True)

In [3]:

# create empty cognitive matrix
edges = np.random.normal(0, 0.025, [100, 100])

# Set within network connectivity to be stronger
for network in atlasinfo['yeo7networks'].unique():
    idx =  atlasinfo[atlasinfo['yeo7networks']==network].index
    idx_pairs = np.array(list(itertools.combinations(idx, 2)))
    edges[idx_pairs[:, 0], idx_pairs[:, 1]] = np.random.normal(0.5, 0.025, [len(idx_pairs)])
    edges[idx_pairs[:, 1], idx_pairs[:, 0]] = np.random.normal(0.5, 0.025, [len(idx_pairs)])

# create empty cognitive matrix edges = np.random.normal(0, 0.025, [100, 100]) # Set within network connectivity to be stronger for network in atlasinfo['yeo7networks'].unique(): idx = atlasinfo[atlasinfo['yeo7networks']==network].index idx_pairs = np.array(list(itertools.combinations(idx, 2))) edges[idx_pairs[:, 0], idx_pairs[:, 1]] = np.random.normal(0.5, 0.025, [len(idx_pairs)]) edges[idx_pairs[:, 1], idx_pairs[:, 0]] = np.random.normal(0.5, 0.025, [len(idx_pairs)])

In [4]:

# Show what the connectivity matrix looks like
fig, ax = plt.subplots(1)
ax.imshow(edges)

# Show what the connectivity matrix looks like fig, ax = plt.subplots(1) ax.imshow(edges)

Out[4]:

<matplotlib.image.AxesImage at 0x7fba4e5b0fd0>

In [5]:

# Plot it all
netplotbrain.plot(template='MNI152NLin2009cAsym',
                  nodes=atlas,
                  nodes_df=atlasinfo,
                  edges=edges,
                  view='LSs',
                  template_style='glass',
                  node_scale=20,
                  node_color='yeo7networks',
                  edge_threshold=0,
                  edge_thresholddirection='>',
                  seed=2022)

# Plot it all netplotbrain.plot(template='MNI152NLin2009cAsym', nodes=atlas, nodes_df=atlasinfo, edges=edges, view='LSs', template_style='glass', node_scale=20, node_color='yeo7networks', edge_threshold=0, edge_thresholddirection='>', seed=2022)

Out[5]:

(<Figure size 648x252 with 4 Axes>,
 [<Axes3D: title={'center': 'Left'}>,
  <Axes3D: title={'center': 'Superior'}>,
  <Axes3D: >,
  <Axes: >])