plotting connectomes¶

in this tutorial, you will see how to use yabplot to visualise connectomes i.e. graphs consisting of nodes and edges from data matrices.

why plot connectomes? often when you would like to visualise the relationship between different brain regions, then single regional data is not enough. the field of connectomics often studies relationships between brain regions and often visualise the brain as a vast graph where regions are shown as nodes and their relationships as edges.

inputs and outputs¶

1. data matrix (numpy.ndarray or pandas.DataFrame): an n x n shaped matrix.

finally, we can pass this matrix into plot_connectome to visualize the matrix in a 3D rendered scene as a graph.

In [1]:

import pandas as pd
import yabplot as yab
import matplotlib.pyplot as plt

data = pd.read_csv('/Users/to8050an/Developer/yabplot_miscfiles/test_data/hcp_connectivity/funcMatrix_ctx_schaefer_400.csv', header=None).to_numpy()
labels = list(pd.read_csv('/Users/to8050an/Developer/yabplot_miscfiles/test_data/hcp_connectivity/funcLabels_ctx_schaefer_400.csv').columns)
df = pd.DataFrame(data, index=labels, columns=labels)
df

import pandas as pd import yabplot as yab import matplotlib.pyplot as plt data = pd.read_csv('/Users/to8050an/Developer/yabplot_miscfiles/test_data/hcp_connectivity/funcMatrix_ctx_schaefer_400.csv', header=None).to_numpy() labels = list(pd.read_csv('/Users/to8050an/Developer/yabplot_miscfiles/test_data/hcp_connectivity/funcLabels_ctx_schaefer_400.csv').columns) df = pd.DataFrame(data, index=labels, columns=labels) df

Out[1]:

	7Networks_LH_Vis_1	7Networks_LH_Vis_2	7Networks_LH_Vis_3	7Networks_LH_Vis_4	7Networks_LH_Vis_5	7Networks_LH_Vis_6	7Networks_LH_Vis_7	7Networks_LH_Vis_8	7Networks_LH_Vis_9	7Networks_LH_Vis_10	...	7Networks_RH_Default_PFCdPFCm_13	7Networks_RH_Default_pCunPCC_1	7Networks_RH_Default_pCunPCC_2	7Networks_RH_Default_pCunPCC_3	7Networks_RH_Default_pCunPCC_4	7Networks_RH_Default_pCunPCC_5	7Networks_RH_Default_pCunPCC_6	7Networks_RH_Default_pCunPCC_7	7Networks_RH_Default_pCunPCC_8	7Networks_RH_Default_pCunPCC_9
7Networks_LH_Vis_1	0.000000	0.248430	0.383680	0.297230	0.301150	0.30694	0.124500	0.365270	0.261410	0.218580	...	0.062431	0.18768	0.046968	0.16267	0.100340	0.12387	0.100360	0.041825	0.086295	0.15960
7Networks_LH_Vis_2	0.248430	0.000000	0.184670	0.256660	0.184390	0.15954	0.206150	0.172160	0.175490	0.157310	...	0.026568	0.36570	0.071601	0.27566	0.185050	0.20589	0.172900	0.050208	0.125210	0.21362
7Networks_LH_Vis_3	0.383680	0.184670	0.000000	0.470760	0.602060	0.63561	0.141000	0.664050	0.488640	0.390000	...	0.125560	0.17417	0.058647	0.17260	0.094387	0.11809	0.091927	0.079465	0.102520	0.20023
7Networks_LH_Vis_4	0.297230	0.256660	0.470760	0.000000	0.624720	0.40105	0.212380	0.430560	0.407430	0.543970	...	0.105960	0.24534	0.068197	0.24113	0.071932	0.10353	0.081813	0.068869	0.077668	0.23314
7Networks_LH_Vis_5	0.301150	0.184390	0.602060	0.624720	0.000000	0.60393	0.154300	0.559400	0.674580	0.591350	...	0.119660	0.20623	0.066630	0.20999	0.075629	0.10575	0.081727	0.077694	0.081771	0.21635
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7Networks_RH_Default_pCunPCC_5	0.123870	0.205890	0.118090	0.103530	0.105750	0.14404	0.142250	0.104530	0.145230	0.104330	...	0.143020	0.45636	0.188770	0.48416	0.672120	0.00000	0.400730	0.217680	0.529040	0.28717
7Networks_RH_Default_pCunPCC_6	0.100360	0.172900	0.091927	0.081813	0.081727	0.13190	0.116020	0.071925	0.132100	0.065465	...	0.090063	0.34082	0.188370	0.36454	0.407170	0.40073	0.000000	0.267580	0.401820	0.28982
7Networks_RH_Default_pCunPCC_7	0.041825	0.050208	0.079465	0.068869	0.077694	0.11807	0.061815	0.072621	0.098202	0.067691	...	0.179760	0.13496	0.153040	0.22489	0.223450	0.21768	0.267580	0.000000	0.269030	0.17599
7Networks_RH_Default_pCunPCC_8	0.086295	0.125210	0.102520	0.077668	0.081771	0.14174	0.104180	0.094047	0.116840	0.070157	...	0.226070	0.29112	0.188120	0.37039	0.517750	0.52904	0.401820	0.269030	0.000000	0.34648
7Networks_RH_Default_pCunPCC_9	0.159600	0.213620	0.200230	0.233140	0.216350	0.21991	0.157280	0.182150	0.221640	0.210260	...	0.166240	0.36226	0.147520	0.40675	0.229300	0.28717	0.289820	0.175990	0.346480	0.00000

400 rows × 400 columns

1. plot any subcortical or cortical atlas as a connectome¶

In [2]:

# plotting a an atlas without data
ax = yab.plot_connectome(
    atlas='aseg', # you can use all cortical and subcortical atlases
    views=['superior', 'posterior', 'left_lateral']
)

# plotting a an atlas without data ax = yab.plot_connectome( atlas='aseg', # you can use all cortical and subcortical atlases views=['superior', 'posterior', 'left_lateral'] )

2. add matrix data corresponding to atlas¶

In [3]:

ax = yab.plot_connectome(
    matrix=df, # now lets add some data
    atlas='schaefer400',
    edge_cmap='dense',
    views=['superior', 'posterior', 'left_lateral']
)

ax = yab.plot_connectome( matrix=df, # now lets add some data atlas='schaefer400', edge_cmap='dense', views=['superior', 'posterior', 'left_lateral'] )

3. many parameters to edit the visualised connectome¶

In [4]:

fig, axes = plt.subplots(2, 1, figsize=(8, 5), dpi=200)

# you can change a set of parameters, see below
axes[0] = yab.plot_connectome(
    matrix=df,
    ax=axes[0],
    atlas='schaefer400',
    edge_threshold='90%', 
    edge_alpha=0,
    node_cmap='viridis',
    views=['superior', 'posterior', 'left_lateral'],
)

axes[1] = yab.plot_connectome(
    matrix=df,
    ax=axes[1],
    atlas='schaefer400',
    edge_threshold='99%',
    edge_alpha=1,
    edge_thickness='weight',
    edge_cmap='Reds',
    edge_curve=0.3,
    node_size='strength',
    node_color='strength',
    node_cmap='Reds',
    views=['superior', 'posterior', 'left_lateral'],
    directed=False
)

fig, axes = plt.subplots(2, 1, figsize=(8, 5), dpi=200) # you can change a set of parameters, see below axes[0] = yab.plot_connectome( matrix=df, ax=axes[0], atlas='schaefer400', edge_threshold='90%', edge_alpha=0, node_cmap='viridis', views=['superior', 'posterior', 'left_lateral'], ) axes[1] = yab.plot_connectome( matrix=df, ax=axes[1], atlas='schaefer400', edge_threshold='99%', edge_alpha=1, edge_thickness='weight', edge_cmap='Reds', edge_curve=0.3, node_size='strength', node_color='strength', node_cmap='Reds', views=['superior', 'posterior', 'left_lateral'], directed=False )

In [ ]: