Connectivity Matrix
Chart overview
A connectivity matrix displays pairwise values - such as functional correlation, structural tract density, or effective connectivity - between all nodes in a network as a colour-coded square matrix.
Key points
- Neuroimaging researchers use it to characterise whole-brain functional networks from fMRI or structural networks from diffusion MRI tractography.
- The matrix reveals modular community structure, hubs, and inter-network coupling that underlie cognition.
Python Tutorial
How to create a connectivity matrix in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
How to Create a Heatmap in PythonExample Visualization
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"Create a brain connectivity matrix from my data. Display pairwise values as a symmetric square heatmap, cluster regions by network, annotate significance with asterisks, use a diverging colormap centred at zero, and format for a journal-quality neuroimaging figure."
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Python Code Example
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
np.random.seed(42)
matrix = np.random.rand(15, 15)
matrix = (matrix + matrix.T) / 2
np.fill_diagonal(matrix, 1)
plt.figure(figsize=(8, 7))
sns.heatmap(matrix, cmap='viridis', vmin=0, vmax=1, square=True)
plt.title('Functional Connectivity Matrix', fontsize=14, fontweight='bold', pad=20)
plt.xlabel('Region', fontsize=12)
plt.ylabel('Region', fontsize=12)
plt.tight_layout()
plt.savefig('plotivy-connectivity-matrix.png', dpi=150)
print("Connectivity matrix generated successfully.")
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Console Output
Connectivity matrix generated successfully.
Common Use Cases
- 1Visualising resting-state functional connectivity from fMRI BOLD signals
- 2Displaying structural white-matter connectivity from tractography streamline counts
- 3Comparing connectivity matrices between patient and control groups
- 4Identifying large-scale network modules such as default mode and sensorimotor networks
Pro Tips
Sort rows and columns by network affiliation to reveal modular block structure
Use a diverging colormap (e.g. RdBu_r) centred at zero for correlation data
Mask the diagonal and optionally the lower triangle to reduce redundancy
Annotate statistically significant connections with an asterisk overlay
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for connectivity-matrix.
numpy
Useful in specialized workflows that complement core Python plotting libraries for connectivity-matrix analysis tasks.
seaborn
Fastest path to statistically-aware defaults and tidy-data workflows, especially for grouped and distribution-focused connectivity-matrix views.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.