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"""
Demo script to visualize FC matrices from real fMRI data using nilearn's built-in datasets.
This script uses the consolidated visualization utilities.
"""
import numpy as np
# Configure matplotlib for headless environment
import matplotlib
matplotlib.use('Agg') # Use non-interactive backend
import matplotlib.pyplot as plt
from nilearn import datasets
from nilearn import input_data, connectome
from fc_visualization import FCVisualizer
from visualization import plot_fc_matrices
def visualize_from_nilearn_dataset():
"""Download and visualize FC matrices from nilearn's ADHD dataset."""
print("Downloading a sample fMRI dataset (ADHD)...")
adhd_dataset = datasets.fetch_adhd(n_subjects=1)
# Get the fMRI file path
func_file = adhd_dataset.func[0]
confound_file = adhd_dataset.confounds[0]
print(f"Downloaded fMRI file: {func_file}")
# Get Power atlas coordinates
power = datasets.fetch_coords_power_2011()
coords = np.vstack((power.rois['x'], power.rois['y'], power.rois['z'])).T
print(f"Using Power atlas with {len(coords)} ROIs")
# Create a masker to extract time series from the ROIs
masker = input_data.NiftiSpheresMasker(
coords,
radius=8, # 8mm radius
standardize=True,
memory='nilearn_cache',
memory_level=1,
verbose=1,
detrend=True,
low_pass=0.08,
high_pass=0.01,
t_r=2.0 # ADHD dataset TR
)
# Extract time series, including confounds
print("Extracting time series from ROIs...")
time_series = masker.fit_transform(func_file, confounds=confound_file)
print(f"Time series shape: {time_series.shape}")
# Compute correlation matrix (FC matrix)
correlation_measure = connectome.ConnectivityMeasure(
kind='correlation',
vectorize=False,
discard_diagonal=False
)
fc_matrix = correlation_measure.fit_transform([time_series])[0]
print(f"FC matrix shape: {fc_matrix.shape}")
# Save the FC matrix for future use
np.save('adhd_fc_matrix.npy', fc_matrix)
print("Saved FC matrix to adhd_fc_matrix.npy")
# Visualize using both methods for comparison
# Method 1: Using FCVisualizer (OOP approach)
visualizer = FCVisualizer(cmap='RdBu_r', vmin=-1, vmax=1)
fig1, _ = visualizer.plot_single_matrix(fc_matrix, title="ADHD FC Matrix (FCVisualizer)")
fig1.savefig('adhd_fc_matrix_method1.png', dpi=300, bbox_inches='tight')
# Method 2: Using plot_fc_matrices (functional approach)
# Create a zero matrix for the "reconstructed" and "generated" placeholders
zero_matrix = np.zeros_like(fc_matrix)
fig2 = plot_fc_matrices(fc_matrix, zero_matrix, zero_matrix)
fig2.savefig('adhd_fc_matrix_method2.png', dpi=300, bbox_inches='tight')
print("Saved visualizations using both methods")
if __name__ == "__main__":
visualize_from_nilearn_dataset() |