<｜begin▁of▁sentence｜>### Instruction:
In a study aiming to identify different brain connectivity patterns associated with two different cognitive states, a research team acquires three sets of functional MRI scans from a group of subjects. Each scan represents the brain's connectivity network, with nodes corresponding to regions of interest (ROIs) and edges corresponding to the functional connectivity between them. The scans are split into two classes based on the cognitive states. Researchers want to determine which functional connections (edges between ROIs) are the most important for differentiating the two cognitive states. They have connectivity data for 5 ROIs across 3 scans and binary labels indicating the associated cognitive state for each scan. How can the team use the SignalSubgraph estimator from graspologic to find the signal-subgraph that differentiates the two cognitive states, given a constraint on the number of edges and vertices in the subgraph?
### Response:
import numpy as np
from graspologic.subgraph import SignalSubgraph

# Generate synthetic graph data
n_vertices = 5
s_samples = 3
np.random.seed(42)
graphs = np.random.rand(n_vertices, n_vertices, s_samples)
graphs = np.triu(graphs, k=1)  # Using only the upper triangle for undirected graphs
labels = np.array([0, 1, 1])  # Binary class labels for the samples

# Instantiate and fit the SignalSubgraph estimator
sg_estimator = SignalSubgraph()
constraints = (7, 3)  # Number of edges in sig subgraph and number of vertices the subgraph must be incident to
sg_estimator.fit(graphs, labels, constraints)

# Extract the signal subgraph
sigsub = sg_estimator.fit_transform(graphs, labels, constraints)

# Print the results
print('Signal subgraph indices (row, col):', sigsub)
print('Signal subgraph mask:\n', sg_estimator.mask_)
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