<｜begin▁of▁sentence｜>### Instruction:
As a biochemist studying the complex interplay of neural structures, let's consider the intricate network within the Drosophila larva's mushroom body. Through the use of the `graspologic` dataset, we can access the connectome data specific to the right hemisphere by utilizing the `load_drosophila_right` API function. Our objective is to translate the neuronal adjacency matrix this function provides us into a comprehensive NetworkX graph. This will allow us to not only visualize the connections but also to meticulously annotate each neuron with its corresponding label, integrating these labels directly into the node attributes. As an initial step in unraveling this data, we should extract information about the overall structure of the NetworkX graph. Furthermore, to get a preliminary yet insightful glimpse into our dataset, let's display the labels associated with the first quintet of nodes. How would we proceed to achieve these tasks while maintaining the integrity of the information presented by the connectome data?
### Response:
import networkx as nx
from graspologic.datasets import load_drosophila_right

# Load the right Drosophila brain connectome
adjacency_matrix, labels = load_drosophila_right(return_labels=True)

# Create a NetworkX graph from the numpy adjacency matrix
G = nx.from_numpy_array(adjacency_matrix)

# Print information about the graph
print('Number of nodes:', G.number_of_nodes())
print('Number of edges:', G.number_of_edges())

# Optionally, you can add labels to the nodes:
label_dict = {idx: label for idx, label in enumerate(labels)}
nx.set_node_attributes(G, label_dict, 'label')

# Display the labels for the first five nodes
for node in G.nodes(data=True)[:5]:
    print(node)
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