  We explore the main characteristics of big brain network data that offer
unique statistical challenges. The brain networks are biologically expected to
be both sparse and hierarchical. Such unique characterizations put specific
topological constraints onto statistical approaches and models we can use
effectively. We explore the limitations of the current models used in the field
and offer alternative approaches and explain new challenges.
