Network neuroscience uses graph theory to analyze the brain’s network properties by envisioning brain regions as nodes of the graph and their functional connectivity strength with other regions as the edges. The first step in these analyses is to define the individual regions, which will often rely on established parcellations of the brain. There are now dozens of published brain parcellations, which can vary significantly in the number, size, and definitional criteria of their individual regions. I am working to understand how these variables may impact our analyses, and what these impacts might mean for interpreting the results of graph theory-based analyses in neuroscience.