Many factors can contribute to inter-individual variability in learning, including the mechanics of the particular skill or information that one learns and the strategies by which one apprehends a given phenomenon, concept, or principle \cite{KANFER1990221,marton1976qualitative}. It is intuitively plausible that individuals could be predisposed to engage in common and distinct learning strategies based on the nature of their central nervous systems \cite{bassett2017network}. Unique and shared genetic and environmental factors present in the early stages of development can give rise to different and common wiring patterns, respectively \cite{di2014unraveling}, which in  turn lead to distinct and conserved spatiotemporal responses in neuronal ensembles  \cite{Atun-Einy2012,harrison2011learning,wigfield2000expectancy}. A particularly parsimonious language in which to describe and characterize such wiring patterns and spatiotemporal responses is that of network science \cite{bullmore2009complex,bassett2017networka}. In this formalism, regions of the brain are represented as network nodes, whose activity can vary over time \cite{murphy2016explicitly}, and functional connections between brain regions are represented as network edges, whose strength can also vary over time \cite{gu2017functional}. While some organizational principles of brain network organization and dynamics appear to be conserved throughout a healthy, normative population \cite{betzel2016multi}, others – including measures of activity \cite{prat2011individual,prat2007individual} and connectivity \cite{KANFER1990221} – can vary appreciably across individuals \cite{Finn_2015}. Yet, the degree to which activity and connectivity are constrained versus variable across individuals during learning remains largely unknown.