Motor imagery-based brain-computer interfaces (BCIs) use an individual’s ability to volitionally modulate localized brain activity as a therapy or to probe relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the eﬃcacy of BCIs. Experiments designed to probe the nature of BCI learning suggest that activity across functionally diverse cognitive systems is a hallmark of learning. However, little is known about how these networks interact through time to support learning. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in BCI learning using magnetoencephalography. We employ a minimally constrained matrix decomposition method – non-negative matrix factorization – to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression. We ﬁnd that good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial and temporal properties of subgraphs. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and ﬁnd that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of brain regions important for attention. The nature of our contribution to the neuroscience of BCI learning is both computational and theoretical; we ﬁrst use a minimally-constrained, individual speciﬁc method of identifying mesoscale structure in dynamic brain activity to show how global connectivity supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identiﬁed subgraphs are well suited to modulate attention.