When we are asleep, previously established memories are reprocessed, leading to the stabilization and improved retention of the learned information. Theoretical accounts suggest such sleep-related memory consolidation relies on the precisely coordinated reactivation of brain-wide memory networks. However, a firm neural basis for this intriguing notion has been lacking. Sleep spindles - brief rhythmic brain oscillations - hold considerable promise in this regard, considering their intimate relation to memory performance. Yet, whether sleep spindle activity reflects the reinstatement of widespread memory networks is presently an unanswered question. Applying advanced network analysis tools, I propose to characterize human sleep spindle networks in unprecedented detail. Using this approach, I aim to demonstrate that patterns of spindle activity reflect distinct reactivated memory networks. If successful, the proposed studies would offer the strongest support thus far that human sleep-related memory reprocessing is reliant on the patterned reactivation of pre-existing memory networks, and that sleep spindles mediate this process. As such, this project holds the promise of greatly expanding our understanding of how our brain enhances our memories while we sleep.