Author Summary For more than a decade, network analysis has been used to investigate the organization and function of the human brain. However, applications of multilayer network analysis to neuronal networks are still at a preliminary stage, in part because of the difficulties of adequately representing brain-imaging data in the form of multilayer networks. In this study, we investigate the main differences in using multiplex networks versus more general multilayer networks when constructing frequency-based brain networks. Specifically, we are concerned with the differences for estimating the algebraic connectivity λ 2 , which has been related to structural, diffusion, and synchronization properties of networks. Using synthetic network models and real data, we show how edge-weight heterogeneity and missing interlayer edges crucially influence the value of λ 2 . Abstract We explore how to study dynamical interactions between brain regions by using functional multilayer networks whose layers represent different frequency bands at which a brain operates. Specifically, we investigate the consequences of considering the brain as (i) a multilayer network, in which all brain regions can interact with each other at different frequency bands; and as (ii) a multiplex network, in which interactions between different frequency bands are allowed only within each brain region and not between them. We study the second-smallest eigenvalue λ 2 of the combinatorial supra-Laplacian matrix of both the multiplex and multilayer networks, as λ 2 has been used previously as an indicator of network synchronizability and as a biomarker for several brain diseases. We show that the heterogeneity of interlayer edge weights and, especially, the fraction of missing edges crucially modify the value of λ 2 , and we illustrate our results with both synthetic network models and real data obtained from resting-state magnetoencephalography. Our work highlights the differences between using a multiplex approach and a full multilayer approach when studying frequency-based multilayer brain networks.