2017/03/17 by Javier M. Buldú, Mason A. Porter, Buldú, Javier M. +1 · 4 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · Physics and Astronomy · #Blind Source Separation Techniques #Combinatorics (math.CO) #Data Analysis #FOS: Biological sciences #FOS: Mathematics #FOS: Physical sciences #Functional Brain Connectivity Studies #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Statistics and Probability (physics.data-an) #math.CO #physics.data-an #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1703.06091
13 pages, 8 figures
openalex publication_date 2017/03/17 · arxiv created 2017/09/14 · arxiv updated 2017/09/15 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
We explore how to study dynamical interactions between brain regions using functional multilayer networks whose layers represent the different frequency bands at which a brain operates. Specifically, we investigate the consequences of considering the brain as a multilayer network in which all brain regions can interact with each other at different frequency bands, instead of as a multiplex network, in which interactions between different frequency bands are only allowed within each brain region and not between them. We study the second smallest eigenvalue of the combinatorial supra-Laplacian matrix of the multilayer network in detail, and we thereby show that the heterogeneity of interlayer edges and, especially, the fraction of missing edges crucially modify the spectral properties of the multilayer network. We illustrate our results with both synthetic network models and real data sets obtained from resting state magnetoencephalography. Our work demonstrates an important issue in the construction of frequency-based multilayer brain networks.