Weight-conserving characterization of complex functional brain networks

Abstract

Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects.

NeuroImage, in press

References in corpus (15)

• Fast unfolding of communities in large networks
• Community structure in social and biological networks
• Community detection in graphs
• Fast algorithm for detecting community structure in networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Finding community structure in networks using the eigenvectors of matrices
• Link communities reveal multiscale complexity in networks
• Intensity and coherence of motifs in weighted complex networks
• The performance of modularity maximization in practical contexts
• Hierarchical modularity in human brain functional networks
• Community detection in networks with positive and negative links
• Robustness of community structure in networks
• Analysis of community structure in networks of correlated data
• Improved community structure detection using a modified fine tuning strategy
• A dual assortative measure of community structure

Cited by in corpus (41)

• Graph analysis of functional brain networks: practical issues in translational neuroscience
• Consistency and differences between centrality measures across distinct classes of networks
• Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks
• Learning and comparing functional connectomes across subjects
• Preferential Detachment During Human Brain Development: Age- and Sex-Specific Structural Connectivity in Diffusion Tensor Imaging (DTI) Data
• Full-density multi-scale account of structure and dynamics of macaque visual cortex
• Influence of Neighborhood SES on Functional Brain Network Development
• Analyzing complex functional brain networks: fusing statistics and network science to understand the brain
• Clustering coefficients for correlation networks
• Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity
• Resolving structural variability in network models and the brain
• Reduced integration and improved segregation of functional brain networks in Alzheimer's disease
• A Unifying Framework for Measuring Weighted Rich Clubs
• Fluctuations between high- and low-modularity topology in time-resolved functional connectivity
• A mathematical perspective on edge-centric brain functional connectivity
• Small-world topology of functional connectivity in randomly connected dynamical systems
• Conserved Ising Model on the Human Connectome
• Accounting for the Complex Hierarchical Topology of EEG Phase-Based Functional Connectivity in Network Binarisation
• Stability of spontaneous, correlated activity in mouse auditory cortex
• Configuration model for correlation matrices preserving the node strength
• The Complex Hierarchical Topology of EEG Functional Connectivity
• Functional brain modules reconfigure at multiple scales across the human lifespan
• Introduction to correlation networks: Interdisciplinary approaches beyond thresholding
• Explicitly Linking Regional Activation and Function Connectivity: Community Structure of Weighted Networks with Continuous Annotation
• The emergent integrated network structure of scientific research
• Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates
• Gene communities in co-expression networks across different tissues
• Uncovering functional brain signature via random matrix theory
• Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening
• Assortative Mixing in Weighted Directed Networks
• Extraction of hierarchical functional connectivity components in human brain using resting-state fMRI
• Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks
• Dissociable changes in functional network topology underlie early category learning and development of automaticity
• The landscape of NeuroImage-ing research
• Threshold-free estimation of entropy from a Pearson matrix
• The Importance of Being Negative: A serious treatment of non-trivial edges in brain functional connectome
• Early deafness leads to re-shaping of global functional connectivity beyond the auditory cortex
• Learning Graph Filters for Structure-Function Coupling based Hub Node Identification
• Combined MEG and fMRI Exponential Random Graph Modeling for inferring functional Brain Connectivity
• Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks
• Spatial Neural Networks and their Functional Samples: Similarities and Differences