Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks

Abstract

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks

“…Our work is not the first one attempting to address the issue of multilayer clustering algorithms' performances. In Silva et al (2016) and Schmidt et al (2018), the authors propose analysis with the same purpose. However, in the former, the focus is only on algorithms based on evolutionary clustering, which have been tested in a simple synthetic network and in three real networks not related to neuroscience.…”

“…In Schmidt et al (2018), the authors tested two multilayer clustering approaches on an artificial network with more realistic properties. However, the test made on a single network, as previously said, might lack generalization of the results.…”

Deriving Electrophysiological Brain Network Connectivity via Tensor Component Analysis During Freely Listening to Music

“…T HE electrophysiological network, characterized by neuronal synchronization between spatially separate brain regions, plays an important role in the human cognition [1], [2]. Such neuronal-synchronized networks are transient and dynamic, established on the specific frequency modes in order to support ongoing cognitive operations [3]- [7]. The characterization of the functional networks during resting state, referred to as resting-state brain networks (RSNs), has been widely studied during past few decades [8]- [10].…”

Distinct Patterns of Functional Connectivity During the Comprehension of Natural, Narrative Speech

“…5,6 Advances in methodology and brain imaging technology have enabled us to examine how the brain mediates information flow in large-scale functional networks during continuous task execution. 5,[7][8][9][10] Function connectivity (FC), based on statistical interdependencies between signals recorded using neuroimaging technology, [11][12][13][14] is a widely-used approach to describe the large-scale configuration of brain functional activity. [15][16][17][18] FC modes provide fingerprints for the organization of functional brain networks during resting state [19][20][21] and continuous task performance.…”