Dynamic small-world behavior in functional brain networks unveiled by an event-related networks approach

Abstract

There is growing interest in studying the role of connectivity patterns in brain functions. In recent years, functional brain networks were found to exhibit small-world properties during different brain states. In previous studies, time-independent networks were recovered from long time periods of brain activity. In this paper, we propose an approach, the event-related networks, that allows one to characterize the dynamical evolution of functional brain networks in time-frequency space. We illustrate this approach by characterizing connectivity patterns in magnetoencephalographic signals recorded during a visual stimulus paradigm. When compared with equivalent random and regular networks, the results reveal that functional connectivity varies with time and frequency during the processing of the stimulus, while maintaining a small-world structure. This approach may provide insights into the connectivity of other complex and spatially extended nonstationary systems.

Tracking brain dynamics via time-dependent network analysis

“…The above results constitute additional support for the currently emerging belief (initiated by two recent studies of Valencia et al (2008) andDe Vico Fallani et al (2007)) that NMTS, by unfolding self-organization tendencies, can provide accurate information about how the brain manages local processing and global integration. Moreover, our approach, using a frequencydependent selection of the employed time-window, appears to provide enhanced information, compared with the fixed-window approach, about delicate dynamical changes.…”

“…In this approach, the evolution of connectivity is studied by employing a time-window that constructs distinct connectivity graphs from the enclosed-signal segments, estimates some topological measures for each one, and plots the derived results as a function of time (Valencia et al, 2008;De Vico Fallani et al, 2007). In the limited number of recently published works that incorporate time-varying graphs, the following issues lack thorough treatment: (i) the duration of the time-window is fixed to an arbitrary value (e.g., 100 ms) without any consideration of the characteristic time scale of the underlying phenomena and (ii) the treatment of independent signal segments (i.e., no timewindow overlap) for the descriptions of the network cannot reveal the brain connectivity evolution with sufficient time resolution.…”

Graph analysis of functional brain networks: practical issues in translational neuroscience

“…Previous studies have put some effort in approaching the problem by simply tracking the temporal profile of individual topological metrics [128,137,183]. While these studies have the merit to address a central issue, they still represent an over-simplified methodological approach and leave space for advances.…”

Temporal networks

“…Such a study would be even richer in the temporal network framework where the motifs would represent temporal subnetworks. There are, to our knowledge, only a few papers where the time domain is directly taken into account: Valencia et al [156] study functional brain networks reconstructed from MEG data with the phase-locking criterion, and show that the functional connectivity varies with time and frequency during the processing of visual stimuli, while certain network features such as small-world characteristics are maintained (see also [149] [12] monitor the evolution of a brain network while the subject is learning a simple motor task. In addition, it would be of great interest to measure the dynamics of functional networks when the applied stimulus is also time-dependent, especially with naturalistic (close-toreal-life) paradigms such as watching a movie or listening to music in the fMRI scanner (see e.g.…”