Network neuroscience

Abstract

Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

“…We chose to use graph theory with connectivity measured using envelope correlations (Hipp et al, 2012) as the core metric, to analyze cortical resting state (relaxed fixation) MEG signals from 131 individuals (64 females), ages 7 to 29, in each of the five fundamental frequency bands. We focused on five well-studied graph theory metrics because the approach is well-suited for studying global network properties also in the functional domain (Bullmore and Sporns, 2009, 2012; Rubinov and Sporns, 2010; Misic et al, 2016; Bassett and Sporns, 2017). The results were then validated using similar data from 31 individuals (16 females, ages 21–28) from an independent early adulthood resting state data set (Niso et al, 2015).…”

Role of graph architecture in controlling dynamical networks with applications to neural systems

“…Yet, exact mechanisms driving the relationship between structure and function remain elusive, hampering the analysis, modification, and control of interconnected complex systems. The relationship between interconnection architecture and dynamics is particularly important in biological systems such as the brain [5], where it is thought to support optimal information processing at cellular [6] and regional [7, 8] levels. Understanding structure-function relationships in this system could inform personalized therapeutics [9] including more targeted treatments for drug-resistant epilepsy to make the epileptic state energetically unfavorable to maintain [10, 11], especially due to the development of multi-site stimulation tools [12, 13] that allow for exponentially increasing stimulation configurations.…”

A Survey of Graph Based Complex Brain Network Analysis Using Functional and Diffusional MRI

“…They evaluated resting-state functional images that were acquired using an echo planar imaging sequence (repetition time = 2400 ms; echo time = 25 ms; flip angle = 80°; matrix = 68×68; voxel size = 3.25×3.25×3.25 mm 3 ). Bassett and Sporns (2007), illustrated that graph theory has proven to be an extremely productive framework in which to understand the structure and function of large-scale brain network and their implications for human cognition (Bassett and Sporns, 2007); alternative approaches that build on this framework-such as network control theory-necessarily require sceptical evaluation to clearly delineate value added. Now we just focus different equations on this table to measure connectivity of complex brain networks (Table 6).…”