Multi-scale brain networks

Richard F. Betzel, Danielle S. Bassett
2017 NeuroImage  
A B S T R A C T The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales-of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and
more » ... l methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multiscale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure-irrespective of species, imaging modality, or spatial resolution. 2016). Finally, networks can be analyzed at different topological scales ranging from individual nodes to the network as a whole (Stam and Reijneveld, 2007; Bullmore and Sporns, 2009; Rubinov and Sporns, 2010) . Collectively, these scales define the axes of a three-dimensional space in which any analysis of brain network data lives (Fig. 1) . Most brain network analyses exist as points in this space-i.e. they focus on networks defined singularly at one spatial, temporal, and topological scale. We argue that, while such studies have proven illuminating, in order to better understand the brain's true multi-scale, multi-modal nature, it is essential that our network analyses begin to form bridges that link different scales to one another. In this review, we focus on two specific aspects of the multi-scale brain. First, we present and discuss variations of network algorithms (particularly, community detection) that make it possible to describe a network at multiple topological scales Fortunato, 2010) . We choose to focus on community detection-which we define carefully in the next section-because it encompasses one of the most frequently used set of tools capable of extracting and characterizing network organization across a continuous range of scales. We do, of course, make mention of other alternatives. Next, we discuss the topic http://dx.
doi:10.1016/j.neuroimage.2016.11.006 pmid:27845257 pmcid:PMC5695236 fatcat:wqdktooym5g6vnepdwj7bfhelm