Multi-scale brain networks

By Richard F. Betzel, D. Bassett. NeuroImage, 2016

560 citations recorded by the source corpus.

&NA; 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 novel methodological advances for the multi‐scale brain. We separate our exposition into content related to multi‐scale topological structure, multi‐scale temporal structure, and multi‐scale 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. HighlightsThe human brain can be represented as a multi‐scale network.Characterizing the architecture of multi‐scale networks requires new tools.We review promising multilayer tools from network science.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/70083c0582dd2f465161de97114f45c92ab943d0

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Communication dynamics in complex brain networks (2017, background): “…that describes the system as a whole: heterogeneous degree and strength distributions, short path lengths and high clustering, a multi-scale modular organization and a densely connected core of high-degree nodes are some of the network attributes that are shared across species 30 and scales 31,32 .”
Graph theory methods: applications in brain networks (2018, background): “One common avenue is the inclusion 33 Varying the resolution parameter is important since many brain networks exhibit communities across different scales, 34 which renders the selection of a single scale of analysis potentially problematic.”
Graph Neural Networks in Network Neuroscience (2021, unknown): “, Alzheimer’s disease and autism) [12], [13].”