Network neuroscience

By D. Bassett, Olaf Sporns. Nature Neuroscience, 2017

2,307 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/c32b3653f51b709734066ed220943c18ffa7a4f0

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Citations in the corpus

Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks (2019, positive): “ICA is a model-free approach that decomposes neuroimaging datasets into a set of independent one-dimensional time series and associated three-dimensional spatial maps that describe the temporal and spatial characteristics of the underlying signals (Beckmann et al. 2005).”
Exploring Randomly Wired Neural Networks for Image Recognition (2019, methodology): “Random graph modeling has been used as a tool to study the neural networks of human brains [2, 4, 3].”
Small-World Brain Networks Revisited (2016, background): “We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.”
What are higher-order networks? (2021, methodology): “recordings or synthetic data from a neural model network dynamical system—can be seen as data that has been analyzed using correlation based techniques by projecting them on (higher-order) networks [22].”
Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function (2016, background): “For neural networks specifically, we lack a unifying mathematical framework to unambiguously describe the emergent behavior of the network in terms of its underlying structure (Bassett and Sporns, 2017).”
Motor primitives in space and time via targeted gain modulation in cortical networks (2018, positive): “In summary, our results support the view that knowing only the structure of neuronal networks is not sufficient to explain their dynamics [48, 49].”
Graph Neural Networks in Network Neuroscience (2021, background): “and [1], [2], [3], [4], [5], [6], [14], [16], [48] do not discuss specific GNN-based methods for solving neuroscience problems, but instead act as a reference for a specific topic (i.”
Communication dynamics in complex brain networks (2017, background): “Topology and dynamics, however, are central to network neuroscience: brain function relies on dynamic communication within highly structured networks 14 , which enables coherent brain states that underpin behaviour and cognition 12 .”
Hierarchical consciousness: the Nested Observer Windows model (2024, background): “Coherence allows for increased flexibility in a dynamic world because a weak synaptic pathway can be temporarily strengthened by coherent electrical activity (Bassett and Sporns 2017).”