Organization, development and function of complex brain networks
Open accessAbstract
Recent research has revealed general principles in the structural and functional organization of complex networks which are shared by various natural, social and technological systems. This review examines these principles as applied to the organization, development and function of complex brain networks. Specifically, we examine the structural properties of large-scale anatomical and functional brain networks and discuss how they might arise in the course of network growth and rewiring. Moreover, we examine the relationship between the structural substrate of neuroanatomy and more dynamic functional and effective connectivity patterns that underlie human cognition. We suggest that network analysis offers new fundamental insights into global and integrative aspects of brain function, including the origin of flexible and coherent cognitive states within the neural architecture.
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A fundamental property of brain networks-either structural or functional-is that they demonstrate clear small-world characteristics; that is, they are simultaneously segregated (high clustering coefficient) and integrated (short path lengths).
“A fundamental property of brain networks-either structural or functional-is that they demonstrate clear small-world characteristics; that is, they are simultaneously segregated (high clustering coefficient) and integrated (short path lengths).10,87,103”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [87]
Brain processes can best be thought of as coordinated activity of disparate regions across time.
“Two, given that brain processes can best be thought of as coordinated activity of disparate regions across time [14], [15], we necessitated an approach for arriving at precise brain connectivity maps that quantifies relations among brain regions across time for each individual.”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [14]
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- Authors
- SPORNS, O., CHIALVO, D., KAISER, M., & HILGETAG, C.
- Affiliations
- Indiana University Bloomington (US)
- Northwestern University (US)
- Constructor University (DE)
- Corresponding author
- SPORNS, O
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2004-09
- Volume
- 8
- Issue
- 9
- Pages
- 418-425
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2128314959
- Cited by
- 2,055
- References
- 78
- Citations by year
- 2026: 29 · 2025: 75 · 2024: 61 · 2023: 66 · 2022: 82 · 2021: 90 · 2020: 132 · 2019: 141 · 2018: 117 · 2017: 99 · 2016: 106 · 2015: 130 · 2014: 136 · 2013: 144 · 2012: 116
- FWCI
- 21.82
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Funders
- Studienstiftung des Deutschen Volkes
- National Institute of Neurological Disorders and Stroke (R01NS042660, R01NS035115)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- large-scale brain networks, structural connectivity, functional connectivity, effective connectivity, network topology, cognitive states, neural architecture, network analysis
- MeSH terms
- Brain, Cognition, Humans, Nerve Net
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