Cited by 2,553Functional Brain Connectivity StudiesComplex Network Analysis TechniquesAdvanced Neuroimaging Techniques and Applications

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Efficiency and Cost of Economical Brain Functional Networks

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Achard, Sophie, Bullmore, Ed·2007·PLoS Computational Biology

DOI 10.1371/journal.pcbi.0030017PMC PMC1794324

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Abstract

Brain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI) in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years) and healthy young (N = 15; mean age = 24.7 years) volunteers were each scanned twice in a no-task or "resting" state following placebo or a single dose of a dopamine receptor antagonist (sulpiride 400 mg). Functional connectivity between 90 cortical and subcortical regions was estimated by wavelet correlation analysis, in the frequency interval 0.06-0.11 Hz, and thresholded to construct undirected graphs. These brain functional networks were small-world and economical in the sense of providing high global and local efficiency of parallel information processing for low connection cost. Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Dopamine antagonism also impaired global and local efficiency of the network, but this effect was differentially localised and did not interact with the effect of age. Brain functional networks have economical small-world properties-supporting efficient parallel information transfer at relatively low cost-which are differently impaired by normal aging and pharmacological blockade of dopamine transmission.

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Authors: Achard, S., & Bullmore, E.
Affiliations: University of Cambridge (GB)
ORCID: Achard, Sophie
Venue: PLoS Computational Biology
Publisher: Public Library of Science (PLoS)
Published: 2007-02-02
Volume: 3
Issue: 2
Pages: e17
ISSN: 1553-734X (ISSN-L)
1553-7358
DOI: 10.1371/journal.pcbi.0030017
PMC: PMC1794324
OpenAlex: W1974764791
Cited by: 2,553
References: 55
Citations by year: 2026: 103 · 2025: 147 · 2024: 150 · 2023: 142 · 2022: 162 · 2021: 201 · 2020: 169 · 2019: 162 · 2018: 185 · 2017: 175 · 2016: 157 · 2015: 144 · 2014: 130 · 2013: 143 · 2012: 115
FWCI: 26.22
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Computational Biology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Figshare (submittedVersion, cc-by)
Funders: Wellcome Trust
Medical Research Council
National Institute of Mental Health
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: functional brain networks, small-world networks, network efficiency, network cost, aging, sulpiride, dopamine receptor antagonism, functional connectivity, resting state, wavelet correlation, graph theory, frontal cortex, temporal cortex, subcortical structures
MeSH terms: Adult, Aged, Aging, Brain, Brain Mapping, Computer Simulation, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Neurons, Dopamine Antagonists

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