Human Brain Networks in Health and Disease

By D. Bassett, E. Bullmore. Current Opinion in Neurology, 2009

944 citations recorded by the source corpus.

Purpose of reviewRecent developments in the statistical physics of complex networks have been translated to neuroimaging data in an effort to enhance our understanding of human brain structural and functional networks. This review focuses on studies using graph theoretical measures applied to structural MRI, diffusion MRI, functional MRI, electroencephalography, and magnetoencephalography data. Recent findingsComplex network properties have been identified with some consistency in all modalities of neuroimaging data and over a range of spatial and time scales. Conserved properties include small worldness, high efficiency of information transfer for low wiring cost, modularity, and the existence of network hubs. Structural and functional network metrics have been found to be heritable and to change with normal aging. Clinical studies, principally in Alzheimer's disease and schizophrenia, have identified abnormalities of network configuration in patients. Future work will likely involve efforts to synthesize structural and functional networks in integrated models and to explore the interdependence of network configuration and cognitive performance. SummaryGraph theoretical analysis of neuroimaging data is growing rapidly and could potentially provide a relatively simple but powerful quantitative framework to describe and compare whole human brain structural and functional networks under diverse experimental and clinical conditions.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/04adddf983097c31beaa52cbf14752dfc69f4676

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Citations in the corpus

Rich-Club Organization of the Human Connectome (2011, methodology): “To assess statistical significance of rich-club organization, permutation testing was used (Bassett and Bullmore, 2009; van den Heuvel et al., 2010).”
Closures and Cavities in the Human Connectome (2016, background): “While traditional notions from graph theory and network science preferentially focus on local properties of the network at individual vertices or edges [2, 3, 9, 11], here we utilize an enriched network formalism that comes from the field of algebraic topology [26].”
Dynamic reconfiguration of human brain networks during learning (2010, methodology): “In order to measure the functional connectivity between these different portions, it is common to apply an atlas of the entire brain to raw fMRI data in order to combine information from all 3 mm cubic voxels found in a given functionally or anatomically defined region (for recent reviews, see [3–5]”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Studies in the field of complex brain networks have demonstrated that analyzing the network properties and metrics derived from brain topology using rs-fMRI can help neurologists distinguish patient groups from control subjects in mental disorders (Bassett and Bullmore, 2009; Wang et al., 2010; Stam”
Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data (2011, background): “These brain networks have consistently demonstrated many non-trivial topological properties, such as small-worldness, modularity and highly connected hubs (for reviews, see [3,4,5,6,7]), and exhibited distinct alterations associated with different neurocognitive disorders (for reviews, see [8,9]).”
Complex network measures of brain connectivity: Uses and interpretations (2010, background): “Across subject populations, comparisons may detect abnormalities of network connectivity in various brain disorders (Bassett and Bullmore, 2009).”
Cognitive Network Neuroscience (2015, background): “Such questions remain open in the extraction of anatomical, morphometric, and functional networks from neuroimaging data (Bullmore & Bassett, 2011; Bassett & Bullmore, 2009; Bullmore & Sporns, 2009; Bullmore et al., 2009).”
Brain graphs: graphical models of the human brain connectome. (2011, background): “There is also a rapidly growing literature on specialist applications to human neuroimaging and other neurophysiological data (for recent reviews, see Bassett & Bullmore 2006, 2009; Bullmore et al. 2009; Bullmore & Sporns 2009; Hagmann et al. 2010; and Wang et al. 2009).”
The structural-functional connectome and the default mode network of the human brain (2014, neutral): “However, only few studies have focused on the relationship between structural and functional brain connectivity (for reviews see Bassett and Bullmore, 2009; Damoiseaux and Greicius, 2009; Honey et al., 2010; Rykhlevskaia et al., 2008; Sporns, 2011).”
Cliques and cavities in the human connectome (2016, background): “While traditional notions from graph theory and network science preferentially focus on local properties of the network at individual vertices or edges [14–16, 68], here we”
The trees and the forest: Characterization of complex brain networks with minimum spanning trees. (2014, positive): “Abnormalities have been reported in Alzheimer's disease, frontal lobe dementia, Parkinson's disease, multiple sclerosis, brain tumours, epilepsy, schizophrenia, depression, autism and ADHD (Bassett and Bullmore, 2009; Stam and van Straaten, 2012).”
Brain Connectivity (2011, methodology): “For a summarized workflow of brain network construction using neuroimaging data, see Figure 1 (Bassett and Bullmore 2009).”