Graph theoretical analysis of complex networks in the brain

By Cornelis J. Stam, Jaap C. Reijneveld. Nonlinear Biomedical Physics, 2007

1,053 citations recorded by the source corpus.

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern.

Subfields: Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/49768fb7280aec248809ec47594a4458888018a8

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Citations in the corpus

Graph-Based Network Analysis of Resting-State Functional MRI (2010, positive): “Several studies have successfully used EEG or MEG to construct intrinsic brain connectivity networks and further investigated state-dependent alterations in network topological properties (Stam and Reijneveld, 2007; Bassett and Bullmore, 2009).”
Schizophrenia, neuroimaging and connectomics (2012, background): “A final caveat is that a tendency towards a more random network architecture has been observed in several conditions other than schizophrenia, leading to the view that it may merely reflect a generic response to neural insult (Stam and Reijneveld, 2007).”
A New Measure of Centrality for Brain Networks (2010, background): “Such nodes are central in network organization, and are often identified by quantities known as centrality metrics [6,7,8,9,10, 11,12,13,14].”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, background): “How are the functional connections between brain regions organized? How efficiently can the brain integrate information between different sub-systems? And are there brain regions that have a specialized role in this efficient communication? Graph theory provides a theoretical framework in which the ”
Comparison of Characteristics between Region- and Voxel-Based Network Analyses in Resting-State fMRI Data (2009, positive): “In a small-world network, the efficacy of long distance communication can be quantified by a metric known as the characteristic path length L (Stam and Reijneveld 2007; Watts and Strogatz 1998).”
Proportional thresholding in resting‐state fMRI functional connectivity networks and consequences for patient‐control connectome studies: Issues and recommendations (2017, positive): “Furthermore, the examination of the topological aspects of functional brain networks and the possibility of examining possible disruptions in network organization in disease has become an invaluable tool for studying brain dysconnectivity in a wide range of psychiatric and neurological disorders (Fi”
GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, background): “through an optimal balance between local processing and global interaction [2].”
Complex network measures of brain connectivity: Uses and interpretations (2010, background): “Network characterization of structural and functional connectivity data is increasing (Bassett and Bullmore, 2006, 2009; Stam and Reijneveld, 2007; Bullmore and Sporns, 2009) and rests on several important motivations.”
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data (2019, background): “The small-world organization of the brain suggests efficient local information processing, together with several long-distance connections responsible for global communication [3, 34, 38].”
Efficiency of Functional Brain Networks and Intellectual Performance (2009, methodology): “…of several long-distance connections that ensure a high level of global communication efficiency across the network and integration of information between the different regions of the brain (Watts and Strogatz, 1998; Latora and Marchiori, 2001; Stam and Reijneveld, 2007; Bullmore and Sporns, 2009).”
Structural Properties of the Caenorhabditis elegans Neuronal Network (2009, background): “We also present the beginnings of a theoretical phase to conn e tomics [28], [29], pursuing an understanding of function.”
The application of graph theoretical analysis to complex networks in the brain. (2007, positive): “As mentioned before, more detailed mathematical descriptions can be found in Albert and Barabási (Albert and Barabási, 2002) and Stam and Reijneveld (Stam and Reijneveld, 2007).”
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]).”
Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, background): “Recent studies have demonstrated the smallworld organization in normal brain as well as disorganization in pathological brain using these techniques that have excellent temporal resolution, providing outstanding information on the dynamics of the networks [20,52,53].”
Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study (2009, background): “For the characterization of complex brain networks, the small-world is also an attractive model because the combination of high local clustering and short paths supports the two fundamental organizational principles in the brain: functional segregation and functional integration (for reviews, see Ba”
Multi-scale brain networks (2016, positive): “Finally, networks can be analyzed at different topological scales ranging from individual nodes to the network as a whole (Stam and Reijneveld, 2007; Bullmore and Sporns, 2009; Rubinov and Sporns, 2010).”