Motifs in Brain Networks

By Olaf Sporns, Rolf Kötter. 2004

838 citations recorded by the source corpus.

Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks (structural and functional motifs) in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information.

Subfields: Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Structural Properties of the Caenorhabditis elegans Neuronal Network (2009, background): “[14–20] and references therein and thereto) based on these incomplete or inconsistent wiring diagrams [7,13].”
Organization, development and function of complex brain networks. (2004, background): “A systematic analysis of motifs in brain networks revealed a small number of characteristic motifs shared among several examples of cortical networks [ 31 ], potentially indicating common modes of information processing.”
Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal (2012, background): “A motif thought to be of some significance is the feedforward “loop” (Sporns and Kotter, 2004).”
Small-World Brain Networks Revisited (2016, background): “More formally, clustering measures the probability that the nodes j and k , which are both directly connected to node i , are also directly connected to each other; this is equivalent to measuring the proportion of closed triangular 3-node motifs in a network [3].”
On How Network Architecture Determines the Dominant Patterns of Spontaneous Neural Activity (2008, background): “Moreover, the analysis of large-scale neuroanatomical data sets has revealed characteristic building blocks of the network architecture [13].”
Frontiers in Integrative Neuroscience Integrative Neuroscience Neural Synchrony in Cortical Networks: History, Concept and Current Status (neutral): “An alternative approach to study dynamical properties and to infer functionality of the topology of complex networks is the decomposition of a network in principle building blocks, i.e. motifs that are repeated connectivity patterns within the network (Milo et al., 2002; Sporns and Kotter, 2004).”
Graph theoretical analysis of complex networks in the brain (2007, background): “Some of the same data studied in the above mentioned papers were re-investigated for the presence of motifs (connected graphs forming a subgraph of a larger network) by Sporns and Kotter [89].”
Toward a Complementary Neuroscience: Metastable Coordination Dynamics of the Brain (2009, background): “The coupling between local groups of neurons is supported by long-range functional connectivity [25], [91], [92].”
Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory (2008, neutral): “Network motifs are subgraphs that appear more frequently in a real network than could be statistically expected, and are associated to the network evolution (Milos et al., 2002; Sporns and Kotter, 2004; Onnela et al., 2005).”
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, methodology): “…to replicate these results to all extent but rather want to highlight differences between methods we here only considered motifs with up to three nodes, which left a single triad, namely triads census 201 ([59,70], see also Figure 7 and S4), i.e. the mutually connected two-path (ID = 9 in [67]).”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “Some types of motifs are present in neu ral networks, as feed-forward loops and bi-parallel connections [279].”
Copying and Evolution of Neuronal Topology (2008, methodology): “In which parts of the brain could a natural selection algorithm be implemented? We have relatively little data about the details of cortical circuit topology [45,107].”
Dynamic Properties of Network Motifs Contribute to Biological Network Organization (2005, methodology): “Used as a structural analysis technique, enumeration of all subgraphs consisting of three or four nodes summarizes the local connectivity patterns that compose a complex network [8–10].”
The Human Connectome: A Structural Description of the Human Brain (2005, background): “Computational analyses of these datasets have revealed a broad range of network characteristics [28], including the existence of clusters of brain regions [29], hierarchical organization [30,31], small-world attributes [32,33], distinct functional streams [34], motifs [35], and areal contributions t”
Complex network measures of brain connectivity: Uses and interpretations (2010, methodology): “This observation motivated the introduction of functional motifs , de ﬁ ned as possible subsets of connection patterns embedded within anatomical motifs ( Sporns and Kotter, 2004).”
Reconstruction and Simulation of Neocortical Microcircuitry. (2015, background): “The reconstruction also allows studies of neurons involved in polysynaptic pathways (see NMC Portal) forming known motifs (Honey et al., 2007; Perin et al., 2011; Silberberg, 2008; Sporns and Kötter, 2004).”
Social network theory: new insights and issues for behavioral ecologists (2009, background): “2007), neural networks (Loughlin and Sejnowski 2003; Sporns and Kotter 2004; Humphries et al. 2006), networks of interacting individual organisms (Scott 2000; Lusseau and Newman 2004; Lusseau et al.”
The application of graph theoretical analysis to complex networks in the brain. (2007, positive): “Other studies on macaque visual and whole cortex, cat cortex as well as the human brainstem reticular formation confirmed small world features of functional neural networks, and demonstrated little or no evidence for scale free degree distributions (Sporns and Zwi, 2004; Humphries et al., 2006).”