Dataset Information

Discovering network structure beyond communities.

ABSTRACT: To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

SUBMITTER: Nishikawa T

PROVIDER: S-EPMC3240966 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Discovering network structure beyond communities.

Nishikawa Takashi T Motter Adilson E AE

Scientific reports 20111109

To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characte ...[more]

PMID: 22355667

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Project description:BackgroundUnderstanding the neural basis of behavior requires insight into how different brain systems coordinate with each other. Existing connectomes for various species have highlighted brain systems essential to various aspects of behavior, yet their application to complex learned behaviors remains limited. Research on vocal learning in songbirds has extensively focused on the vocal control network, though recent work implicates a variety of circuits in contributing to important aspects of vocal behavior. Thus, a more comprehensive understanding of brain-wide connectivity is essential to further assess the totality of circuitry underlying this complex learned behavior.ResultsWe present the Oscine Structural Connectome for Investigating NEural NETwork ORGanization (OSCINE-NET.ORG), the first interactive mesoscale connectome for any vocal learner. This comprehensive digital map includes all known connectivity data, covering major brain superstructures and functional networks. Our analysis reveals that the songbird brain exhibits small-world properties, with highly connected communities functionally designated as motor, visual, associative, vocal, social, and auditory. Moreover, there is a small set of significant connections across these communities, including from social and auditory sub-communities to vocal sub-communities, which highlight ethologically relevant facets of vocal learning and production. Notably, the vocal community contains the majority of the canonical vocal control network, as well as a variety of other nodes that are highly interconnected with it, meriting further evaluation for their inclusion in this network. A subset of nodes forms a "rich broker club," highly connected across the brain and forming a small circuit amongst themselves, indicating they may play a key role in information transfer broadly. Collectively, their bidirectional connectivity with multiple communities indicates they may act as liaisons across multiple functional circuits for a variety of complex behaviors.ConclusionsOSCINE-NET.ORG offers unprecedented access to detailed songbird connectivity data, promoting insight into the neural circuits underlying complex behaviors. This data emphasizes the importance of brain-wide integration in vocal learning, facilitating a potential reevaluation of the canonical vocal control network. Furthermore, we computationally identify a small, previously unidentified circuit-one which may play an impactful role in brain-wide coordination of multiple complex behaviors.

Dataset Information

Discovering network structure beyond communities.

Publications

Discovering network structure beyond communities.

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