Hierarchical Features of Large-Scale Cortical Connectivity

Abstract

The analysis of complex networks has revealed patterns of organization in a variety of natural and artificial systems, including neuronal networks of the brain at multiple scales. In this paper, we describe a novel analysis of the large-scale connectivity between regions of the mammalian cerebral cortex, utilizing a set of hierarchical measurements proposed recently. We examine previously identified functional clusters of brain regions in macaque visual cortex and cat cortex and find significant differences between such clusters in terms of several hierarchical measures, revealing differences in how these clusters are embedded in the overall cortical architecture. For example, the ventral cluster of visual cortex maintains structurally more segregated, less divergent connections than the dorsal cluster, which may point to functionally different roles of their constituent brain regions.

17 pages, 6 figures

References in corpus (5)

• Statistical mechanics of complex networks
• The structure and function of complex networks
• Evolution of networks
• Efficient Hopfield pattern recognition on a scale-free neural network
• Associative Recall in Non-Randomly Diluted Neuronal Networks

Cited by in corpus (6)

• Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications
• Correlations between structure and dynamics in complex networks
• Beyond the average: Detecting global singular nodes from local features in complex networks
• What are the Best Hierarchical Descriptors for Complex Networks?
• Diversity of Cortical States at Non-Equilibrium Simulated by the Ferromagnetic Ising Model Under Metropolis Dynamics
• Feature extraction from complex networks: A case of study in genomic sequences classification