Abstract
The nervous system can be represented as a multiscale network comprised by single cells or ensembles that are linked by physical or functional connections. Groups of morphologically and physiologically diverse neurons are wired as connectivity patterns with a certain degree of universality across species and individual variability. Thereby, community detection approaches are often used to characterize how neural units cluster into such densely interconnected groups. However, the communities may possess deeper structural features that remain undetected by current algorithms. We present a scheme for refined parcellation of neuronal networks, by identifying local integrator units (LU) that are contained in network communities. An LU is defined as a connected subnetwork in which all neuronal connections are constrained within this unit, and can be formed for instance by a set of interneurons. Our method uses the Louvain algorithm to detect communities and participation coefficients to discriminate local neurons from global hubs. The sensitivity of the algorithm for discovering LUs with respect to the choice of community detection algorithm and network parameters was tested by simulations of different synthetic networks. The appropriateness of the algorithm for real-world scenarios was demonstrated on weighted and binary Caenorhabditis elegans connectomes. The detected LUs are distinctly localized within the worm body and clearly define functional groups. This approach provides a robust, observer-independent parcellation strategy that is useful for functional structure confirmation and potentially contributes to the current efforts in quantitative whole-brain architectonics of different species as well as the analysis of functional connectivity networks.
Highlights
The brain processes information as a multiscale network comprising local integrative components
We presume that interneurons form a local integrator unit (LU) to perform information processing in the neural system, and we propose a network-theoretical approach to detect them
An local integrator units (LU) is defined as a connected subnetwork of a neuronal community consisting of interneurons (INs) whose nerve fibers are completely constrained to that community
Summary
The brain processes information as a multiscale network comprising local integrative components. A main focus of neuroscientists has been to subdivide brain into a mosaic of anatomically and functionally distinct, spatially contiguous areas (cortical areas and subcortical nuclei) as a prerequisite for understanding how the brain works These areas differ from their neighbors in terms of microstructural architecture, functional specialization, connectivity with other areas, and/or orderly intra-area topographic organization [1,2,3,4,5,6]. We presume that interneurons form a local integrator unit (LU) to perform information processing in the neural system, and we propose a network-theoretical approach to detect them. It represents a simple framework to improve our understanding of recent findings [32,33] on the function of C. elegans neurons
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