Refined parcellation of the nervous system by algorithmic detection of hidden features within communities

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.

Combining local and global evolutionary trajectories of brain–behaviour relationships through game theory

“…Network neuroscience traditionally involves the analysis of co‐activated regions (Hutchinson et al., 2013) but can also include other aspects of the brain, such as structural covariance (Alexander‐Bloch, Giedd & Bullmore, 2013) or genomic patterns (Conaco et al., 2012; Valk et al., 2020). Through the implementation of graph theory in network neuroscience (Bullmore & Sporns, 2009; Rubinov & Sporns, 2010), neuroscientists can investigate the way ensembles of brain subunits functionally integrate or segregate during behaviour (Godwin, Barry, & Marois, 2015; Sporns, 2013), considering also structural features (Shi, Levina, & Noori, 2019). Modern parcellations incorporate structural and functional aspects, merging together cortical thickness, myelinization, brain activity, functional and structural connectivity.…”

Combining local and global evolutionary trajectories of brain-behavior equilibrium through game theory

“…Through the implementation of graph theory in network neuroscience (Bullmore and Sporns, 2009;Rubinov and Sporns, 2010), neuroscientists can investigate the way ensembles of brain subunits functionally integrate or segregate during behavior (Sporns, 2013;Godwin et al, 2015), considering also structural features (Shi et al, 2019). Modern parcellations incorporate structural and functional aspects (e.g., cortical thickness, myelinization, brain activity, functional and structural connectivity) both at the regional level and at the modular level.…”

“…• As the outcome is determined by the configuration of the whole network, behavior is determined by multivariate patterns of coactivation and inhibition among functionally related brain subunits, and not by the contribution of single brain subunit which instead represent sub-processors of chunks of information (Sporns, 2013;Godwin et al, 2015;Bassett and Sporns, 2017;Bathelt et al, 2019;Shi et al, 2019).…”

“…We incorporated global and local trajectories into a single framework taking advantage of graph theory (Bullmore and Sporns, 2009;Rubinov and Sporns, 2010;Sporns, 2013;Godwin et al, 2015;Bassett and Sporns, 2017;Shi et al, 2019) and game theory (Nash, 1950(Nash, , 1951Lieberman et al, 2005;Ripa et al, 2009;Johansson and Dieckmann, 2009;Brown, 2016). In the brain game, the global trajectory shapes the pattern of available structural-functional neural substrates of each species, while the local trajectory shapes the distribution of functional profiles supporting behavior, defining the stochastic (Shapley, 1953;Ummels and Wojtczak, 2011;Solan and Vieille, 2015) range of achievable payoffs, that is, the patterns of behavioral repertoires and cognitive skills of individuals within a species.…”

Dynamic Community Detection Based on a Label-Based Swarm Intelligence

“…Complex networks formalize the real-world as nodes and edges. Among them, nodes represent the objects of actual world and edges denote the relationships of objects [1], [2]. Under the circumstances, researches of the community discovery preferably trace networks (i.e., comprehend the topology [3], predict the evolution [4], analyze the function [5] and detect the regularity [6]).…”