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Communities in C.elegans connectome through the prism of non-backtracking walksстатья
- Авторы: Onuchin Arsenii, Chernizova Alina, Lebedev Mikhail, Polovnikov Kirill
- Журнал: ArXiv e-prints
- Год издания: 2022
- DOI: 10.48550/ARXIV.2207.11282
- Аннотация: The fundamental relationship between the mesoscopic structure of neuronal circuits and organismic functions they subserve is one of the major challenges in contemporary Neuroscience. Formation of structurally connected modules of neurons enacts the conversion from single-cell firing to large-scale behaviour of an organism, highlighting the importance of their accurate profiling in the data. While connectomes are typically characterized by significant sparsity of neuronal connections, recent advances in network theory and machine learning have revealed fundamental limitations of traditionally used community detection approaches in cases where the network is sparse. Here we studied the optimal community structure in the structural connectome of C.elegans, for which we exploited a non-conventional approach that is based on non-backtracking random walks, virtually eliminating the sparsity issue. In full agreement with the previous asymptotic results, we demonstrated that non-backtracking walks resolve the ground true annotation into clusters on stochastic block models (SBM) with the size and density of the connectome better than the spectral methods related to simple random walks. Based on the cluster detectability threshold, we determined that the optimal number of modules in the connectome of C.elegans is seven, which, surprisingly, further provides the best agreement with the anatomical partition (the so-called ganglia). The discovered communities have a clear interpretation in terms of their functional role, which allows us to discern three groups of structural clusters in the worm: the brain group, movement controller, and information flow connector. Thus, our work provides a robust network-based framework to reveal mesoscopic structures in sparse connectome data, paving way to further investigations of connectome mechanisms for different functions.
- Добавил в систему: Онучин Арсений Андреевич