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Preprints, Working Papers, ... (Preprint) Year : 2024

Revising clustering and small-worldness in brain networks

Abstract

As more connectome data become available, the question of how to best analyse the structure of biological neural networks becomes increasingly pertinent. In brain networks, knowing that two areas are connected is often not sufficient, as the directionality and weight of the connection affect the dynamics in crucial ways. Still, the methods commonly used to estimate network properties, such as clustering and small-worldness, usually disregard features encoded in the directionality and strength of network connections. To address this issue, we propose using fully-weighted and directed clustering measures that provide higher sensitivity to non-random structural features. Using artificial networks, we demonstrate the problems with methods routinely used in the field and how fully-weighted and directed methods can alleviate them. Specifically, we highlight their robustness to noise and their ability to address thresholding issues, particularly in inferred networks. We further apply our method to the connectomes of different species and uncover regularities and correlations between neuronal structures and functions that cannot be detected with traditional clustering metrics. Finally, we extend the notion of small-worldness in brain networks to account for weights and directionality and show that some connectomes can no longer be considered ``small-world''. Overall, our study makes a case for a combined use of fully-weighted and directed measures to deal with the variability of brain networks and suggests the presence of complex patterns in neural connectivity that can only be revealed using such methods.
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Motifs.pdf (15.29 Ko) Télécharger le fichier
Supplementary_Drosophila.pdf (530.72 Ko) Télécharger le fichier
Supplementary_graph_metrics.pdf (22.56 Ko) Télécharger le fichier
Supplementary_method_comparison.pdf (23.55 Ko) Télécharger le fichier
compare_motifs_mouse_NEW.pdf (1.82 Mo) Télécharger le fichier
comparison_directed_undirected.pdf (2.08 Mo) Télécharger le fichier
droso_modulatory_net.pdf (1.3 Mo) Télécharger le fichier
mouse_pathways.pdf (27.02 Ko) Télécharger le fichier
suppmat_swp.pdf (13.8 Ko) Télécharger le fichier
swp.pdf (84.67 Ko) Télécharger le fichier
thresholding_comparison.pdf (52.76 Ko) Télécharger le fichier
weight_contribution.pdf (1.41 Mo) Télécharger le fichier
Licence : CC BY SA - Attribution - ShareAlike
Licence : CC BY SA - Attribution - ShareAlike
Licence : CC BY SA - Attribution - ShareAlike
Origin : Files produced by the author(s)
Licence : CC BY SA - Attribution - ShareAlike
Licence : CC BY SA - Attribution - ShareAlike
Licence : CC BY SA - Attribution - ShareAlike
Origin : Files produced by the author(s)
Licence : CC BY SA - Attribution - ShareAlike
Licence : CC BY SA - Attribution - ShareAlike
Licence : CC BY SA - Attribution - ShareAlike
Licence : CC BY SA - Attribution - ShareAlike
Licence : CC BY SA - Attribution - ShareAlike
Licence : CC BY SA - Attribution - ShareAlike
Origin : Files produced by the author(s)
Licence : CC BY SA - Attribution - ShareAlike

Dates and versions

hal-04427288 , version 1 (01-02-2024)

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Tanguy Fardet, Emmanouil Giannakakis, Lukas Paulun, Anna Levina. Revising clustering and small-worldness in brain networks. 2024. ⟨hal-04427288⟩
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