Resolving inter-regional communication capacity in the human connectome

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher-order cognitive functions. We find that these regions’ proclivity towards functional integration could naturally arise from the brain’s anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network’s topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain’s functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

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“…Code and data used to perform the analyses can be found at https://github.com/fmilisav/milisav_dyadic_communication ( Milisav, 2023 ).…”

Section: Data Availabilitymentioning

confidence: 99%

Resolving inter-regional communication capacity in the human connectome

Milisav

Bazinet

Iturria-Medina

et al. 2023

Network Neuroscience

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“…In this report, we benchmark the performance of the simulated annealing procedure against another rewiring algorithm for strength sequence-preserving randomization (hereafter referred to as the Rubinov-Sporns algorithm; [92]), as well as the classic Maslov-Sneppen degree-preserving rewiring model [70]. In parallel, we introduce novel tools for assessing null network variability, a seldom considered [8, 61, 73], but important evaluation step when comparing network null models.…”

Section: Introductionmentioning

confidence: 99%

A simulated annealing algorithm for randomizing weighted networks

Milisav,

Bazinet,

Betzel

et al. 2024

Preprint

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Scientific discovery in connectomics relies on the use of network null models. To systematically evaluate the prominence of brain network features, empirical measures are compared against null statistics computed in randomized networks. Modern imaging and tracing technologies provide an increasingly rich repertoire of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here, to adapt network null models to weighted network inference, we propose a simulated annealing procedure for generating strength sequence-preserving randomized networks. This model outperforms other commonly used rewiring algorithms in preserving weighted degree (strength). We show that these results generalize to directed networks as well as a wide range of real-world networks, making them generically applicable in neuroscience and in other scientific disciplines. Furthermore, we introduce morphospace representation as a tool for the assessment of null network ensemble variability and feature preservation. Finally, we show how the choice of a network null model can yield fundamentally different inferences about established organizational features of the brain such as the rich-club phenomenon and lay out best practices for the use of rewiring algorithms in brain network inference. Collectively, this work provides a simple but powerful inferential method to meet the challenges of analyzing richly detailed next-generation connectomics datasets.

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Resolving inter-regional communication capacity in the human connectome

Cited by 2 publications

References 163 publications

Resolving inter-regional communication capacity in the human connectome

Resolving inter-regional communication capacity in the human connectome

A simulated annealing algorithm for randomizing weighted networks

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