ArticleScientific reports2017
Inferring structural connectivity using Ising couplings in models of neuronal networks.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 24 citations in OpenAlex.
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- Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes.Frontiers in computational neuroscience · 2023Article
- Disrupted Excitation-Inhibition Balance in Cognitively Normal Individuals at Risk of Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2023Article
- Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function.Network neuroscience (Cambridge, Mass.) · 2022Article
- Optimal Population Coding for Dynamic Input by Nonequilibrium Networks.Entropy (Basel, Switzerland) · 2022Article
- Article
- Connectome Signatures of Hyperexcitation in Cognitively Intact Middle-Aged Female APOE-ε4 Carriers.Cerebral cortex (New York, N.Y. : 1991) · 2020Article
- Inference of synaptic connectivity and external variability in neural microcircuits.Journal of computational neuroscience · 2020Article
- Six degrees of depolarization: Comment on "Network science of biological systems at different scales: A review" by Marko Gosak et al.Physics of life reviews · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Functional connectivity metrics have been widely used to infer the underlying structural connectivity in neuronal networks. Maximum entropy based Ising models have been suggested to discount the effect of indirect interactions and give good results in inferring the true anatomical connections. However, no benchmarking is currently available to assess the performance of Ising couplings against other functional connectivity metrics in the microscopic scale of neuronal networks through a wide set of network conditions and network structures. In this paper, we study the performance of the Ising model couplings to infer the synaptic connectivity in in silico networks of neurons and compare its performance against partial and cross-correlations for different correlation levels, firing rates, network sizes, network densities, and topologies. Our results show that the relative performance amongst the three functional connectivity metrics depends primarily on the network correlation levels. Ising couplings detected the most structural links at very weak network correlation levels, and partial correlations outperformed Ising couplings and cross-correlations at strong correlation levels. The result was consistent across varying firing rates, network sizes, and topologies. The findings of this paper serve as a guide in choosing the right functional connectivity tool to reconstruct the structural connectivity.
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Registered trials
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