Weighted stochastic block model

Ng, Tin Lok James

doi:10.1007/s10260-021-00590-6

Cited by 14 publications

(6 citation statements)

References 41 publications

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“…However, we view the knowledge derived from sparse light microscopy collected from multiple brains valuable in its own merit, as it provides complementary information on the shared circuit elements across individuals (Ascoli 2015). In this regard, it is useful to note that our probabilistic model, currently restricted to Bernoulli adjacency matrices, can be generalized to cluster non-binary connectomes using a weighted SBM (Aicher, Jacobs, & Clauset 2015; Ng & Murphy 2021), which would allow accounting for the number of synapses between connected neuron pairs (Tecuatl, Wheeler, & Ascoli 2021) in the identification of connectivity-based classes.…”

Section: Discussionmentioning

confidence: 99%

Circuit Analysis of the Drosophila Brain using Connectivity-based Neuronal Classification Reveals Organization of Key Communication Pathways

Mehta

Goldin

Ascoli

2022

Preprint

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We present a functionally relevant, quantitative characterization of the neural circuitry of Drosophila melanogaster at the mesoscopic level of neuron types as classified exclusively based on network connectivity. Starting from a large neuron-to-neuron brain-wide connectome of the fruit fly, we use stochastic block modeling and spectral graph clustering to group neurons together into a common 'cell class' if they connect to neurons of other classes according to the same probability distributions. Thus, nodes of the identified circuit represent cell classes, while the directed edges represent the connection probabilities between neurons in those respective classes. We then characterize the connectivity-based cell classes with standard neuronal biomarkers, including neurotransmitters, developmental birthtimes, morphological features, spatial embedding, and functional anatomy. Mutual information indicates that connectivity-based classification reveals aspects of neurons that are not adequately captured by traditional classification schemes. Next, using graph-theoretic and random walk analyses to identify neuron classes as hubs, sources, or destinations, we detect patterns of directional connectivity and information flow that potentially underpin specific functional interactions in the Drosophila brain. We uncover a core of highly interconnected dopaminergic cell classes functioning as the backbone information highway for multisensory integration. We also recognize glutamatergic-dominant connections between the motor and optic regions, possibly facilitating circadian rhythmic activity. Additional predicted pathways pertain to spatial orientation, fight-or-flight response, and olfactory learning. Mapping the developmental birthtimes of neurons also pinpoints the temporal change of the circuit blueprint over the fly lifespan, revealing distinct critical growth periods of individual pathways. Our analysis provides experimentally testable hypotheses critically deconstructing complex brain function from organized connectomic architecture. We present a functionally relevant, quantitative characterization of the neural circuitry of Drosophila melanogaster at the mesoscopic level of neuron types as classified exclusively based on network connectivity. Starting from a large neuron-to-neuron brain-wide connectome of the fruit fly, we use stochastic block modeling and spectral graph clustering to group neurons together into a common 'cell class' if they connect to neurons of other classes according to the same probability distributions. Thus, nodes of the identified circuit represent cell classes, while the directed edges represent the connection probabilities between neurons in those respective classes. We then characterize the connectivity-based cell classes with standard neuronal biomarkers, including neurotransmitters, developmental birthtimes, morphological features, spatial embedding, and functional anatomy. Mutual information indicates that connectivity-based classification reveals aspects of neurons that are not adequately captured by traditional classification schemes. Next, using graph-theoretic and random walk analyses to identify neuron classes as hubs, sources, or destinations, we detect patterns of directional connectivity and information flow that potentially underpin specific functional interactions in the Drosophila brain. We uncover a core of highly interconnected dopaminergic cell classes functioning as the backbone information highway for multisensory integration. We also recognize glutamatergic-dominant connections between the motor and optic regions, possibly facilitating circadian rhythmic activity. Additional predicted pathways pertain to spatial orientation, fight-or-flight response, and olfactory learning. Mapping the developmental birthtimes of neurons also pinpoints the temporal change of the circuit blueprint over the fly lifespan, revealing distinct critical growth periods of individual pathways. Our analysis provides experimentally testable hypotheses critically deconstructing complex brain function from organized connectomic architecture.

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Section: Discussionmentioning

confidence: 99%

Circuit Analysis of the Drosophila Brain using Connectivity-based Neuronal Classification Reveals Organization of Key Communication Pathways

Mehta

Goldin

Ascoli

2022

Preprint

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“…Fourth, in this paper, we estimate the number of communities for weighted networks generated from DCDFM by Equation ( 3 ) when we choose the method

as the spectral method nDFA. If we let

be algorithms developed in [ 48 , 49 , 50 , 51 , 52 , 53 , 54 ] to fit their weighted stochastic blockmodels for weighted networks, we wonder that we can also estimate K for these models through Equation ( 3 ). We leave them for the future.…”

Section: Conclusion and Future Workmentioning

confidence: 99%

“…A significant drawback of the above SBM-based and DCSBM-based methods is that they ignore the impact of edge weights which are common in network data and could help us to understand the community structure of a network better [ 16 ]. Recently, community detection in weighted networks has become a hot topic and many statistical models have been developed to fit weighted networks, such as the weighted stochastic blockmodels (WSBM) proposed in [ 48 , 49 , 50 , 51 , 52 , 53 , 54 ], the distribution-free model (DFM) of [ 55 ], and the degree-corrected distribution-free model (DCDFM) introduced in [ 56 ]. Among these models, DFM and its extension DCDFM stand out as they allow edge weights to follow any distribution as long as the expected adjacency matrix follows a block structure related to community partition.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Number of Communities in Weighted Networks

Qing

2023

Entropy

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Community detection in weighted networks has been a popular topic in recent years. However, while there exist several flexible methods for estimating communities in weighted networks, these methods usually assume that the number of communities is known. It is usually unclear how to determine the exact number of communities one should use. Here, to estimate the number of communities for weighted networks generated from arbitrary distribution under the degree-corrected distribution-free model, we propose one approach that combines weighted modularity with spectral clustering. This approach allows a weighted network to have negative edge weights and it also works for signed networks. We compare the proposed method to several existing methods and show that our method is more accurate for estimating the number of communities both numerically and empirically.

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“…An argument can also be made to use stochastic block models for weighted food webs. While such approaches exist for weighted networks (Aicher et al 2015;Ng and Murphy 2021), it is also important to consider that this could introduce further stochasticity in the data, for example, fluctuating populations and biomass.…”

Section: Paper I-iiimentioning

confidence: 99%

The significance of species groups for food web structure and functioning

Ohlsson

2023

Linköping Studies in Science and Technology. Dissertations

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II. Took active part in formulating the research question, did a majority of the coding for the simulations and analyzes, wrote the first draft of the manuscript.III. Formulated the research question, did a majority of the coding for the simulations and analyzes, wrote the first draft of the manuscript.IV. Took part in formulating the research question, did a majority of the coding for the simulations and analyzes, wrote the first draft of the manuscript.

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Weighted stochastic block model

Cited by 14 publications

References 41 publications

Circuit Analysis of the Drosophila Brain using Connectivity-based Neuronal Classification Reveals Organization of Key Communication Pathways

Circuit Analysis of the Drosophila Brain using Connectivity-based Neuronal Classification Reveals Organization of Key Communication Pathways

Estimating the Number of Communities in Weighted Networks

The significance of species groups for food web structure and functioning

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