Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure

Pavlović, Dragana M.; Guillaume, Bryan; Towlson, Emma K.; Kuek, Nicole M. Y.; Afyouni, Soroosh; Vértes, Petra E.; Yeo, B.T. Thomas; Bullmore, Edward T.; Nichols, Thomas E.

doi:10.1016/j.neuroimage.2020.116611

Cited by 8 publications

(1 citation statement)

References 53 publications

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“…Another approach in the tensor regression literature, which assumes a general version of the tensor and does not consider the symmetry constraint (inherent in an undirected network), applies regression with a matrix/tensor response (Gahrooei et al, 2021;Guhaniyogi and Spencer, 2021). Some recent articles on supervised stochastic block models (Kim and Levina, 2019;Pavlović et al, 2020) focus on clustering the nodes of the network into groups, which is methodologically a different problem than ours, where the focus is on clustering subjects into groups.…”

Section: Introductionmentioning

confidence: 99%

Covariate-Dependent Clustering of Undirected Networks with Brain-Imaging Data

Guha,

Guhaniyogi

2024

Technometrics

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This article focuses on model-based clustering of subjects based on the shared relationships of subject-specific networks and covariates in scenarios when there are differences in the relationship between networks and covariates for different groups of subjects. It is also of interest to identify the network nodes significantly associated with each covariate in each cluster of subjects. To address these methodological questions, we propose a novel nonparametric Bayesian mixture modeling framework with an undirected network response and scalar predictors. The symmetric matrix coefficients corresponding to the scalar predictors of interest in each mixture component involve low-rankness and group sparsity within the low-rank structure. While the lowrank structure in the network coefficients adds parsimony and computational efficiency, the group sparsity within the low-rank structure enables drawing inference on network nodes and cells significantly associated with each scalar predictor. Being a principled Bayesian mixture modeling framework, our approach allows model-based identification of the number of clusters, offers clustering uncertainty in terms of the co-clustering matrix and presents precise characterization of uncertainty in identifying network nodes significantly related to a predictor in each cluster. Empirical results in various simulation scenarios illustrate substantial inferential gains of the proposed framework in comparison with competitors. Analysis of a real brain connectome dataset using the proposed method provides interesting insights into the brain regions of interest (ROIs) significantly related to creative achievement in each cluster of subjects. Supplementary material shows the convergence rate for the posterior predictive density of the proposed model, additional simulation examples with model mis-specification and also presents traceplots for various model parameters to demonstrate convergence of the Markov Chain Monte Carlo (MCMC) algorithm.

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Section: Introductionmentioning

confidence: 99%

Covariate-Dependent Clustering of Undirected Networks with Brain-Imaging Data

Guha,

Guhaniyogi

2024

Technometrics

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Bayesian network mediation analysis with application to the brain functional connectome

Zhao

Chen

Cai

et al. 2022

Statistics in Medicine

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The brain functional connectome, the collection of interconnected neural circuits along functional networks, facilitates a cutting‐edge understanding of brain functioning, and has a potential to play a mediating role within the effect pathway between an exposure and an outcome. While existing mediation analytic approaches are capable of providing insight into complex processes, they mainly focus on a univariate mediator or mediator vector, without considering network‐variate mediators. To fill the methodological gap and accomplish this exciting and urgent application, in the article, we propose an integrative mediation analysis under a Bayesian paradigm with networks entailing the mediation effect. To parameterize the network measurements, we introduce individually specified stochastic block models with unknown block allocation, and naturally bridge effect elements through the latent network mediators induced by the connectivity weights across network modules. To enable the identification of truly active mediating components, we simultaneously impose a feature selection across network mediators. We show the superiority of our model in estimating different effect components and selecting active mediating network structures. As a practical illustration of this approach's application to network neuroscience, we characterize the relationship between a therapeutic intervention and opioid abstinence as mediated by brain functional sub‐networks.

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Network neuroscience and the connectomics revolution

Betzel

2022

Connectomic Deep Brain Stimulation

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Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure

Cited by 8 publications

References 53 publications

Covariate-Dependent Clustering of Undirected Networks with Brain-Imaging Data

Covariate-Dependent Clustering of Undirected Networks with Brain-Imaging Data

Bayesian network mediation analysis with application to the brain functional connectome

Network neuroscience and the connectomics revolution

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