Bayesian Inference and Testing of Group Differences in Brain Networks

Durante, Daniele; Dunson, David B.

doi:10.1214/16-ba1030

Cited by 85 publications

(77 citation statements)

References 65 publications

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“…Hypothesis testing on a type of graph average has also been proposed (Ginestet et al, 2017). Bayesian nonparametrics approaches for modeling populations of networks allow to test for local edge differences between the groups (Durante and Dunson, 2018), but are computationally feasible only for small networks.…”

Section: Introductionmentioning

confidence: 99%

Network classification with applications to brain connectomics

Arroyo

Kessler²,

Levina³

et al. 2019

Ann. Appl. Stat.

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While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of networks presents its own challenges which require a different set of analytic tools. Here we study the problem of classification of networks with labeled nodes, motivated by applications in neuroimaging. Brain networks are constructed from imaging data to represent functional connectivity between regions of the brain, and previous work has shown the potential of such networks to distinguish between various brain disorders, giving rise to a network classification problem. Existing approaches tend to either treat all edge weights as a long vector, ignoring the network structure, or focus on graph topology as represented by summary measures while ignoring the edge weights. Our goal is to design a classification method that uses both the individual edge information and the network structure of the data in a computationally efficient way, and that can produce a parsimonious and interpretable representation of differences in brain connectivity patterns between classes. We propose a graph classification method that uses edge weights as predictors but incorporates the network nature of the data via penalties that promote sparsity in the number of nodes, in addition to the usual sparsity penalties that encourage selection of edges. We implement the method via efficient convex optimization and provide a detailed analysis of data from two fMRI studies of schizophrenia.

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Section: Introductionmentioning

confidence: 99%

Network classification with applications to brain connectomics

Arroyo

Kessler²,

Levina³

et al. 2019

Ann. Appl. Stat.

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“…We applied our model to study communication behavior on real social media data (Instagram and Twitter), as well as for brain connectivity data. We saw that by not relying on a a user-specified threshold, our proposed method offers robustness over the methodology of [10], besides outperforming other baselines like LDA, Ngram language models.…”

Section: Resultsmentioning

confidence: 94%

“…However, much of the work has focused on testing subgraphs within a larger graph (e.g., [7]), or on one-sample tests comparing a single graph to a null model (e.g., [8]). Work focusing on populations of graphs has received considerably less attention and falls into one of two categories: that of [9], which introduces a geometric characterization of the network using the so-called Fréchet mean, and that of [10], who proposed a Bayesian latent-variable model for unweighted graphs. We focus on the latter, which allows us to bring the powerful machinery of probabilistic hierarchical modeling to the table, allowing noisiness and missingness, and providing interpretable confidence scores.…”

Section: Introductionmentioning

confidence: 99%

Multi-level Hypothesis Testing for Populations of Heterogeneous Networks

Gomes

Rao

Neville

2018

2018 IEEE International Conference on Data Mining (ICDM)

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In this work, we consider hypothesis testing and anomaly detection on datasets where each observation is a weighted network. Examples of such data include brain connectivity networks from fMRI flow data, or word co-occurrence counts for populations of individuals. Current approaches to hypothesis testing for weighted networks typically requires thresholding the edge-weights, to transform the data to binary networks. This results in a loss of information, and outcomes are sensitivity to choice of threshold levels. Our work avoids this, and we consider weighted-graph observations in two situations, 1) where each graph belongs to one of two populations, and 2) where entities belong to one of two populations, with each entity possessing multiple graphs (indexed e.g. by time). Specifically, we propose a hierarchical Bayesian hypothesis testing framework that models each population with a mixture of latent space models for weighted networks, and then tests populations of networks for differences in distribution over components. Our framework is capable of population-level, entity-specific, as well as edge-specific hypothesis testing. We apply it to synthetic data and three real-world datasets: two social media datasets involving word co-occurrences from discussions on Twitter of the political unrest in Brazil, and on Instagram concerning Attention Deficit Hyperactivity Disorder (ADHD) and depression drugs, and one medical dataset involving fMRI brain-scans of human subjects. The results show that our proposed method has lower Type I error and higher statistical power compared to alternatives that need to threshold the edge weights. Moreover, they show our proposed method is better suited to deal with highly heterogeneous datasets.

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“…, X q i ), and i is the noise. similar models (Fosdick and Hoff 2015;Durante, Dunson, and Vogelstein 2017;Durante and Dunson 2018;Tang et al 2017;Young and Scheinerman 2007), whereas Assumption 2 is a standard sparsity assumption for high-dimensional regression (Tibshirani 1996;Hastie et al 2015;Hastie, Tibshirani, and Friedman 2008). Under these two assumptions, a schematic of the model (2) can be seen in Fig.…”

Section: A Statistical Model For Multi-scale Network Regressionmentioning

confidence: 99%

Multi-Scale Network Regression for Brain-Phenotype Associations

Xia¹,

Cui³

et al. 2019

Preprint

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Complex brain networks are increasingly characterized at different scales, including global summary statistics, community connectivity, and individual edges. While research relating brain networks to demographic and behavioral measurements has yielded many insights into brain-phenotype relationships, common analytical approaches only consider network information at a single scale, thus failing to incorporate rich information present at other scales.Here, we designed, implemented, and deployed Multi-Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge-and community-level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant association between functional connectivity patterns with brain development, sex differences, and motion-related artifacts. Notably, compared to single-scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge-and community-1 level information, MSNR has the potential to yield novel insights into brain-behavior relationships.

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Bayesian Inference and Testing of Group Differences in Brain Networks

Cited by 85 publications

References 65 publications

Network classification with applications to brain connectomics

Network classification with applications to brain connectomics

Multi-level Hypothesis Testing for Populations of Heterogeneous Networks

Multi-Scale Network Regression for Brain-Phenotype Associations

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