Symmetric Bilinear Regression for Signal Subgraph Estimation

Wang, Lu; Zhang, Zhengwu; Dunson, David B.

doi:10.1109/tsp.2019.2899818

Cited by 26 publications

(19 citation statements)

References 32 publications

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“…From the result, we can see that a simpler model in general favors predictive accuracy. This discovery is also confirmed by Wang et al (2019), where only a few brain connections are selected to predict traits. In terms of the prediction power, TN-PCA is better than the simple PCA and much better than the CP tensor regression.…”

Section: Resultsmentioning

confidence: 66%

“…A few recent variants using Bayesian principles can be found in Guha-niyogi et al, [2017] and Guha and Rodriguez [2018]. Another method to compare is the supervised bi-linear regression (BLR) [Wang et al, 2019] model, which emphasizes signal sub-network selection. In our implementation of BLR, we set the number of components as 10 and selected the L 1 penalty parameter based on cross-validation.…”

Section: Resultsmentioning

confidence: 99%

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Tensor network factorizations: Relationships between brain structural connectomes and traits

et al. 2019

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Advanced brain imaging techniques make it possible to measure individuals’ structural connectomes in large cohort studies non-invasively. Given the availability of large scale data sets, it is extremely interesting and important to build a set of advanced tools for structural connectome extraction and statistical analysis that emphasize both interpretability and predictive power. In this paper, we developed and integrated a set of toolboxes, including an advanced structural connectome extraction pipeline and a novel tensor network principal components analysis (TN-PCA) method, to study relationships between structural connectomes and various human traits such as alcohol and drug use, cognition and motion abilities. The structural connectome extraction pipeline produces a set of connectome features for each subject that can be organized as a tensor network, and TN-PCA maps the high-dimensional tensor network data to a lower-dimensional Euclidean space. Combined with classical hypothesis testing, canonical correlation analysis and linear discriminant analysis techniques, we analyzed over 1100 scans of 1076 subjects from the Human Connectome Project (HCP) and the Sherbrooke test-retest data set, as well as 175 human traits measuring different domains including cognition, substance use, motor, sensory and emotion. The test-retest data validated the developed algorithms. With the HCP data, we found that structural connectomes are associated with a wide range of traits, e.g., fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain structural connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity. We also demonstrated that our extracted structural connectomes and analysis method can give superior prediction accuracies compared with alternative connectome constructions and other tensor and network regression methods.

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Section: Resultsmentioning

confidence: 66%

Section: Resultsmentioning

confidence: 99%

Tensor network factorizations: Relationships between brain structural connectomes and traits

et al. 2019

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“…Notice that

which may not be negative semi-definite. However, since the diagonal entries of each adjacency matrix

are zero, the loss function (4) is actually a convex function of each entry β hu in β h when fixing the others, making the coordinate descent algorithm very appealing ( Wang et al, 2019 ). The technical details of deriving the analytic form update for each parameter are discussed in Appendix A .…”

Section: Methodsmentioning

confidence: 99%

Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition

et al. 2021

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Structural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.

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“…Another group of methods finds subsets of vertices, or a subgraph, that contain the most information about certain covariates [9,46,109,110,113]. Estimating signal subgraphs is useful since networks can be extremely large (i.e.…”

Section: Bag Of Networkmentioning

confidence: 99%

Statistical Connectomics

Chung¹,

Bridgeford²,

Arroyo³

et al. 2020

Preprint

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The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics.

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Symmetric Bilinear Regression for Signal Subgraph Estimation

Cited by 26 publications

References 32 publications

Tensor network factorizations: Relationships between brain structural connectomes and traits

Tensor network factorizations: Relationships between brain structural connectomes and traits

Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition

Statistical Connectomics

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