A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability

Kiar, Gregory; Ew, Bridgeford; Wr, Gray; Chandrashekhar,; Mhembere, Disa; Ryman, Sephira G.; Zuo, Xi‐Nian; Ds, Margulies; Rc, Craddock; Ce, Priebe; Jung, Rex E.; Vd, Calhoun; Caffo, Brian; Burns, Randal; Mp, Milham; Vogelstein, Joshua T.

doi:10.1101/188706

Cited by 48 publications

(44 citation statements)

References 80 publications

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“…The method is computationally efficient since the regularization we use is convex, and the solution is implemented with efficient optimization algorithms. These properties guarantee fast convergence to the solution, making the methods scalable to networks with thousands of nodes, which is enough to deal with many of the brain atlases usually employed in neuroimaging (see for example Kiar et al (2018)). Statistically, the rate of convergence depends on the number of active nodes only, not the total number of nodes, which allows for accurate results with even moderate sample sizes if the active node set is small.…”

Section: Numerical Results On Simulated Networkmentioning

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: Numerical Results On Simulated Networkmentioning

confidence: 99%

Network classification with applications to brain connectomics

Arroyo

Kessler²,

Levina³

et al. 2019

Ann. Appl. Stat.

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show abstract

“…Connectomes have often weighted edges, which can take on arbitrary values. Rescaling and normalizing the edge weights has been shown to increase reliability and can improve estimation of spectral embeddings [53]. Pass-to-ranks (PTR) is a method for rescaling the positive edge weights such that all edge weights are between 0 and 1, inclusive.…”

Section: Pass-to-ranks (Ptr)mentioning

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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“…Diffusion tensor magnetic resonance imaging (DTMRI) is a form of MRI which allows for the estimation of coarse scale approximations of the networks of a human brain [41,42], so called connectomes. While the estimated graphs frequently only have 10s or 100s of vertices, recent tractography methods and parcellation techniques have allowed the creation of connectomes with tens 0 56 121 20 right 7 173 646 0 81 0 65 576 20 left 20 136 816 1 121 0 78 324 10 right 20 192 1077 2 144 0 56 256 20 left 33 70 369 0 36 0 45 289 20 right 33 81 355 0 49 0 30 121 10 left 34 206 1528 0 324 0 78 729 10 right 34 173 1363 0 256 0 95 625 10 of thousands of vertices.…”

Section: Large-scale Dtmri Graphsmentioning

confidence: 99%

Matched Filters for Noisy Induced Subgraph Detection

Sussman

Park

Priebe

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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The problem of finding the vertex correspondence between two noisy graphs with different number of vertices where the smaller graph is still large has many applications in social networks, neuroscience, and computer vision. We propose a solution to this problem via a graph matching matched filter: centering and padding the smaller adjacency matrix and applying graph matching methods to align it to the larger network. The centering and padding schemes can be incorporated into any algorithm that matches using adjacency matrices. Under a statistical model for correlated pairs of graphs, which yields a noisy copy of the small graph within the larger graph, the resulting optimization problem can be guaranteed to recover the true vertex correspondence between the networks. However, there are currently no efficient algorithms for solving this problem. To illustrate the possibilities and challenges of such problems, we use an algorithm that can exploit a partially known correspondence and show via varied simulations and applications to Drosophila and human connectomes that this approach can achieve good performance.

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A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability

Cited by 48 publications

References 80 publications

Network classification with applications to brain connectomics

Network classification with applications to brain connectomics

Statistical Connectomics

Matched Filters for Noisy Induced Subgraph Detection

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