Magnetic Resonance Connectome Automated Pipeline: An Overview

Gray, William R.; Bogovic, John A.; Vogelstein, Joshua T.; Landman, Bennett A.; Prince, Jerry L.; Vogelstein, R. Jacob

doi:10.1109/mpul.2011.2181023

Cited by 32 publications

(26 citation statements)

References 26 publications

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“…The current iteration of our software in the LONI Pipeline results in significant improvements to both scalability and processing time relative to the MRCAP baseline [5], which produces a small graph in approximately 10 hours on our small cluster (248 concurrent nodes, 1 TB total RAM). On average, the MIGRAINE baseline takes approximately 3 hours/subject to compute small graphs (i.e., the output from MRCAP), an additional 5 hours/subject to produce big graphs, and 3.5 hours/subject for graph invariants, for a total of 11.5 hours/subject.…”

Section: A Scalabilitymentioning

confidence: 98%

MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics

Roncal

Koterba

Mhembere

et al. 2013

2013 IEEE Global Conference on Signal and Information Processing

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Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at $\approx 1~mm^3$ scale using a combination of diffusion weighted magnetic resonance imaging, functional magnetic resonance imaging and structural magnetic resonance imaging scans. This manuscript summarizes a novel, scalable implementation of open-source algorithms to rapidly estimate magnetic resonance connectomes, using both anatomical regions of interest (ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we develop a novel nonparametric non-Euclidean reliability metric. Here we provide an overview of the methods used, demonstrate our implementation, and discuss available user extensions. We conclude with results showing the efficacy and reliability of the pipeline over previous state-of-the-art.Comment: Published as part of 2013 IEEE GlobalSIP conferenc

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Section: A Scalabilitymentioning

confidence: 98%

MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics

Roncal

Koterba

Mhembere

et al. 2013

2013 IEEE Global Conference on Signal and Information Processing

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“…Graphs are an increasingly popular data modality in scientific research and statistical inference, with diverse applications in connectomics [6], social network analysis [7], and pattern recognition [21], to name a few. Many joint graph inference methodologies (see, for example, [45,18,6,37]), joint graph embedding algorithms (see, for example, [19,34,41,39]) and graph-valued time-series methodologies (see, for example, [23,33,46,50]) operate under the implicit assumption that an explicit vertex correspondence is a priori known across the vertex sets of the graphs. While this assumption is natural in a host of real data settings, in many applications these correspondences may be unobserved and/or errorfully observed [48].…”

Section: Introductionmentioning

confidence: 99%

“…Connectomics offers a striking example of this continuum. Indeed, while for some simple organisms (e.g., the C. elegans roundworm [52]) explicit neuron labels are known across specimen, and in human DTMRI connectomes, the vertices are often regions of the brain registered to a common template (see [18]), explicit cross-subject neuron labels are often unknown for more complex organisms.…”

Section: Introductionmentioning

confidence: 99%

Information Recovery in Shuffled Graphs via Graph Matching

Lyzinski

2018

IEEE Trans. Inform. Theory

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While many multiple graph inference methodologies operate under the implicit assumption that an explicit vertex correspondence is known across the vertex sets of the graphs, in practice these correspondences may only be partially or errorfully known. Herein, we provide an information theoretic foundation for understanding the practical impact that errorfully observed vertex correspondences can have on subsequent inference, and the capacity of graph matching methods to recover the lost vertex alignment and inferential performance. Working in the correlated stochastic blockmodel setting, we establish a duality between the loss of mutual information due to an errorfully observed vertex correspondence and the ability of graph matching algorithms to recover the true correspondence across graphs. In the process, we establish a phase transition for graph matchability in terms of the correlation across graphs, and we conjecture the analogous phase transition for the relative information loss due to shuffling vertex labels. We demonstrate the practical effect that graph shufflingand matching-can have on subsequent inference, with examples from two sample graph hypothesis testing and joint spectral graph clustering.

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“…In connectomics, brain imaging data for each subject can be processed to output a graph, where each vertex represents a well-defined anatomical region present in each subject. For structural brain imaging, such as diffusion tensor MRI, an edge may represent the presence of anatomical connections between the two regions as estimated using tractography algorithms [14]. For functional brain imaging, such as fMRI, an edge between two regions may represent the presence of correlated brain activity between the two regions.…”

Section: Statistical Connectome Modelsmentioning

confidence: 99%

Connectome smoothing via low-rank approximations

Tange

Ketcha

Badea

et al. 2019

IEEE Trans. Med. Imaging

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In brain imaging and connectomics, the study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem. Often, this problem is especially difficult because the sample or cohort size is relatively small, sometimes even a single subject, while the number of nodes can be very large with noisy estimates of connectivity. While the element-wise sample mean of the adjacency matrices is a common approach, this method does not exploit underlying structural properties of the graphs. We propose using a low-rank method which incorporates dimension selection and diagonal augmentation to smooth the estimates and improve performance over the naïve methodology for small sample sizes. Theoretical results for the stochastic blockmodel show that this method offers major improvements when there are many vertices. Similarly, we demonstrate that the low-rank methods outperform the standard sample mean for a variety of independent edge distributions as well as human connectome data derived from magnetic resonance imaging, especially when sample sizes are small. Moreover, the low-rank methods yield "eigen-connectomes", which correlate with the lobe-structure of the human brain and superstructures of the mouse brain, and enable estimation of latent connectome structure. These results indicate that low-rank methods are an important part of the toolbox for researchers studying populations of graphs in general, and statistical connectomics in particular.

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Magnetic Resonance Connectome Automated Pipeline: An Overview

Cited by 32 publications

References 26 publications

MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics

MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics

Information Recovery in Shuffled Graphs via Graph Matching

Connectome smoothing via low-rank approximations

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