Author

William Gray Roncal

Johns Hopkins University Applied Physics Laboratory

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 3 Research Focus:benchmarking· 2 Research Focus:database / resource· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:software / tool· 2 Assay:multiome· 1

Frequent Co-Authors

Brian S. CaffoiD· 2 shared Carey E. PriebeiD· 2 shared Daniel S. MarguliesiD· 2 shared Disa MhembereiD· 2 shared Eric BridgefordiD· 2 shared Gregory KiariD· 2 shared Joshua T VogelsteiniD· 2 shared Michael Peter MilhamiD· 2 shared

Activity by Year

2018-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Brian S. CaffoORCID

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3 papers

Recent·Most cited

A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis

Jaewon Chung, Ross Lawrence, Alex Loftus, Derek Pisner, Gregory Kiar, Eric Bridgeford, William Gray Roncal, Vikram Chandrashekhar, Disa Mhembere, Sephira G. Ryman, Xi‐Nian Zuo, Daniel S. Margulies, Richard Cameron Craddock, Carey E. Priebe, Rex Eugene Jung, , Joshua T Vogelstein

This paper introduces a low-resource pipeline for estimating connectomes from MRI data, demonstrating its reliability across diverse datasets without manual intervention.

bioRxiv · Nov 1, 2021 · PreprintBioinformatics

A three-dimensional thalamocortical dataset for characterizing brain heterogeneity

Judy A. Prasad, Aishwarya Balwani, Erik C. Johnson, Joseph Miano, Vandana Sampathkumar, Vincent De Andrade, Kamel Fezzaa, Ming Du, Rafael Vescovi, Chris J. Jacobsen, Konrad Paul Kording, Doğa Gürsoy, William Gray Roncal, Narayanan Kasthuri, Eva L. Dyer

The paper provides a three-dimensional X-ray microtomography dataset of thalamocortical structures to study neural heterogeneity.

Scientific Data · Oct 20, 2020Peer-reviewedNeuroscience

A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability

Gregory Kiar, Eric Bridgeford, William Gray Roncal, Vikram Chandrashekhar, Disa Mhembere, Sephira G. Ryman, Xi‐Nian Zuo, Daniel S. Margulies, Richard Cameron Craddock, Carey E. Priebe, Rex Eugene Jung, Vince D. Calhoun, Brian S. Caffo, Randal C. Burns, Michael Peter Milham, Joshua T Vogelstein

The paper develops a pipeline to estimate connectomes from neuroimaging data, showing high consistency within individuals but significant variability across studies and sites.

bioRxiv · Apr 24, 2018 · PreprintNeuroscience