Author

Jae Wook Cho

Child Mind InstituteORCID 0000-0002-2742-9136Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Organism:human· 2 Assay:imaging· 2 Approach:statistical model· 2 Approach:deep learning· 1 Approach:experimental assay· 1 Organism:non-human primate· 1

Frequent Co-Authors

Michael Peter MilhamiD· 3 shared Ting XuiD· 3 shared Annachiara KorchmarosiD· 2 shared Joshua T VogelsteiniD· 2 shared Adam MessingeriD· 1 shared Alan Charles EvansiD· 1 shared Alisa A. OmelchenkoiD· 1 shared Andrew S. FoxiD· 1 shared

Activity by Year

2020-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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3 shared papersNeuroscience

Michael Peter MilhamORCID

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3 papers

Recent·Most cited

A Guide for Quantifying and Optimizing Measurement Reliability for the Study of Individual Differences

Ting Xu, Jae Wook Cho, Gregory Kiar, Eric Bridgeford, Joshua T Vogelstein, Michael Peter Milham

The paper introduces a framework to quantify and optimize measurement reliability for studying individual differences in neuroscience and clinical research.

bioRxiv · Jan 27, 2022 · PreprintNeuroscience

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

Jae Wook Cho, Annachiara Korchmaros, Joshua T Vogelstein, Michael Peter Milham, Ting Xu

The paper examines how concatenating fMRI data affects the reliability of functional connectomics, finding that concatenating multiple scans under the same state improves reliability compared to a single long scan.

NeuroImage · Nov 25, 2020Peer-reviewedNeuroscience

U-Net Model for Brain Extraction: Trained on Humans for Transfer to Non-human Primates

Xindi Wang, Xinhui Li, Jae Wook Cho, Brian Edward Russ, Nanditha Rajamani, Alisa A. Omelchenko, Lei Ai, Annachiara Korchmaros, Stephen J. Sawiak, R. Austin Benn, Pamela García-Saldivar, Zheng Wang, Ned H. Kalin, Charles E. Schroeder, Richard Cameron Craddock, , Ting Xu

The paper develops a transfer-learning U-Net model for brain extraction in non-human primates using human data, achieving better performance than traditional methods and popular MRI packages.

bioRxiv · Nov 17, 2020 · PreprintNeuroscience