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Xi-Nian Zuo
Researcher at Chinese Academy of Sciences
Publications - 210
Citations - 30340
Xi-Nian Zuo is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Resting state fMRI & Default mode network. The author has an hindex of 62, co-authored 194 publications receiving 23229 citations. Previous affiliations of Xi-Nian Zuo include Max Planck Society & Allen Institute for Brain Science.
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Corrigendum to “Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity” [NeuroImage 50/4 (2010) 1690–1701]
Maarten Mennes,Clare Kelly,Xi-Nian Zuo,Adriana Di Martino,Bharat B. Biswal,F. Xavier Castellanos,Michael P. Milham +6 more
TL;DR: This research presents a novel and scalable approach called “Smartphones, tablets, and brains” that allow for real-time, 3D image analysis of the brain activity of children aged six to 18 months.
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Open Science as a Better Gatekeeper for Science and Society: A Perspective from Neurolaw
Posted ContentDOI
Eliminating accidental deviations to minimize generalization error and maximize reliability: applications in connectomics and genomics
Eric W. Bridgeford,Shangsi Wang,Zhi Yang,Zeyi Wang,Ting Xu,R. Cameron Craddock,Jayanta Dey,Gregory Kiar,William Gray-Roncal,Carlo Coulantoni,Christopher Douville,Carey E. Priebe,Brian Caffo,Michael P. Milham,Xi-Nian Zuo,Reproducibility,Joshua T. Vogelstein +16 more
TL;DR: In this article, the authors proposed a new measure called discriminability, which quantifies the degree to which an individual9s samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean.
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Connecting Openness and the Resting-State Brain Network: A Discover-Validate Approach.
TL;DR: This study used a fully data-driven approach to discover and validate the association between openness and the resting-state brain network and identifies a parietal network that consisted of the precuneus and inferior parietal lobe.
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Corrigendum: Editorial: Reliability and Reproducibility in Functional Connectomics.
TL;DR: This research presents a novel probabilistic approach to estimating the number of mitochondria in the response of the immune system to chemotherapy-like injuries.