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Chao-Gan Yan

Researcher at Chinese Academy of Sciences

Publications -  107
Citations -  12863

Chao-Gan Yan 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 34, co-authored 103 publications receiving 9824 citations. Previous affiliations of Chao-Gan Yan include MIND Institute & Beijing Normal University.

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DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.

TL;DR: The newly developed toolbox, DPABI, which was evolved from REST and DPARSF is introduced, designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies.
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REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing

TL;DR: A toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST), which was developed in MATLAB with graphical user interface (GUI).
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A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics

TL;DR: A comprehensive voxel-based examination of the impact of motion on the BOLD signal suggests that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact.
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The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry.

TL;DR: The conceptual basis of the NKI-RS is described, including study design, sampling considerations, and steps to synchronize phenotypic and neuroimaging assessment, and it is hoped that familiarity with the conceptual underpinnings will facilitate harmonization with future data collection efforts aimed at advancing psychiatric neuroscience and nosology.
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Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans

TL;DR: The results demonstrate the highly organized modular architecture and associated topological properties in the temporal and spatial brain functional networks of the human brain that underlie spontaneous neuronal dynamics, which provides important implications for understanding of how intrinsically coherent spontaneous brain activity has evolved into an optimal neuronal architecture to support global computation and information integration in the absence of specific stimuli or behaviors.