Z
Zijin Gu
Researcher at Cornell University
Publications - 8
Citations - 69
Zijin Gu is an academic researcher from Cornell University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 2, co-authored 5 publications receiving 7 citations.
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Journal ArticleDOI
Heritability and interindividual variability of regional structure-function coupling.
TL;DR: In this article, the authors quantify regional structural and functional coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals.
Posted Content
NeuroGen: activation optimized image synthesis for discovery neuroscience.
Zijin Gu,Keith Jamison,Meenakshi Khosla,Emily J. Allen,Yihan Wu,Thomas Naselaris,Kendrick Kay,Mert R. Sabuncu,Amy Kuceyeski +8 more
TL;DR: NeGen as discussed by the authors combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation.
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Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups.
TL;DR: In this article, the authors used clinically acquired lesion masks to estimate both structural and functional connectomes in patient populations to better understand brain lesion-dysfunction mapping in pwMS.
Posted ContentDOI
Regional structural-functional connectome coupling is heritable and associated with age, sex and cognition in adults
TL;DR: In this article, the authors investigated the relationship between individual cortical and subcortical regions of the human brain using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project.
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Personalized visual encoding model construction with small data
TL;DR: In this article , an ensemble approach is proposed to create encoding models for novel individuals with relatively little data by modeling each subject's predicted response vector as a linear combination of the other subjects' predicted response vectors.