Yuan Li
Shandong Normal University
13 Papers
5 Citations
Yuan Li is an academic researcher from Shandong Normal University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 4, co-authored 8 publications. Previous affiliations of Yuan Li include Lanzhou University & Shandong Institute of Business and Technology.
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Papers
Altered dynamic functional connectivity in weakly-connected state in major depressive disorder.
Zhijun Yao,Jie Shi,Zhe Zhang,Weihao Zheng,Tao Hu,Yuan Li,Yue Yu,Zicheng Zhang,Yu Fu,Ying Zou,Wenwen Zhang,Xia Wu,Bin Hu +12 more
TL;DR: It is suggested that the MDD-caused FC alterations mostly appeared in the weakly-connected state, which might contribute to clinical diagnosis of MDD.
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Structural alterations of the brain preceded functional alterations in major depressive disorder patients: Evidence from multimodal connectivity.
Zhijun Yao,Ying Zou,Weihao Zheng,Zhe Zhang,Yuan Li,Yue Yu,Zicheng Zhang,Yu Fu,Jie Shi,Wenwen Zhang,Xia Wu,Bin Hu +11 more
TL;DR: Alterations of SN in the brain of MDD patients preceded that of FN to some extent, and reorganization of the brain network was a mechanism which compensated for functional and structural alterations during disease progression.
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Neutrophil Infiltration Characterized by Upregulation of S100A8, S100A9, S100A12 and CXCR2 Is Associated With the Co-Occurrence of Crohn’s Disease and Peripheral Artery Disease
TL;DR: Investigating the key molecules and pathways mediating the co-occurrence of Crohn’s disease and peripheral arterial disease through quantitative bioinformatic analysis of a public RNA sequencing database elucidates S 100A8, S100A9,S100A12 and CXCR2 as hub genes for theCo-occurrences of CD and PAD.
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A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder.
Yu Fu,Jie Zhang,Yuan Li,Jie Shi,Ying Zou,Hanning Guo,Yongchao Li,Zhijun Yao,Yalin Wang,Bin Hu +9 more
TL;DR: This work proposes a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers, and suggests shape-related SBM features may further boost the classification performance of MRI between ASD and TD.
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Brain network alterations in individuals with and without mild cognitive impairment: parallel independent component analysis of AV1451 and AV45 positron emission tomography
TL;DR: It was found that the significant differences between MCI and NC were mainly distributed in DMN, cognitive control network and visual networks, and the altered brain networks obtained from pICA analysis are consistent with the abnormalities of brain network in MCI patients.