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  1.  22
    Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach.Lin Lin, Jindi Zhang, Yutong Liu, Xinyu Hao, Jing Shen, Yang Yu, Huashuai Xu, Fengyu Cong, Huanjie Li & Jianlin Wu - 2022 - Frontiers in Human Neuroscience 16:974094.
    ObjectiveType 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities.MethodsA total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state functional magnetic resonance (...)
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  2.  51
    Characterization of the Fiber Connectivity Profile of the Cerebral Cortex in Schizotypal Personality Disorder: A Pilot Study.Kai Liu, Teng Zhang, Qing Zhang, Yueji Sun, Jianlin Wu, Yi Lei, Winnie C. W. Chu, Vincent C. T. Mok, Defeng Wang & Lin Shi - 2016 - Frontiers in Psychology 7.
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  3.  19
    Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization.Xiulin Wang, Wenya Liu, Xiaoyu Wang, Zhen Mu, Jing Xu, Yi Chang, Qing Zhang, Jianlin Wu & Fengyu Cong - 2021 - Frontiers in Human Neuroscience 15.
    Ongoing electroencephalography signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder patients and (...)
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  4.  24
    Associations of the Disrupted Functional Brain Network and Cognitive Function in End-Stage Renal Disease Patients on Maintenance Hemodialysis: A Graph Theory-Based Study of Resting-State Functional Magnetic Resonance Imaging.Die Zhang, Yingying Chen, Hua Wu, Lin Lin, Qing Xie, Chen Chen, Li Jing & Jianlin Wu - 2021 - Frontiers in Human Neuroscience 15.
    Objective: Cognitive impairment is a common neurological complication in patients with end-stage renal disease undergoing maintenance hemodialysis. Brain network analysis based on graph theory is a promising tool for studying CI. Therefore, the purpose of this study was to analyze the changes of functional brain networks in patients on MHD with and without CI by using graph theory and further explore the underlying neuropathological mechanism of CI in these patients.Methods: A total of 39 patients on MHD and 25 healthy controls (...)
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