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Study on Functional Connectivity of Resting State Brain in First Episode Schizophrenic Auditory Hallucinations
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作者 WANG Yanjiao SUN Yutao +1 位作者 LU Xiuchun LIU Yanbin 《外文科技期刊数据库(文摘版)医药卫生》 2021年第11期013-015,共5页
Objective: in this paper, the functional connectivity of the resting state brain in first-episode schizophrenia patients with auditory hallucinations was studied. Methods: according to the DDSM -IV schizophrenia diagn... Objective: in this paper, the functional connectivity of the resting state brain in first-episode schizophrenia patients with auditory hallucinations was studied. Methods: according to the DDSM -IV schizophrenia diagnostic criteria, 94 patients with first schizophrenic auditory hallucinations and 94 healthy patients were selected as control. All subjects underwent clinical evaluation and resting-state functional magnetic resonance (FMRI) scans, and the differences between the two groups were analyzed and compared. Results: compared to control group, patients with first-episode schizophrenia auditory hallucination itself left superior temporal gyrus, the ipsilateral medial prefrontal function and connection of side guide vane fall further, at the same time, to the left side of the patients with first-episode schizophrenia hallucinations Heschl back with ipsilateral medial prefrontal function and connection of the contralateral temporal gyrus, showed a trend of decline, in addition, for the right of HG, it was also functionally connected to the left middle temporal gyrus and showed a declining trend. Conclusion: in the early stages of schizophrenia, there are serious abnormal connectivity between different brain regions, especially between the two sides of the brain, and there is also an abnormal pattern of "disconnection", which indicates that there are serious abnormalities in the integration function of the brain in the first episode of schizophrenia. 展开更多
关键词 first-episode schizophrenia auditory hallucinations resting state brain functional connectivity
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Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity 被引量:7
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作者 Zhenyu Liu Jiangang Liu +7 位作者 Huijuan Yuan Taiyuan Liu Xingwei Cui Zhenchao Tang Yang Du Meiyun Wang Yusong Lin Jie Tian 《Genomics, Proteomics & Bioinformatics》 SCIE CAS CSCD 2019年第4期441-452,共12页
Majority of type 2 diabetes mellitus(T2DM)patients are highly susceptible to several forms of cognitive impairments,particularly dementia.However,the underlying neural mechanism of these cognitive impairments remains ... Majority of type 2 diabetes mellitus(T2DM)patients are highly susceptible to several forms of cognitive impairments,particularly dementia.However,the underlying neural mechanism of these cognitive impairments remains unclear.We aimed to investigate the correlation between whole brain resting state functional connections(RSFCs)and the cognitive status in 95 patients with T2DM.We constructed an elastic net model to estimate the Montreal Cognitive Assessment(MoCA)scores,which served as an index of the cognitive status of the patients,and to select the RSFCs for further prediction.Subsequently,we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected RSFCs.The estimated and chronological MoCA scores were significantly correlated with R=0.81 and the mean absolute error(MAE)=1.20.Additionally,cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54%and the area under the receiver operating characteristic(ROC)curve(AUC)of 0.9737.This connectivity pattern not only included the connections between regions within the default mode network(DMN),but also the functional connectivity between the task-positive networks and the DMN,as well as those within the task-positive networks.The results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM. 展开更多
关键词 Type 2 diabetes mellitus resting state functional connectivity Elastic net Support vector machines MOCA
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