中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Exploring different impaired speed of genetic-related brain function and structures in schizophrenic progress using multimodal analysis

文献类型:会议论文

作者Luo N(罗娜)9,10; Tian Lin8; Vince Calhoun7; Jiayu Chen7; Dongdong Lin7; Shuquan Rao6; Jian Yang5; Chuanjun Zhuo4; Yong Xu3; Jessica A. Turner2
出版日期2018
会议日期Honolulu, Hawaii
会议地点July 17-21, 2018
英文摘要

Schizophrenia (SZ) is a highly heritable disease exhibiting substantial structural and functional brain impairments. The duration of illness and medication use may cause different presentations of impairments in patients. To understand the progressive variations of the disease, most
recent studies have reported brain functional or structural abnormalities associated with illness duration, but a comprehensive study of pathology underlying brain structure, function and illness duration is still limited. In this work, we employed a three-way parallel independent component analysis (pICA) algorithm to jointly analyze grey matter volume(GM),
functional connectivity (FC) and single nucleotide polymorphisms (SNPs) from drug-naïve first-episode [FESZ], chronic schizophrenia [CSZ]) and healthy controls[HC], aiming to identify the linked alterations in SNP-GM-FC components, and evaluate the impairment speed of imaging measures
associated with SZ-susceptible genetic variants in different disease stages (FESZ and CSZ). Results demonstrated significant group differences on GM and FC in hippocampus, temporal gyrus and cerebellum between SZ and HC, which are also significantly correlated with SNPs residing in genes like GABBR2, SATB2, CACNA1C, PDE4B, involved in pathways of
cell junction, synapse and neuron projection. Moreover, two-sample t-tests showed that GM volume and FC strength presented similar trends of progressive decrease with the increase of the illness duration (HC>FESZ>CSZ). Besides that genetic-related GM and FC components both showed significant associations with illness duration, FC indicates the higher impairment speed than GM, suggesting that functional connectivity may serve as a more sensitive measure to detect the
disruptions in SZ at the very early stage.

源URL[http://ir.ia.ac.cn/handle/173211/28381]  
专题自动化研究所_脑网络组研究中心
作者单位1.CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences
2.Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, USA
3.Department of Psychiatry, First Clinical Medical College, First Hospital of Shanxi Medical University, Taiyuan 030000, China
4.Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Tianjin Mental Health Center
5.Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China
6.School of Life Science and Engineering, Southwest Jiaotong University
7.Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, GA 30303, USA
8.Wuxi Mental Health Center, Wuxi 214000, China
9.University of Chinese Academy of Sciences, Beijing 100190, China
10.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences.
推荐引用方式
GB/T 7714
Luo N,Tian Lin,Vince Calhoun,et al. Exploring different impaired speed of genetic-related brain function and structures in schizophrenic progress using multimodal analysis[C]. 见:. July 17-21, 2018. Honolulu, Hawaii.

入库方式: OAI收割

来源:自动化研究所

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