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Alternative TitleImaging genetics studies of psychiatry based on brain network
Thesis Advisor刘冰
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword精神分裂症 影像遗传学 脑网络 Schizophrenia Imaging Genetics Brain Network
Abstract精神分裂症是一种慢性的、严重的、功能障碍的脑疾病,至始至终都在影响着人类的健康。本文中,我们采用了一种联合分析手法:影像遗传学,将神经影像和遗传整合在一起,来研究精神分裂症的遗传风险位点对于脑网络的影响。我们希望能阐明这些风险位点与精神分裂症之间关联的神经机制。 首先,我们研究一个与精神分裂症有很强关联的候选基因-MIR137的可能神经机制。通过引入假设驱动的功能连接作为内表型,我们发现,不同MIR137基因型的个体间表现出显著不同的静息态的背外侧前额皮层到海马的功能连接。此外,风险杂合子个体的功能连接值与2-back工作记忆表现显著相关,但是这种相关在风险纯合子中没有出现。我们的发现证实了MIR137与这一功能环路以及工作记忆之间的关联。 然后,我们采用了一种全脑体素水平的、数据驱动的脑网络分析方法:特征向量中心度,来研究在多巴胺功能中起重要调节作用的COMT对于脑网络的影响。我们发现,Val纯合子相对于Met携带者在左侧海马旁回表现出更强的特征向量中心度,且该区域的中心度值与2-back工作记忆表现之间显著正相关。我们的发现可以为COMT Val158Met对于脑网络和认知功能的遗传影响提供重要信息。 最后,由于精神分裂症是一种多基因、高遗传的疾病,而之前的研究多集中在几个特定的风险位点上,我们这里采用多基因风险分数来探究这些新发现的风险位点与精神分裂症的内表型之间的关联。联合两万多个风险位点的效应,我们发现风险分数可以预测个体的工作记忆表现,并且与静息态的背外侧前额皮层到海马的功能连接显著正相关。我们的发现证实了精神分裂症的多基因属性,以及这些新发现的位点与精神分裂症标记之间的关联。
Other AbstractSchizophrenia is a chronic, severe, and disabling brain disorder which has affected people throughout history. Here we employed an integrated research method: imaging genetics combining neuroimaging data and genetics to investigate the effect of risk variants related to schizophrenia on brain network. We were aimed to illuminate the neural mechanism underlying the association between the risk alleles and schizophrenia. First, we chose to investigate the relationship between a candidate gene: MIR137, which was a well-validated gene related to schizophrenia, and a hypothesis-driven endophenotype: hippocampal formation (HF) functional coupling with dorsolateral prefrontal cortex (DLPFC). Based on resting-state functional MRI data, we found that individuals homozygous for the MIR137 risk allele (TT) showed significantly different DLPFC-HF functional connectivity compared with other individuals. What’s more, DLPFC-HF functional connectivity was negatively correlated with working memory performance in TG group; however, this phenomenon disappeared in TT group. Our findings confirmed the hypothesis that MIR137 impacts DLPFC-HF coupling and its association with working memory capacity. Then, we employed a voxel-wise data-driven method: eigenvector centrality (EC), to investigate the influence of COMT gene which plays a critical role in central dopamine function, on brain network. We found that Val/Val individuals exhibited significantly higher EC in the left parahippocampal cortex compared to the Met carriers. Furthermore, there was a significantly positive correlation between the mean EC of the significant cluster and the 2-back working memory performance. Our findings may provide plausible implications regarding individual differences in the genetic contribution of COMT to brain network and cognition. At last, since schizophrenia is a polygenic and highly heritable disease and previous studies often only focused on specific variants, we applied polygenic risk score (PGRS) combining the effects of more than twenty thousand risk variants related to schizophrenia, and endophenotype to investigate whether these newly identified alleles are associated with the markers of schizophrenia. We found that the PGRS could predict individual n-back working memory and was associated with resting-state DLPFC-HF functional connectivity. Our findings confirmed the polygenic nature of schizophrenia and the association between these newly identified variants and the cogniti...
Other Identifier201228014628063
Document Type学位论文
Recommended Citation
GB/T 7714
张小龙. 基于脑网络的精神疾病影像遗传学研究[D]. 中国科学院自动化研究所. 中国科学院大学,2015.
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