基于结构与功能连接网络的自闭症脑重组发育研究及生物标记筛选

Autism is a highly prevalent neurodevelopmental disorder. Growing evidence suggests that autism is associated with abnormalities in neuroconnectivity. However, due to the complexities of autism and the lack of effective research method, the findings from neuroconnectivity studies have been inconsistent, leaving the exact significance of neuroconnectivity abnormalities in question. The newly emerging multidisciplinary brain network analysis methods have demonstrated considerable efficacy in comprehensively characterizing large-scale brain connectivity and advancing our understanding of human brain organization in both healthy subjects and clinical populations, thus bring new hope for the neuropathological study and early diagnosis of autism. Our preliminary work on structural brain connectivity network and oral language skills shows that some autistic brains adapt to compensate earlier underdevelopment with stronger local structural connectivity, and such stronger local connectivity is positively related to functional recovery of children with autism. The results indicate compensatory development and reorganization of brain in autism group, which provides useful extension to previous theories of connectivity deficit in autism. However, such work only focuses on structural connectivity network, without considering the dynamics of cortical circuits or functional connectivity. Therefore, in this proposal, we will study the dynamic functional connectivity and its correlation with structural connectivity, by involving MEG data, aiming to define the developmental connectivity patterns of autism and advance our understanding of the underlying mechanism of this disorder. Meanwhile, robust neuroimaging biomarkers will be identified for possible computer-aided diagnosis and therapeutic response prediction of autism.

自闭症是一种发病率较高的神经发育性疾病。越来越多的证据表明，自闭症患者的行为异常可能和神经连接缺陷相关。但由于病理的复杂和研究方法的匮乏，关于神经连接的研究结论并不统一，导致了人们对神经连接缺陷理论的重要性仍存在大量疑问。基于先进影像的脑网络分析方法使得人们可以系统的研究大脑神经连接的结构与功能状况，正成为神经性疾病的研究热点。申请者首次用弥散张量成像和图论，分析了自闭症患者的脑神经纤维连接网络，证明了神经纤维补偿性重组发育理论，为自闭症发病机理的研究和早期诊断提供了理论依据。但此分析主要是基于脑结构连接网络展开的，不能反映大脑活动的时域动态特性。因此，本项目提出通过引进脑磁图（MEG），研究脑功能连接网络及其与结构连接网络的关系，试图在脑网络水平上推进自闭症脑重组发育理论的补充和完善；同时利用机器学习的方法，筛选稳定的影像生物学标记，为自闭症的辅助诊断、疗效评价和预后判断打下基础。

自闭症是一种发病率较高的神经发育性疾病。越来越多的证据表明，自闭症患者的行为异常可能和神经连接缺陷相关。但由于病理的复杂和研究方法的匮乏，关于神经连接的研究结论并不统一，导致了人们对神经连接缺陷理论的重要性仍存在大量疑问。基于先进影像的脑网络分析方法使得人们可以系统的研究大脑神经连接的结构与功能状况，正成为神经性疾病的研究热点。本项目利用弥散张量成像和图论，分析了自闭症患者的脑神经纤维连接网络，证明了神经纤维补偿性重组发育理论，为自闭症发病机理的研究和早期诊断提供了理论依据；同时利用机器学习的方法，筛选了稳定的影像生物学标记，为自闭症的辅助诊断、疗效评价和预后判断打下基础。