School of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai 200433, PR China.
Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Oxford Centre for Computational Neuroscience, Oxford, UK.
Neuroimage. 2017 Mar 1;148:169-178. doi: 10.1016/j.neuroimage.2016.12.068. Epub 2016 Dec 28.
A powerful new method is described called Knowledge based functional connectivity Enrichment Analysis (KEA) for interpreting resting state functional connectivity, using circuits that are functionally identified using search terms with the Neurosynth database. The method derives its power by focusing on neural circuits, sets of brain regions that share a common biological function, instead of trying to interpret single functional connectivity links. This provides a novel way of investigating how task- or function-related networks have resting state functional connectivity differences in different psychiatric states, provides a new way to bridge the gap between task and resting-state functional networks, and potentially helps to identify brain networks that might be treated. The method was applied to interpreting functional connectivity differences in autism. Functional connectivity decreases at the network circuit level in 394 patients with autism compared with 473 controls were found in networks involving the orbitofrontal cortex, anterior cingulate cortex, middle temporal gyrus cortex, and the precuneus, in networks that are implicated in the sense of self, face processing, and theory of mind. The decreases were correlated with symptom severity.
一种强大的新方法被描述为基于知识的功能连接富集分析(KEA),用于解释静息状态功能连接,使用使用神经综合数据库中的搜索词功能识别的电路。该方法通过专注于神经回路(共享共同生物学功能的脑区集合)而获得其力量,而不是试图解释单个功能连接链接。这为研究任务或功能相关网络在不同精神状态下的静息状态功能连接差异提供了一种新方法,为弥合任务和静息状态功能网络之间的差距提供了一种新方法,并可能有助于识别可能需要治疗的大脑网络。该方法应用于解释自闭症中的功能连接差异。与 473 名对照相比,在涉及眶额皮质、前扣带皮质、中颞叶皮质和楔前叶的网络中,在涉及自我意识、面部处理和心理理论的网络中,394 名自闭症患者的网络电路水平的功能连接减少,这些减少与症状严重程度相关。