Jiang Xi, Zhu Dajiang, Li Kaiming, Zhang Tuo, Wang Lihong, Shen Dinggang, Guo Lei, Liu Tianming
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA, USA.
Brain Imaging Behav. 2014 Dec;8(4):542-57. doi: 10.1007/s11682-013-9280-x.
Due to the difficulties in establishing correspondences between functional regions across individuals and populations, systematic elucidation of functional connectivity alterations in mild cognitive impairment (MCI) in comparison with normal controls (NC) is still a challenging problem. In this paper, we assessed the functional connectivity alterations in MCI via novel, alternative predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. First, ICA-clustering was used to construct RSNs from R-fMRI data in NC group. Second, since the RSNs in MCI are already altered and can hardly be constructed directly from R-fMRI data, structural landmarks derived from DTI data were employed as the predictive models of RSNs for MCI. Third, given that the landmarks are structurally consistent and correspondent across NC and MCI, functional connectivities in MCI were assessed based on the predicted RSNs and compared with those in NC. Experimental results demonstrated that the predictive models of RSNs based on multimodal R-fMRI and DTI data systematically and comprehensively revealed widespread functional connectivity alterations in MCI in comparison with NC.
由于在个体和群体之间建立功能区域对应关系存在困难，与正常对照（NC）相比，系统地阐明轻度认知障碍（MCI）中的功能连接改变仍然是一个具有挑战性的问题。在本文中，我们通过从多模态静息态功能磁共振成像（R-fMRI）和扩散张量成像（DTI）数据中学习到的静息态网络（RSN）的新型替代预测模型，评估了MCI中的功能连接改变。首先，使用独立成分分析聚类（ICA-clustering）从NC组的R-fMRI数据中构建RSN。其次，由于MCI中的RSN已经改变，很难直接从R-fMRI数据中构建，因此将从DTI数据中得出的结构标志物用作MCI的RSN预测模型。第三，鉴于这些标志物在NC和MCI中在结构上是一致且对应的，基于预测的RSN评估了MCI中的功能连接，并与NC中的功能连接进行了比较。实验结果表明，基于多模态R-fMRI和DTI数据的RSN预测模型系统且全面地揭示了与NC相比MCI中广泛的功能连接改变。
