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利用静息态功能磁共振成像衍生的定向脑网络研究阿尔茨海默病中的局灶性连接缺陷

Investigating Focal Connectivity Deficits in Alzheimer's Disease Using Directional Brain Networks Derived from Resting-State fMRI.

作者信息

Zhao Sinan, Rangaprakash D, Venkataraman Archana, Liang Peipeng, Deshpande Gopikrishna

机构信息

AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn UniversityAuburn, AL, United States.

Department of Psychiatry and Biobehavioral Sciences, University of California, Los AngelesLos Angeles, CA, United States.

出版信息

Front Aging Neurosci. 2017 Jul 6;9:211. doi: 10.3389/fnagi.2017.00211. eCollection 2017.

Abstract

Connectivity analysis of resting-state fMRI has been widely used to identify biomarkers of Alzheimer's disease (AD) based on brain network aberrations. However, it is not straightforward to interpret such connectivity results since our understanding of brain functioning relies on regional properties (activations and morphometric changes) more than connections. Further, from an interventional standpoint, it is easier to modulate the activity of regions (using brain stimulation, neurofeedback, etc.) rather than connections. Therefore, we employed a novel approach for identifying focal directed connectivity deficits in AD compared to healthy controls. In brief, we present a model of directed connectivity (using Granger causality) that characterizes the coupling among different regions in healthy controls and Alzheimer's disease. We then characterized group differences using a (between-subject) generative model of pathology, which generates latent connectivity variables that best explain the (within-subject) directed connectivity. Crucially, our generative model at the second (between-subject) level explains connectivity in terms of local or regionally specific abnormalities. This allows one to explain disconnections among multiple regions in terms of regionally specific pathology; thereby offering a target for therapeutic intervention. Two foci were identified, locus coeruleus in the brain stem and right orbitofrontal cortex. Corresponding disrupted connectivity network associated with the foci showed that the brainstem is the critical focus of disruption in AD. We further partitioned the aberrant connectomic network into four unique sub-networks, which likely leads to symptoms commonly observed in AD. Our findings suggest that fMRI studies of AD, which have been largely cortico-centric, could in future investigate the role of brain stem in AD.

摘要

静息态功能磁共振成像的连通性分析已被广泛用于基于脑网络异常来识别阿尔茨海默病(AD)的生物标志物。然而,由于我们对大脑功能的理解更多地依赖于区域特性(激活和形态测量变化)而非连接,因此解释此类连通性结果并非易事。此外,从干预的角度来看,调节区域的活动(使用脑刺激、神经反馈等)比调节连接更容易。因此,与健康对照相比,我们采用了一种新颖的方法来识别AD中的局灶性定向连通性缺陷。简而言之,我们提出了一种定向连通性模型(使用格兰杰因果关系),该模型表征了健康对照和阿尔茨海默病中不同区域之间的耦合。然后,我们使用病理学的(受试者间)生成模型来表征组间差异,该模型生成最能解释(受试者内)定向连通性的潜在连通性变量。至关重要的是,我们在第二个(受试者间)层面的生成模型根据局部或区域特异性异常来解释连通性。这使得人们能够根据区域特异性病理学来解释多个区域之间的断开连接;从而为治疗干预提供了一个靶点。确定了两个病灶,即脑干中的蓝斑和右侧眶额皮质。与病灶相关的相应连通性网络中断表明,脑干是AD中断的关键病灶。我们进一步将异常的连接组网络划分为四个独特的子网,这可能导致AD中常见的症状。我们的研究结果表明,以往主要以皮质为中心的AD功能磁共振成像研究,未来可能会研究脑干在AD中的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd5c/5498531/0e9058adfcd6/fnagi-09-00211-g0001.jpg

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