Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.

中文翻译：

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根据时间序列功能磁共振成像数据计算阿尔茨海默病的因果功能连接

功能连接已知阿尔茨海默病的大脑区域之间的变化会发生改变，并有望成为早期诊断的生物标志物。功能连接的几种方法获得了代表大脑区域之间随机关联（相关性）的无向网络。然而，关联并不一定意味着因果关系。相比之下，因果功能连接（CFC）信息更丰富，提供了代表大脑区域之间因果关系的有向网络。在本文中，我们从三个临床组的受试者的静息态功能磁共振成像（rs-fMRI）记录中获得了全脑的因果功能连接组：认知正常组、轻度认知障碍组和阿尔茨海默病组。我们应用最近开发的时间感知 PC (TPC) 算法来推断整个大脑的因果功能连接组。TPC 支持基于时间序列设置中的定向图形建模的全脑 CFC 的无模型估计。我们将 TPC 的 CFC 结果与文献中其他相关方法的结果进行了比较。然后，我们使用 TPC 的 CFC 结果，基于边缘方向，对阿尔茨海默病组和认知正常组之间的 CFC 边缘强度差异进行了探索性分析。p-韦尔奇获得的值t-测试。由此确定的大脑区域与临床/医疗机构研究人员发表的有关受阿尔茨海默病影响的大脑区域的文献一致。