An Overview of Identification Methods on Human Brain Effective Connectivity Networks Based on Functional Magnetic Resonance Imaging
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摘要: 人脑效应连接网络刻画了脑区间神经活动的因果效应. 对不同人群的脑效应连接网络进行研究不仅能为神经精神疾病病理机制的理解提供新视角, 而且能为疾病的早期诊断和治疗评价提供新的脑网络影像学标记, 具有十分重要的理论意义和应用价值. 利用计算方法从功能磁共振成像(Functional magnetic resonance imaging, fMRI)数据中识别脑效应连接网络是目前人脑连接组学中一项重要的研究课题. 本文首先概括了从fMRI数据中进行脑效应连接网络识别的主要流程, 说明了其中的主要步骤和方法; 然后, 给出了一种脑效应连接网络识别方法的分类体系, 并对其中一些代表性的识别算法进行了阐述; 最后, 通过对该领域挑战性问题的分析, 预测了脑效应连接网络识别未来的研究方向, 以期对相关研究提供一定的参考.Abstract: The brain effective connectivity networks characterize the causal interactions of neural activity between brain regions. Researches on brain effective connectivity networks of different populations can not only provide a new perspective for understanding the pathological mechanism of neuropsychiatric diseases, but also provide novel brain network imaging markers for the early diagnosis and evaluation for treatment of diseases, thus have very important theoretical and practical value. Using computational approaches to identify brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data is currently an important subject in the human brain connectome. This paper firstly summarizes a workflow of identifying brain effective connectivity networks from fMRI data and illustrates its main processes and methods. Next, a comprehensive category system of identifying brain effective connectivity networks is presented, and several typical identifying algorithms in each category are described. Finally, by analyzing challenging problems in this area, we predict the further research directions in identifying brain effective connectivity networks and hope to present some references for related researches.
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图 1 人脑效应连接网络识别的流程
Fig. 1 The process of human brain effective connectivity networks identification
图 2 人脑效应连接网络识别方法的分类体系
Fig. 2 The category system for identification methods of human brain effective connectivity networks
图 3 神经生理学响应信号映射为BOLD响应信号的过程
Fig. 3 The process of mapping a neurophysiological response signal to a BOLD response signal
图 4 基于OUM方法识别脑效应连接网络的流程图
Fig. 4 The process of identifying a brain effective connectivity network by the OUM method
图 5 基于贝叶斯网络评分搜索方法识别脑效应连接网络结构的流程图
Fig. 5 The process of identifying a brain effective connectivity network structure by the Bayesian network scoring search method
表 1 人脑效应连接网络的部分典型识别方法对比
Table 1 The comparisons of several typical identification methods on human brain effective connectivity networks
方法 年份 类别 个体水平/组水平 探索型/验证型 主要优缺点及适用场景 uSEM[55] 2007 基于结构方程模型 个体水平 验证型 适用于组块设计实验, 能够识别同期和滞后的因果效应, 但不能用于事件相关的实验设计 euSEM[56] 2011 基于结构方程模型 个体水平、组水平 验证型、探索型 适用于事件相关设计实验, 不仅识别了同期和滞后的因果效应, 而且描述了外界刺激对脑区神经活动的直接影响和间接调节作用, 在个体水平和组水平上都获得了较好的识别效果 GIMME[58] 2012 基于结构方程模型 个体水平、组水平 验证型、探索型 从高度异质数据中准确地识别了组水平和个体水平的效应连接网络, 且具有良好的灵活性和可扩展性 cs-GIMME[59] 2019 基于结构方程模型 个体水平、组水平 验证型、探索型 与 GIMME 相比, 具有更强地识别组间差异的能力, 但不适于识别大规模脑效应连接网络 spDCM[62] 2014 基于动态因果模型 组水平 验证型 用于从静息态 fMRI 数据中识别高精度的效应连接网络, 对组间差异敏感, 不足在于仅考虑了较小规模的脑区, 且忽略了效应连接的动态性 rDCM[67] 2017 基于动态因果模型 个体水平 验证型 适用于识别较大规模的脑效应连接网络 spare rDCM[68] 2018 基于动态因果模型 个体水平、组水平 验证型 用于准确、高效地识别全脑的效应连接网络, 且对组间差异敏感 文献 [46] 2018 基于动态因果模型 个体水平、组水平 验证型 用于识别动态效应连接网络, 方法具有良好的可扩展性 文献 [71] 2016 基于 OUM 组水平 验证型 适用于识别较大规模的效应连接网络, 但滞后信息可能对因果效应的识别造成混淆 文献 [72] 2018 基于 OUM 组水平 验证型 利用零时滞的功能连接识别效应连接网络, 避免了滞后信息的不良影响, 但方法的鲁棒性较差 AIAEC[44] 2016 基于静态 BN 评分搜索 组水平 探索型 能够准确地识别脑区间的连接方向, 且在大规模脑区上也具有良好的性能, 缺陷是不能计算连接强度 ACOEC[45] 2016 基于静态 BN 评分搜索 组水平 探索型 能够识别连接方向并计算连接强度, 且具有良好的精度和鲁棒性, 但方法的时间复杂度较高 ACOMMEC[79] 2019 基于静态 BN 评分搜索 组水平 探索型 利用多源信息识别脑效应连接网络, 比仅利用 fMRI 数据获得了更高的求解质量和计算效率, 但不能模拟人脑的循环机制 GDBN[80] 2014 基于动态 BN 评分搜索 组水平 探索型 能够识别具有反馈机制的效应连接网络, 但该方法仅采用了一阶马尔科夫链 DBN 模型, 未考虑多个不同时刻的循环交互作用 HO-DBN-DP[81] 2017 基于动态 BN 评分搜索 组水平 探索型 能够高效地识别多个不同时刻的脑区间具有循环和反馈机制的因果效应连接 TB-based score[82] 2018 基于动态 BN 评分搜索 组水平 探索型 基于融合 fMRI 和 DTI 信息的评分函数学习网络结构, 在精度和鲁棒性上均获得了良好性能, 但不能有效地处理数据缺失问题 SVAR[92] 2015 基于格兰杰因果 组水平 探索型 利用因子降维策略获得了较高的计算效率, 适用于识别较大规模的效应连接网络, 不足在于因子模型对一些特殊脑区不适用 文献 [95] 2016 基于格兰杰因果 组水平 探索型 结合了时、频域格兰杰因果的优势, 获得了更高的准确性, 但方法对模型的阶数敏感 MKGC[90] 2017 基于格兰杰因果 组水平 探索型 适用于捕捉脑区间非线性的因果效应, 具有较强的识别出脑区之间连接的能力 lsGC[97] 2019 基于格兰杰因果 组水平 探索型 能高效地识别大规模脑区间的效应连接网络(在90、138 和 246 个节点的大规模脑区上验证了有效性) P-correlation[100] 2017 基于预测相关性 组水平 探索型 在数据非平稳的情况下能够准确地识别每对脑区间的因果效应, 具有灵敏度高、鲁棒性强的特点 GS[101] 2002 基于相空间重构 组水平 探索型 适用于识别每对脑区间的连接方向, 但有时三个指标的方向判断结果不一致 MCA[102] 2018 基于相空间重构 组水平 探索型 用于识别每对脑区间非线性的因果效应, 且在样本量较小时仍具有较高的准确率 文献 [105] 2019 基于概率分布 组水平 探索型 综合考虑了 BOLD 信号多方面的信息, 在每对脑区间方向的识别上具有良好的鲁棒性和准确性 CDD[106] 2019 基于概率分布 组水平 探索型 无需严格假设数据的分布, 能够灵活、准确地度量每对脑区间的非线性因果关系 
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