IEEE Trans Biomed Eng. 2018 May;65(5):1057-1068. doi: 10.1109/TBME.2017.2738035. Epub 2017 Aug 10.
Effective connectivity (EC) is the methodology for determining functional-integration among the functionally active segregated regions of the brain. By definition EC is "the causal influence exerted by one neuronal group on another" which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure-function relationship using multimodal imaging studies, till date only a few studies have done joint modeling of the two modalities: functional MRI (fMRI) and diffusion tensor imaging (DTI). We aim to propose a unified probabilistic framework that combines information from both sources to learn EC using dynamic Bayesian networks (DBNs).
DBNs are probabilistic graphical temporal models that learn EC in an exploratory fashion. Specifically, we propose a novel anatomically informed (AI) score that evaluates fitness of a given connectivity structure to both DTI and fMRI data simultaneously. The AI score is employed in structure learning of DBN given the data.
Experiments with synthetic-data demonstrate the face validity of structure learning with our AI score over anatomically uninformed counterpart. Moreover, real-data results are cross-validated by performing classification-experiments.
EC inferred on real fMRI-DTI datasets is found to be consistent with previous literature and show promising results in light of the AC present as compared to other classically used techniques such as Granger-causality.
Multimodal analyses provide a more reliable basis for differentiating brain under abnormal/diseased conditions than the single modality analysis.
有效连通性（EC）是确定大脑中功能活跃分离区域之间功能整合的方法。根据定义，EC 是“一个神经元群对另一个神经元群施加的因果影响”，它受到解剖连通性（AC）（轴突连接）的限制。AC 是 EC 的必要条件，但不能完全决定 EC，因为突触通讯是以动态的、上下文依赖的方式发生的。尽管使用多模态成像研究已经有大量关于结构-功能关系的新兴证据，但迄今为止，只有少数研究同时对两种模态进行了联合建模：功能磁共振成像（fMRI）和弥散张量成像（DTI）。我们旨在提出一个统一的概率框架，该框架结合了来自两种来源的信息，使用动态贝叶斯网络（DBN）来学习 EC。
DBN 是概率图形时间模型，以探索性的方式学习 EC。具体来说，我们提出了一种新颖的解剖学信息（AI）评分，该评分同时评估给定连通结构对 DTI 和 fMRI 数据的适应性。在给定数据的情况下，AI 评分用于 DBN 的结构学习。
使用合成数据的实验证明了我们的 AI 评分在结构学习方面的有效性，优于解剖学上无信息的对应物。此外，通过进行分类实验，对真实数据的结果进行了交叉验证。
在真实的 fMRI-DTI 数据集上推断出的 EC 与之前的文献一致，并与其他经典使用的技术（如 Granger 因果关系）相比，在考虑到存在的 AC 时，表现出有前景的结果。
与单模态分析相比，多模态分析为区分异常/患病大脑提供了更可靠的基础。
