Brain structural connectivity, capturing the white matter fiber tracts among brain regions inferred by diffusion MRI (dMRI), provides a unique characterization of brain anatomical organization. One fundamental question to address with structural connectivity is how to properly summarize and perform statistical inference for a group-level connectivity architecture, for instance, under different sex groups, or disease cohorts. Existing analyses commonly summarize group-level brain connectivity by a simple entry-wise sample mean or median across individual brain connectivity matrices. However, such a heuristic approach fully ignores the associations among structural connections and the topological properties of brain networks. In this project, we propose a latent space-based generative network model to estimate group-level brain connectivity. Within our modeling framework, we incorporate the anatomical information of brain regions as the attributes of nodes to enhance the plausibility of our estimation and improve biological interpretation. We name our method the attributes-informed brain connectivity (ABC) model, which compared with existing group-level connectivity estimations, (1) offers an interpretable latent space representation of the group-level connectivity, (2) incorporates the anatomical knowledge of nodes and tests its co-varying relationship with connectivity and (3) quantifies the uncertainty and evaluates the likelihood of the estimated group-level effects against chance. We devise a novel Bayesian MCMC algorithm to estimate the model. We evaluate the performance of our model through extensive simulations. By applying the ABC model to study brain structural connectivity stratified by sex among Alzheimer’s Disease (AD) subjects and healthy controls incorporating the anatomical attributes (volume, thickness and area) on nodes, our method shows superior predictive power on out-of-sample structural connectivity and identifies meaningful sex-specific network neuromarkers for AD.

中文翻译：

---

在潜在空间建模下结合解剖学知识建立群体水平的大脑结构连接


大脑结构连接，捕获弥散 MRI （dMRI） 推断的大脑区域之间的白质纤维束，提供了大脑解剖组织的独特特征。结构连接需要解决的一个基本问题是，如何正确总结和执行组级连接架构的统计推断，例如，在不同性别群体或疾病队列下。现有分析通常通过单个大脑连接矩阵的简单入口样本平均值或中位数来总结群体水平的大脑连接。然而，这种启发式方法完全忽略了结构连接之间的关联和大脑网络的拓扑特性。在这个项目中，我们提出了一个基于潜在空间的生成网络模型来估计群体层面的大脑连接。在我们的建模框架中，我们将大脑区域的解剖信息作为节点的属性，以提高我们估计的合理性并改进生物学解释。我们将我们的方法命名为属性知情脑连接 （ABC） 模型，与现有的组级连接估计相比，（1） 提供了组级连接的可解释潜在空间表示，（2） 整合了节点的解剖学知识并测试其与连接的协变关系，以及 （3） 量化不确定性并评估估计的组级效应与机会的可能性。我们设计了一种新的贝叶斯 MCMC 算法来估计模型。我们通过广泛的模拟来评估模型的性能。 通过应用 ABC 模型来研究阿尔茨海默病 （AD） 受试者和健康对照者之间按性别分层的大脑结构连接，并结合节点上的解剖属性（体积、厚度和面积），我们的方法对样本外结构连接显示出卓越的预测能力，并确定了 AD 的有意义的性别特异性网络神经标志物。