Learning to Model the Relationship Between Brain Structural and Functional Connectomes
Learning to Model the Relationship Between Brain Structural and Functional Connectomes
复制标题
DOI：
10.1109/tsipn.2022.3209097
复制
发表时间：
2021-12
影响因子：
3.2
通讯作者：
Yang Li;G. Mateos;Zhengwu Zhang
中科院分区：
文献类型：
--
作者：
Yang Li;G. Mateos;Zhengwu Zhang
最近的神经影像学进展以及从网络数据中统计学习的算法创新为整合大脑结构和功能提供了独特的途径，从而有助于在系统层面揭示大脑的一些组织原则。在这个方向上，我们开发了一个监督图表示学习框架，通过一个图编码器-解码器系统来模拟大脑结构连接（SC）和功能连接（FC）之间的关系，其中SC被用作预测经验FC的输入。可训练的图卷积编码器捕获模拟实际神经通信的大脑感兴趣区域之间的直接和间接相互作用，以及从结构网络拓扑和节点（即特定区域）属性集成信息。编码器学习节点级SC嵌入，这些嵌入结合起来生成（全脑）图级表示，用于重建经验FC网络。提出的端到端模型利用多目标损失函数共同重构FC网络，并学习sc到FC映射的判别图表示，用于下游主题（即图级）分类。综合实验表明，上述关系的习得表征从受试者大脑网络的内在属性中获取了有价值的信息，并提高了从人类连接组项目中对大量酗酒者和不饮酒者进行分类的准确性。我们的工作为大脑网络之间的关系提供了新的见解，支持了使用图表示学习来发现更多关于大脑功能的前景。
Recent neuroimaging advances along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level. In this direction, we develop a supervised graph representation learning framework to model the relationship between brain structural connectivity (SC) and functional connectivity (FC) via a graph encoder-decoder system, where the SC is used as input to predict empirical FC. A trainable graph convolutional encoder captures direct and indirect interactions between brain regions-of-interest that mimic actual neural communications, as well as to integrate information from both the structural network topology and nodal (i.e., region-specific) attributes. The encoder learns node-level SC embeddings which are combined to generate (whole brain) graph-level representations for reconstructing empirical FC networks. The proposed end-to-end model utilizes a multi-objective loss function to jointly reconstruct FC networks and learn discriminative graph representations of the SC-to-FC mapping for downstream subject (i.e., graph-level) classification. Comprehensive experiments demonstrate that the learnt representations of said relationship capture valuable information from the intrinsic properties of the subject's brain networks and lead to improved accuracy in classifying a large population of heavy drinkers and non-drinkers from the Human Connectome Project. Our work offers new insights on the relationship between brain networks that support the promising prospect of using graph representation learning to discover more about brain function.