Functional Connectivity Prediction With Deep Learning for Graph Transformation
Functional Connectivity Prediction With Deep Learning for Graph Transformation
复制标题
DOI：
10.1109/tnnls.2022.3197337
复制
发表时间：
2022-08
影响因子：
10.4
通讯作者：
Negar Etemadyrad;Yuyang Gao;Qingzhe Li;Xiaojie Guo;F. Krueger;Qixiang Lin;D. Qiu;Liang Zhao
中科院分区：
文献类型：
--
作者：
Negar Etemadyrad;Yuyang Gao;Qingzhe Li;Xiaojie Guo;F. Krueger;Qixiang Lin;D. Qiu;Liang Zhao
从解剖学的大脑接线（SC）中推断出静止状态的功能连通性（FC）在理解生物学神经元网络和精神疾病的神经科学中具有巨大的意义SC和FC的重要性，直到最近，关于该主题的迅速发展的研究机构通常集中于线性模型或对FC和SC之间的映射的简单假设，但是FC和SC之间的关系实际上是高度非线性和复杂性的，并且包含了可观的随机性，也可能会对受试者的年龄和健康产生显着影响；无法忽略这些挑战，在这里，我们开发了一个新颖的sc-fc通用对手网络（SF-GAN）框架，以将SC映射到FC，以及基于新提出的基于图形的基于中性网络的通用模型，该模型能够详细地划分，该模型是范围的。然后，使用新的边缘卷积层的形式将其解码为FC的其他元数据，并将其整合到图表中。 FC通过新的多级边缘 - 相关引导的图形聚类问题。
Inferring resting-state functional connectivity (FC) from anatomical brain wiring, known as structural connectivity (SC), is of enormous significance in neuroscience for understanding biological neuronal networks and treating mental diseases. Both SC and FC are networks where the nodes are brain regions, and in SC, the edges are the physical fiber nerves among the nodes, while in FC, the edges are the nodes’ coactivation relations. Despite the importance of SC and FC, until very recently, the rapidly growing research body on this topic has generally focused on either linear models or computational models that rely heavily on heuristics and simple assumptions regarding the mapping between FC and SC. However, the relationship between FC and SC is actually highly nonlinear and complex and contains considerable randomness; additional factors, such as the subject’s age and health, can also significantly impact the SC-FC relationship and hence cannot be ignored. To address these challenges, here, we develop a novel SC-to-FC generative adversarial network (SF-GAN) framework for mapping SC to FC, along with additional metafeatures based on a newly proposed graph neural network-based generative model that is capable of learning the stochasticity. Specifically, a new graph-based conditional generative adversarial nets model is proposed, where edge convolution layers are leveraged to encode the graph patterns in the SC in the form of a graph representation. New edge deconvolution layers are then utilized to decode the representation back to FC. Additional metafeatures of subjects’ profile information are integrated into the graph representation with newly designed sparse-regularized layers that can automatically select features that impact FC. Finally, we have also proposed new post hoc explainer of our SF-GAN, which can identify which subgraphs in SC strongly influence which subgraphs in FC by a new multilevel edge-correlation-guided graph clustering problem. The results of experiments conducted to test the new model confirm that it significantly outperforms existing state-of-the-art methods, with additional interpretability for identifying important metafeatures and subgraphs.