Li Lanting, Wen Guangqi, Cao Peng, Liu Xiaoli, R Zaiane Osmar, Yang Jinzhu
College of Computer Science and Engineering, Northeastern University, Shenyang, China.
Key Laboratory of Intelligent Computing in Medical Image, Northeastern University, Shenyang, China.
Int J Comput Assist Radiol Surg. 2023 Apr;18(4):663-673. doi: 10.1007/s11548-022-02780-3. Epub 2022 Nov 4.
Finding the biomarkers associated with autism spectrum disorder (ASD) is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatments. In essence, we are faced with two challenges (i) how to learn a node representation and a clean graph structure from original graph data with high dimensionality and (ii) how to jointly model the procedure of node representation learning, structure learning and graph classification.
We propose FSL-BrainNet, an interpretable graph convolution network (GCN) model for jointly Learning of node Features and clean Structures in brain networks for automatic brain network classification and interpretation. We formulate an end-to-end trainable and interpretable framework for graph classification and biomarkers (salient brain regions and potential subnetworks) identification.
The experimental results on the ABIDE dataset show that our proposed methods not only achieve improved prediction performance compared with the state-of-the-art methods, but also find a compact set of highly suggestive biomarkers including relevant brain regions and subnetworks to ASD.
Through node feature learning and structure learning, our model can simultaneously select important brain regions and identify subnetworks.
寻找与自闭症谱系障碍（ASD）相关的生物标志物有助于理解该疾病的潜在根源，并能实现早期诊断和更具针对性的治疗。本质上，我们面临两个挑战：（i）如何从高维原始图数据中学习节点表示和清晰的图结构；（ii）如何对节点表示学习、结构学习和图分类过程进行联合建模。
我们提出了FSL-BrainNet，这是一种可解释的图卷积网络（GCN）模型，用于在脑网络中联合学习节点特征和清晰结构，以实现自动脑网络分类和解释。我们为图分类和生物标志物（显著脑区和潜在子网络）识别制定了一个端到端可训练且可解释的框架。
在ABIDE数据集上的实验结果表明，我们提出的方法不仅与现有最先进方法相比取得了更好的预测性能，还发现了一组紧凑的、极具启发性的生物标志物，包括与ASD相关的脑区和子网络。
通过节点特征学习和结构学习，我们的模型可以同时选择重要脑区并识别子网络。
