用脑图神经网络预测中风后失语症患者语言能力，效果优于传统方法。
A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia
- 基于脑区连接与病灶信息构建图模型，融合功能相似性提升预测精度
- 在多个数据集上表现更优，跨扫描协议仍保持稳定性能
- 适合临床神经康复评估，为个性化治疗提供量化支持
我们提出一种病灶感知的边基础图神经网络（LEGNet），用于从卒中后失语症患者的静息态功能磁共振成像（rs-fMRI）连接性预测语言能力。模型包含三个组件：编码脑区间功能连接的边基础学习模块、病灶编码模块，以及利用功能相似性进行子图学习的模块。采用人类连接组计划（HCP）生成的合成数据进行超参数调优和预训练，随后在自建的卒中后失语症神经影像数据集上通过重复10折交叉验证评估性能。结果表明，LEGNet在预测语言能力方面优于基线深度学习方法；在另一套略有不同扫描协议的自建数据集上也表现出更优的泛化能力。研究结果表明，LEGNet能有效学习rs-fMRI连接性与语言能力之间的关系，为卒中后失语症的评估提供新工具。
原文摘要 · Abstract (English)
We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model integrates three components: an edge-based learning module that encodes functional connectivity between brain regions, a lesion encoding module, and a subgraph learning module that leverages functional similarities for prediction. We use synthetic data derived from the Human Connectome Project (HCP) for hyperparameter tuning and model pretraining. We then evaluate the performance using repeated 10-fold cross-validation on an in-house neuroimaging dataset of post-stroke aphasia. Our results demonstrate that LEGNet outperforms baseline deep learning methods in predicting language ability. LEGNet also exhibits superior generalization ability when tested on a second in-house dataset that was acquired under a slightly different neuroimaging protocol. Taken together, the results of this study highlight the potential of LEGNet in effectively learning the relationships between rs-fMRI connectivity and language ability in a patient cohort with brain lesions for improved post-stroke aphasia evaluation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。