标题： 针对神经认知障碍的临床轨迹预测的拓扑感知图增强技术 显示英文标题

标题： Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders

摘要： 基于静息态功能磁共振成像（fMRI）的大脑网络/图有助于通过测量大脑中的神经元活动来研究神经认知障碍的潜在病理生理学。 一些研究利用基于学习的方法进行大脑网络分析，但通常由于标记的fMRI数据稀缺而导致模型泛化能力低。 作为一种显著的自监督策略，图对比学习有助于利用辅助的未标记数据。 然而，现有的方法通常任意扰动图节点/边以生成增强图，而没有考虑大脑网络的基本拓扑信息。 为此，我们提出了一个拓扑感知图增强（TGA）框架，该框架包括一个预训练模型，用于在大规模未标记的fMRI队列上训练通用编码器，以及一个任务特定模型，用于在小规模目标数据集上执行下游任务。 在预训练模型中，我们设计了两种新颖的拓扑感知图增强策略：（1）优先保留脑枢纽区域的中心节点删除，以及（2）基于边权重的重要功能连接保留的依赖权重的边删除。 在1,688个fMRI扫描实验表明，TGA优于几种最先进的方法。 显示英文摘要

摘要： Brain networks/graphs derived from resting-state functional MRI (fMRI) help study underlying pathophysiology of neurocognitive disorders by measuring neuronal activities in the brain. Some studies utilize learning-based methods for brain network analysis, but typically suffer from low model generalizability caused by scarce labeled fMRI data. As a notable self-supervised strategy, graph contrastive learning helps leverage auxiliary unlabeled data. But existing methods generally arbitrarily perturb graph nodes/edges to generate augmented graphs, without considering essential topology information of brain networks. To this end, we propose a topology-aware graph augmentation (TGA) framework, comprising a pretext model to train a generalizable encoder on large-scale unlabeled fMRI cohorts and a task-specific model to perform downstream tasks on a small target dataset. In the pretext model, we design two novel topology-aware graph augmentation strategies: (1) hub-preserving node dropping that prioritizes preserving brain hub regions according to node importance, and (2) weight-dependent edge removing that focuses on keeping important functional connectivities based on edge weights. Experiments on 1, 688 fMRI scans suggest that TGA outperforms several state-of-the-art methods.