用脑功能影像和历史感知图网络预测阿尔茨海默病进展。
Predicting Alzheimer's disease progression using rs-fMRI and a history-aware graph neural network
- 构建结合时序历史的图神经网络，处理不规则随访数据。
- 准确率达82.9%，对CN转MCI预测达68.8%。
- 适合早筛研究者与临床辅助诊断系统开发者。
阿尔茨海默病（AD）是影响美国超七百万人的神经退行性疾病，目前尚无治愈手段，但早期干预可延缓进展。本研究提出一种基于图神经网络（GNN）的模型，用于预测受试者在下次随访中是否将进入更严重认知障碍阶段。研究涵盖三阶段：认知正常（CN）、轻度认知障碍（MCI）和阿尔茨海默病（AD）。使用303名受试者、不同随访次数的静息态功能磁共振成像（rs-fMRI）数据构建功能连接图。模型引入循环神经网络（RNN）模块，可处理完整随访历史，并通过加入访视间隔信息应对不规则时间跨度。即使存在缺失访视，模型仍表现稳健。最终准确率达82.9%，其中对CN转MCI的预测准确率为68.8%，显著优于当前挑战水平。结果表明rs-fMRI在预测MCI或AD起始阶段具有高潜力，结合多模态数据可为早期干预提供可行方案。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a neurodegenerative disorder that affects more than seven million people in the United States alone. AD currently has no cure, but there are ways to potentially slow its progression if caught early enough. In this study, we propose a graph neural network (GNN)-based model for predicting whether a subject will transition to a more severe stage of cognitive impairment at their next clinical visit. We consider three stages of cognitive impairment in order of severity: cognitively normal (CN), mild cognitive impairment (MCI), and AD. We use functional connectivity graphs derived from resting-state functional magnetic resonance imaging (rs-fMRI) scans of 303 subjects, each with a different number of visits. Our GNN-based model incorporates a recurrent neural network (RNN) block, enabling it to process data from the subject's entire visit history. It can also work with irregular time gaps between visits by incorporating visit distance information into our input features. Our model demonstrates robust predictive performance, even with missing visits in the subjects' visit histories. It achieves an accuracy of 82.9%, with an especially impressive accuracy of 68.8% on CN to MCI conversions - a task that poses a substantial challenge in the field. Our results highlight the effectiveness of rs-fMRI in predicting the onset of MCI or AD and, in conjunction with other modalities, could offer a viable method for enabling timely interventions to slow the progression of cognitive impairment.
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