用生成模型预测脑活动，提升阿尔茨海默病分类与解释能力
Generative forecasting of brain activity enhances Alzheimer's classification and interpretation
- 通过LSTM和BrainLM生成脑网络时间序列，实现数据增强
- 生成数据使阿尔茨海默病分类准确率显著提升
- 可识别与阿尔茨海默病相关的特异性脑网络敏感性
通过纯数据驱动方法理解认知与内在脑活动的关系仍是神经科学的重大挑战。静息态功能磁共振成像（rs-fMRI）提供了一种非侵入性监测区域神经活动的方法，生成丰富复杂的时空数据结构。深度学习在捕捉这些复杂表征方面展现出潜力。然而，由于大规模数据集的有限性，尤其是阿尔茨海默病（AD）等疾病特定群体的数据稀缺，制约了深度学习模型的泛化能力。本研究聚焦于从rs-fMRI提取的独立成分网络的多变量时间序列预测，作为数据增强手段，采用传统LSTM模型与新型Transformer-based BrainLM模型。我们评估其在阿尔茨海默病分类中的有效性，证明生成预测能提升分类性能。对BrainLM的后验解释揭示了与AD相关的类别特异性脑网络敏感性。
原文摘要 · Abstract (English)
Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor regional neural activity, providing a rich and complex spatiotemporal data structure. Deep learning has shown promise in capturing these intricate representations. However, the limited availability of large datasets, especially for disease-specific groups such as Alzheimer's Disease (AD), constrains the generalizability of deep learning models. In this study, we focus on multivariate time series forecasting of independent component networks derived from rs-fMRI as a form of data augmentation, using both a conventional LSTM-based model and the novel Transformer-based BrainLM model. We assess their utility in AD classification, demonstrating how generative forecasting enhances classification performance. Post-hoc interpretation of BrainLM reveals class-specific brain network sensitivities associated with AD.
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