IntroductionThe blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data.MethodsIn this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics.ResultsWe theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement.ConclusionOverall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation.

中文翻译：

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具有多分辨率一致性损失的 U 形卷积变压器 GAN，用于恢复大脑功能时间序列和痴呆症诊断

简介源自功能神经影像的血氧水平依赖性（BOLD）信号通常用于脑网络分析和痴呆症诊断。在分析神经系统疾病时，丢失 BOLD 信号可能会导致表现不佳和对结果的误解。很少有研究关注大脑功能时间序列数据的恢复。方法在本文中，一种新颖的方法U提出了形卷积变压器GAN（UCT-GAN）模型来恢复丢失的大脑功能时间序列数据。所提出的模型利用了生成对抗网络（GAN）的力量，同时结合了U形架构，可有效捕捉修复过程中的层次特征。此外，基于变压器的生成器中的多级时间相关注意力和卷积采样被设计为捕获缺失时间序列的全局和局部时间特征，并将它们与其他大脑区域的远程关系相关联。此外，通过引入多分辨率一致性损失，所提出的模型可以促进不同时间模式的学习并保持不同时间分辨率之间的一致性，从而有效地恢复复杂的大脑功能动态。结果我们在公共阿尔茨海默氏病神经影像计划上理论上测试了我们的模型（ ADNI）数据集，我们的实验表明，所提出的模型在定量指标和定性评估方面都优于现有方法。该模型在恢复过程中保留大脑功能网络的底层拓扑结构的能力是一项特别值得注意的成就。结论总体而言，所提出的模型为恢复大脑功能时间序列提供了一种有前途的解决方案，并通过提供增强的工具为神经科学研究的进步做出了贡献用于疾病分析和解释。