改进年龄预测：在多变量时间序列分析中利用基于LSTM的动态预测进行数据增强
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
AI总结：
针对神经影像数据集稀缺的问题，提出基于LSTM动态预测的数据增强框架，通过单步和递归预测ICNs时间过程扩展多变量时间序列数据，提升了年龄预测模型的性能。
摘要：
神经影像数据的高维度和复杂性要求使用大型数据集来开发鲁棒且高性能的深度学习模型。然而，神经影像领域明显受限于此类数据集的稀缺性。在本研究中，我们提出了一种数据增强与验证框架，该框架利用长短期记忆（LSTM）网络的动态预测来丰富数据集。我们通过以单步和递归两种配置预测独立成分网络（ICNs）的时间过程，对多变量时间序列数据进行了扩展。随后，我们使用多种为实足年龄预测任务设计的深度学习模型，将这些增强数据集的效果与原始数据进行了对比。结果表明，我们的方法提升了模型性能，为克服神经影像数据集规模有限带来的挑战提供了一种鲁棒的解决方案。
英文摘要：
The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models. However, the neuroimaging field is notably hampered by the scarcity of such datasets. In this work, we proposed a data augmentation and validation framework that utilizes dynamic forecasting with Long Short-Term Memory (LSTM) networks to enrich datasets. We extended multivariate time series data by predicting the time courses of independent component networks (ICNs) in both one-step and recursive configurations. The effectiveness of these augmented datasets was then compared with the original data using various deep learning models designed for chronological age prediction tasks. The results suggest that our approach improves model performance, providing a robust solution to overcome the challenges presented by the limited size of neuroimaging datasets.
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