Improving age prediction: Utilizing LSTM-based dynamic forecasting for
  data augmentation in multivariate time series analysis
Yutong Gao Charles A. Ellis Vince D. Calhoun Robyn L. Miller
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.