Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis

TL;DR

This paper introduces a novel data augmentation framework using LSTM-based dynamic forecasting to enhance multivariate neuroimaging datasets, significantly improving age prediction models in the context of limited data availability.

Contribution

The study presents a new LSTM-based data augmentation method for multivariate time series, specifically tailored to neuroimaging data, improving age prediction accuracy.

Findings

Augmented datasets led to better age prediction performance.

LSTM-based forecasting effectively enriches neuroimaging data.

The approach addresses data scarcity in neuroimaging studies.

Abstract

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.