Brain decoding from functional MRI using long short-term memory recurrent neural networks

TL;DR

This paper introduces a deep learning framework using LSTM recurrent neural networks to improve brain state decoding from fMRI data by capturing temporal dependencies, outperforming traditional models.

Contribution

The study develops a novel LSTM-based decoding model that leverages subject-specific functional profiles for more accurate brain state classification.

Findings

LSTM models outperform conventional decoding methods in accuracy.

Functional profiles based on intrinsic networks improve decoding performance.

The approach effectively distinguishes different cognitive task states.

Abstract

Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies. Promising performance has been achieved using brain functional connectivity or brain activation signatures for a variety of brain decoding tasks. However, most of existing studies have built decoding models upon features extracted from imaging data at individual time points or temporal windows with a fixed interval, which might not be optimal across different cognitive processes due to varying temporal durations and dependency of different cognitive processes. In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). Particularly, functional profiles extracted from task functional imaging data based on their corresponding subject-specific intrinsic functional networks are used as features to build brain decoding models, and LSTM RNNs are adopted to learn decoding mappings between functional profiles and brain states. We evaluate the proposed method using task fMRI data from the HCP dataset, and experimental results have demonstrated that the proposed method could effectively distinguish brain states under different task events and obtain higher accuracy than conventional decoding models.