Decoding and mapping task states of the human brain via deep learning

TL;DR

This paper introduces a deep neural network approach for decoding human brain task states from fMRI data, achieving high accuracy without manual feature selection, and demonstrating transfer learning capabilities on small datasets.

Contribution

The study presents a novel deep learning method that outperforms traditional SVM-based MVPA in decoding brain states directly from raw fMRI signals, eliminating the need for handcrafted features.

Findings

Achieved 93.7% accuracy on seven tasks with large dataset.

Demonstrated transfer learning with 89.0% and 94.7% accuracy on small datasets.

Outperformed SVM-MVPA in multiple classification tasks.

Abstract

Support vector machine (SVM) based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM-MVPA requires careful feature selection/extraction according to expert knowledge. In this study, we propose a deep neural network (DNN) for directly decoding multiple brain task states from fMRI signals of the brain without any burden for feature handcrafts. We trained and tested the DNN classifier using task fMRI data from the Human Connectome Project's S1200 dataset (N=1034). In tests to verify its performance, the proposed classification method identified seven tasks with an average accuracy of 93.7%. We also showed the general applicability of the DNN for transfer learning to small datasets (N=43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89.0% and 94.7% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2% and 68.6% obtained by the SVM-MVPA. A network visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers.