Deep learning of fMRI big data: a novel approach to subject-transfer decoding

TL;DR

This paper introduces the first successful deep neural network-based subject-transfer decoder for fMRI data, achieving higher accuracy than traditional methods and visualizing universal brain features.

Contribution

It presents a novel DNN-based approach for subject-transfer decoding in fMRI data, overcoming individual variability limitations.

Findings

DNN-based decoder outperforms SVM in accuracy

Successful visualization of subject-independent features

First application of deep learning for universal brain decoding

Abstract

As a technology to read brain states from measurable brain activities, brain decoding are widely applied in industries and medical sciences. In spite of high demands in these applications for a universal decoder that can be applied to all individuals simultaneously, large variation in brain activities across individuals has limited the scope of many studies to the development of individual-specific decoders. In this study, we used deep neural network (DNN), a nonlinear hierarchical model, to construct a subject-transfer decoder. Our decoder is the first successful DNN-based subject-transfer decoder. When applied to a large-scale functional magnetic resonance imaging (fMRI) database, our DNN-based decoder achieved higher decoding accuracy than other baseline methods, including support vector machine (SVM). In order to analyze the knowledge acquired by this decoder, we applied principal sensitivity analysis (PSA) to the decoder and visualized the discriminative features that are common to all subjects in the dataset. Our PSA successfully visualized the subject-independent features contributing to the subject-transferability of the trained decoder.