AUTHOR=Hashimoto Yuki , Ogata Yousuke , Honda Manabu , Yamashita Yuichi 

TITLE=Deep Feature Extraction for Resting-State Functional MRI by Self-Supervised Learning and Application to Schizophrenia Diagnosis

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.696853

DOI=10.3389/fnins.2021.696853

ISSN=1662-453X

ABSTRACT=In this study, we propose a novel deep-learning technique for functional MRI analysis. We introduced an "identity feature" by a self-supervised learning schema in which a neural network is trained solely based on the MRI-scans; furthermore, training does not require any explicit labels. The proposed method demonstrated that each temporal slice of resting state functional MRI contains enough information to identify the subject. The network learned a feature space in which the features were clustered per subject for the test data as well as for the training data; this is unlike the features extracted by conventional methods including region of interests pooling signals and principal component analysis. In addition, using a simple linear classifier for the identity features, we demonstrated that the extracted features could contribute to schizophrenia diagnosis. The classification accuracy of our identity features was higher than that of the conventional functional connectivity. Our results suggested that our proposed training scheme of the neural network captured brain functioning related to the diagnosis of psychiatric disorders as well as the identity of the subject. Our results together highlight the validity of our proposed technique as a design for self-supervised learning.