Learning Representation for fMRI Data Analysis Using Autoencoder

Abstract

Analysis of fMRI data is very useful for studying relationship between neural activity and a variety of brain functions. For many years, a number of brain image analysis techniques using machine learning were proposed. However, this task is still challenging due to the unique characteristics of the brain data with very small samples but extremely high dimensionality, reducing generalization performance. This paper presents a novel analysis method for fMRI data. It consists of three major steps: (1) Identifying informative voxels, (2) extracting feature space by analyzing semantic relationships among voxels and (3) learning fMRI classifier from the extracted features. Preliminary experimental results conducted on the task of image prediction from fMRI data confirmed that the proposed method achieves improvements of classification accuracy more than 20% in mean accuracy in comparing with current neuroimaging methods.