LAUSR

Abstract Background Several machine-learning (ML) algorithms have been deployed in the diagnostic classification of schizophrenia. Compared to other ML methods, the 3D convolutional neural network (CNN) has an advantage of learning complex and subtle patterns in data and preserving spatial information, which is a more suitable tool for brain imaging data. Although resting-state functional MRI (rsfMRI) data has been used in previous ML studies relating to the diagnostic classification of schizophrenia, a limited number of studies have been conducted using resting-state functional connectivity resulted from group independent component analysis (ICA) and dual regression. The objective of this study was to investigate whether a successful diagnostic classification of schizophrenia vs. healthy controls could be achieved by the 3D CNN using resting-state networks in which areas with a significant group difference in activity existed. Methods T1 and rsfMRI data were collected in 46 patients with recent-onset schizophrenia and 22 healthy controls. In the pre-processing steps of rsfMRI, the ICA-based automatic removal of motion artifacts was applied to subject-level ICA results and the resulting rsfMRI data were temporally concatenated for group ICA and dual regression. The executive control and auditory networks had areas with significantly higher activity in the control group compared with the patient group. The independent components (ICs) respective to the executive control and auditory networks were used as input for the 3D CNN model which was developed to discriminate the schizophrenia patients from the healthy controls. Results The 3D CNN model using the executive control and auditory networks as inputs showed classification accuracies of 65~70%, and error rates of 30~35% approximately. Discussion Our findings suggest that the 3D CNN model using rsfMRI data can be useful for learning patterns implicated in schizophrenia and identifying discriminative patterns of schizophrenia in brain imaging data.