Fusion of Multiple Spatial Networks Derived from Complex-Valued fMRI Data via CNN Classification

Abstract

Convolutional neural network (CNN) can achieve better classification by using independent component analysis (ICA) components derived from complex-valued fMRI data than from magnitude-only fMRI data due to incorporating additional phase information. However, thus far magnitude slices of only a single brain network (i.e. spatial component) has been used in the classification. This study aims to take advantages of multiple ICA components in providing rich information and to provide a conclusion for efficient multiple-component fusion. More precisely, we present three fusion approaches: 1) averaging multiple ICA components as inputs of a single-component CNN, 2) concatenating features of multiple single-component CNN, and 3) averaging predictive probabilities of multiple single-component CNN. We evaluate the proposed methods using resting-state fMRI data collected from 42 schizophrenia patients and 40 healthy controls. Experimental results show that all three fusion approaches can improve classification accuracy compared to the single-component CNN, and the first approach performs the best. No matter which fusion method is used, we reach the same conclusion that four-component fusion is sufficient to obtain satisfying performance, and two-component fusion yields higher improvement and better performance than the single-component classification, especially when using components having good accuracy for single-component CNN classification.