Multilayer Functional Connectome Fingerprints: Individual Identification via Multimodal Convolutional Neural Network

Abstract

As a neural fingerprint, functional connectivity networks (FCNs) have been used to identify subjects from group. However, a number of studies have only paid attention to cerebral cortex when constructing the brain FCN. Other areas of the brain also play important roles in brain activities. It is widely accepted that the human brain is composed of many highly complex functional networks of cortex. Moreover, recent studies have confirmed correlations between signals of cortex and white matter (WM) bundles. Therefore, it is difficult to reflect the functional characteristics of the brain through a single-layer FCN. In this paper, a multilayer FCN involving both cerebral cortex and WM was proposed to characterize different levels of FCNs based on resting-state functional Magnetic Resonance Imaging (fMRI) data. The multilayer FCN is regarded as a neural fingerprint to realize individual identification and the individual variability of which is related to individual differences in fluid intelligence and gender. In addition, the proposed multilayer FCN has been learned by a multimodal convolutional neural network (CNN) algorithm to achieve individual identification. Compared with the existing FCN that only involves the cerebral cortex, the accuracy of individual recognition illustrates the effectiveness of our method. Meanwhile, the proposed multilayer FCN also achieves better results than the traditional single-layer FCN in gender classification and fluid intelligence prediction.