  We exploit altered patterns in brain functional connectivity as features for
automatic discriminative analysis of neuropsychiatric patients. Deep learning
methods have been introduced to functional network classification only very
recently for fMRI, and the proposed architectures essentially focused on a
single type of connectivity measure. We propose a deep convolutional neural
network (CNN) framework for classification of electroencephalogram
(EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary
aspects of disrupted connectivity in SZ, we explore combination of various
connectivity features consisting of time and frequency-domain metrics of
effective connectivity based on vector autoregressive model and partial
directed coherence, and complex network measures of network topology. We design
a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of
1D and 2D CNNs to integrate the features from various domains and dimensions
using different fusion strategies. Hierarchical latent representations learned
by the multiple convolutional layers from EEG connectivity reveal apparent
group differences between SZ and healthy controls (HC). Results on a large
resting-state EEG dataset show that the proposed CNNs significantly outperform
traditional support vector machine classifiers. The MDC-CNN with combined
connectivity features further improves performance over single-domain CNNs
using individual features, achieving remarkable accuracy of $93.06\%$ with a
decision-level fusion. The proposed MDC-CNN by integrating information from
diverse brain connectivity descriptors is able to accurately discriminate SZ
from HC. The new framework is potentially useful for developing diagnostic
tools for SZ and other disorders.
