  Deep learning has been recently used for the analysis of neuroimages, such as
structural magnetic resonance imaging (MRI), functional MRI, and positron
emission tomography (PET), and has achieved significant performance
improvements over traditional machine learning in computer-aided diagnosis of
brain disorders. This paper reviews the applications of deep learning methods
for neuroimaging-based brain disorder analysis. We first provide a
comprehensive overview of deep learning techniques and popular network
architectures, by introducing various types of deep neural networks and recent
developments. We then review deep learning methods for computer-aided analysis
of four typical brain disorders, including Alzheimer's disease, Parkinson's
disease, Autism spectrum disorder, and Schizophrenia, where the first two
diseases are neurodegenerative disorders and the last two are
neurodevelopmental and psychiatric disorders, respectively. More importantly,
we discuss the limitations of existing studies and present possible future
directions.
