A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

Abstract

Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it 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.

Keywords: Alzheimer's disease; Parkinson's disease; autism spectrum disorder; deep learning; neuroimage; schizophrenia.

Figure 1

Architectures of the single-layer (A) and multi-layer (B) neural networks. The blue, green, and orange solid circles represent the input visible, hidden, and output units, respectively.

Figure 2

Architectures of a stacked auto-encoder. The blue and red dotted boxes represent the encoding and decoding stage, respectively. The blue solid circles are the input and output units, which have the same number nodes. The orange solid circles represent the latent representation, and the green solid circles represent any hidden layers.

Figure 3

Schematic illustration of Deep Belief Networks (A) and Deep Boltzmann Machine (B). The double-headed arrow represents the undirected connection between the two neighboring layers, and the single-headed arrow is the directed connection. The top two layers of the DBN form an undirected generative model and the remaining layers form directed generative model. But all layers of the DBM are undirected generative model.

Figure 4

Architecture of Generative Adversarial Networks. “R” and “F” represents the real and fake label, respectively.

Figure 5

Architecture of convolutional neural networks. Note that an implicit rectified linear unit (ReLU) non-linearity is applied after every layer. The natural images as input data in Krizhevsky et al. (2012) are replaced by brain MR images.

Figure 6

Architecture of graph convolutional networks. To keep the figure simple, the softmax output layer is not shown.

Figure 7

Architectures of long short-term memory (A) and gated recurrent unit (B) In the subfigure (A), the blue, green, and yellow represent the forget gate f_t, input, gate i_t, and output gate o_t, respectively. In the subfigure (B), the blue and yellow represent the reset gate r_t and update gate z_t, respectively. x_t is input vector and h_t is the hidden state. To keep the figure simple, biases are not shown.