A Novel Two-stage Prediction Model to Classify Functional Connectivity for Brain Disease Diagnosis

Neuroscience studies have demonstrated that inherent interactions in human brain are associated with various brain diseases. The functional connectivity (FC) analysis in the resting-state functional magnetic resonance imaging (rs-fMRI) has attracted increasing attention. Recently, the application of deep learning algorithms has shown great potential in exploring the diagnosis biomarker for brain disease. However, the high dimensionality and small sample size properties of the FC patterns usually restrict the training of deep learning models and limit the model generalization performance. To solve this problem, this paper proposed a novel prediction model with a two-stage design to extract and classify FC features for the brain diseases diagnosis. Our model employed a flexible configuration that contained a deep learning-based FC feature extraction stage followed by a separate classification stage. In the first stage, stacked sparse denoising autoencoders were employed to extract and learn the abstract feature representations of the initial FC patterns with both unsupervised and supervised learning steps. In the second stage, the learnt features were used to train a separate simple classifier for brain disease classification. We conducted experiments on a large-scale fMRI dataset to construct FC patterns and distinguish autism patients from healthy controls. Results showed that our two-stage model outperformed the competing classification methods and showed robust generality for different brain nodes parcellation. Our study indicated that this flexible two-stage model design can further improve the classification performance and provide a promising approach for the computer-aided diagnosis of brain disorders.