Three dimensional convolutional neural network-based classification of conduct disorder with structural MRI
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ORIGINAL RESEARCH
Three dimensional convolutional neural network-based classification of conduct disorder with structural MRI Jianing Zhang 1 & Xuechen Li 2 & Yuexiang Li 2 & Mingyu Wang 1 & Bingsheng Huang 1,3 & Shuqiao Yao 3 & Linlin Shen 2
# Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Conduct disorder (CD) is a common child and adolescent psychiatric disorder with various representative symptoms, and may cause long-term burden to patients and society. Recently, an increasing number of studies have used deep learning-based approaches, such as convolutional neural network (CNN), to analyze neuroimaging data and to identify biomarkers. In this study, we applied an optimized 3D AlexNet CNN model to automatically extract multi-layer high dimensional features of structural magnetic resonance imaging (sMRI), and to classify CD from healthy controls (HCs). We acquired high-resolution sMRI from 60 CD and 60 age- and gender-matched HCs. All subjects were male, and the age (mean ± std. dev) of participants in the CD and HC groups was 15.3 ± 1.0 and 15.5 ± 0.7, respectively. Five-fold cross validation (CV) was used to train and test this model. The receiver operating characteristic (ROC) curve for this model and that for support vector machine (SVM) model were compared. Feature visualization was performed to obtain intuition about the sMRI features learned by our AlexNet model. Our proposed AlexNet model achieved high classification performance with accuracy of 0.85, specificity of 0.82 and sensitivity of 0.87. The area under the ROC curve (AUC) of AlexNet was 0.86, significantly higher than that of SVM (AUC = 0.78; p = 0.046). The saliency maps for each convolutional layer highlighted the different brain regions in sMRI of CD, mainly including the frontal lobe, superior temporal gyrus, parietal lobe and occipital lobe. The classification results indicated that deep learning-based method is able to explore the hidden features from the sMRI of CD and might assist clinicians in the diagnosis of CD. Keywords Conduct disorder . Structural magnetic resonance imaging . Deep learning . Convolutional neural network . Classification
Introduction Jianing Zhang and Xuechen Li contributed equally to this work and cofirst authors. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11682-019-00186-5) contains supplementary material, which is available to authorized users. * Shuqiao Yao [email protected] * Linlin Shen [email protected] 1
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, People’s Republic of China
2
Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, People’s Republic of China
3
Medical Psychological Center, Second Xiangya Hospital, Central South University, Changsha, People’s Republic of China
Conduct disorder (CD) is a child and adolescent psychiatric disorder with various representative symptoms, such as violence, deceitfulness and physica
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