Three dimensional convolutional neural network-based classification of conduct disorder with structural MRI

• Published in: Brain imaging and behavior 2020-12, Vol.14 (6), p.2333-2340
• Main Authors: Zhang, Jianing, Li, Xuechen, Li, Yuexiang, Wang, Mingyu, Huang, Bingsheng, Yao, Shuqiao, Shen, Linlin
• Format: Article
• Language: English
• Subjects: Adolescent; Artificial neural networks; Biomarkers; Biomedical and Life Sciences; Biomedicine; Brain - diagnostic imaging; Brain mapping; Classification; Conduct disorder; Conduct Disorder - diagnostic imaging; Deep learning; Emotional disorders; Feature extraction; Frontal lobe; Humans; Machine learning; Magnetic Resonance Imaging; Male; Medical imaging; Model accuracy; Model testing; Multilayers; Neural networks; Neural Networks, Computer; Neuroimaging; Neurology; Neuropsychology; Neuroradiology; Neurosciences; NMR; Nuclear magnetic resonance; Occipital lobe; Original Research; Parietal lobe; Psychiatry; Superior temporal gyrus; Support Vector Machine; Support vector machines; Temporal gyrus; Three dimensional models
• ISSN: 1931-7557; 1931-7565
• DOI: 10.1007/s11682-019-00186-5

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.