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

Jianing Zhang, Xuechen Li, Yuexiang Li, Mingyu Wang, Bingsheng Huang, Shuqiao Yao, Linlin Shen

Research output: Journal PublicationArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2333-2340
Number of pages8
JournalBrain Imaging and Behavior
Volume14
Issue number6
DOIs
Publication statusPublished - 1 Dec 2020
Externally publishedYes

Keywords

  • Classification
  • Conduct disorder
  • Convolutional neural network
  • Deep learning
  • Structural magnetic resonance imaging

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology
  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health
  • Behavioral Neuroscience

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