Autism spectrum disorder detection using diffusion tensor imaging and machine learning

Autism spectrum disorder (ASD) is a neurological and developmental disorder that manifests in social and behavioral deficits. The onset of symptoms may begin in early childhood, but diagnosis is often subjective, and scores can vary between specialists. Several studies suggest that diffusion tensor imaging (DTI)-derived indicators of anisotropy in water diffusion at microstructural level could be biomarkers for this disorder. Emerging advances in neuroimaging and machine learning can provide a fast and objective alternative for its early diagnosis. We propose and evaluate a machine-learning (ML)-powered computer-aided diagnosis (CAD) system for the detection of ASD from DTI. For the development and validation of the system we used the ABIDE II database (n = 150). The system involves processing the raw DTI to obtain fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) in 25 ASD-relevant regions of interest defined in the JHU ICBM-DTI-81 White-Matter Labeled Atlas to train a ML binary classifier. We evaluated the use of support vector machine (SVM) with various kernels and random forest (RF) optimized for computational efficiency. The best configuration, which used RF, had a sensitivity of 100%, accuracy of 95.65%, precision of 91.67%, and a specificity of 91.67%. An external test yielded 94.73% sensitivity, 97.37% accuracy, and 100% in precision and specificity. Results in this small sample show the generalization power of the best model, and the utility of carefully leveraging imaging information with clinical knowledge on relevant white matter regions commonly affected by ASD to design a CAD system for ASD.