White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models

Abstract

Prior neuroimaging studies have reported white matter network underconnectivity as a potential mechanism for autism spectrum disorder (ASD). In this study, we examined the structural connectome of children with ASD using edge density imaging (EDI), and then applied machine-learning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8-12 years were included: 14 with ASD and 33 typically developing children. The edge density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging. Tract-based spatial statistics was used for voxel-wise comparison and coregistration of ED maps in addition to conventional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Tract-based average DTI/connectome metrics were calculated and used as input for different machine-learning models: naïve Bayes, random forest, support vector machines (SVMs), and neural networks. For these models, cross-validation was performed with stratified random sampling ( × 1,000 permutations). The average accuracy among validation samples was calculated. In voxel-wise analysis, the body and splenium of corpus callosum, bilateral superior and posterior corona radiata, and left superior longitudinal fasciculus showed significantly lower ED in children with ASD; whereas, we could not find significant difference in FA, MD, and RD maps between the two study groups. Overall, machine-learning models using tract-based ED metrics had better performance in identification of children with ASD compared with those using FA, MD, and RD. The EDI-based random forest models had greater average accuracy (75.3%), specificity (97.0%), and positive predictive value (81.5%), whereas EDI-based polynomial SVM had greater sensitivity (51.4%) and negative predictive values (77.7%). In conclusion, we found reduced density of connectome edges in the posterior white matter tracts of children with ASD, and demonstrated the feasibility of connectome-based machine-learning algorithms in identification of children with ASD.

Keywords: autism; diffusion tensor imaging; edge density imaging; machine learning.

FIG. 1.

EDI processing pipeline summary. EDI, edge density imaging. Color images are available online.

FIG. 2.

The mean skeletonized fractional anisotropy of all 47 subjects is overlaid on MNI-152 brain map in green color. Those white matter tracts with significantly lower ED in children with ASD compared with TDC are filled with red, based on voxel-wise TBSS analysis and after applying TFCE correction. ASD, autism spectrum disorder; ED, edge density; TBSS, tract-based spatial statistics; TDC, typically developing children; TFCE, threshold-free cluster enhancement. Color images are available online.

FIG. 3.

Heat map summary for classification performance of different machine-learning algorithms using DTI and connectome metrics for identification of children with ASD. The accuracy, sensitivity, specificity, PPV, and NPV were calculated in validation datasets from × 1,000 cross-validation—details provided in Table 3. In each column, yellow to red color range is assigned to values from low to high, respectively. DTI, diffusion tensor imaging; SVM, support vector machine; PPV, positive predictive value; NPV, negative predictive value. Color images are available online.