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White matter connectome edge density in children with Autism Spectrum Disorders: potential imaging biomarkers using machine learning models

Payabvash, S., Palacios, E., Owen, J. P., Wang, M. B., Tavassoli, T., Gerdes, M. R., Brandes Aitken, A., Cuneo, D., Marco, E. and Mukherjee, P. (2019) White matter connectome edge density in children with Autism Spectrum Disorders: potential imaging biomarkers using machine learning models. Brain Conenctivity, 9 (2). ISSN 2158-0014

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To link to this item DOI: 10.1089/brain.2018.0658

Abstract/Summary

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 leaning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8 to 12 years were included: 14 with ASD and 33 typically developing children (TDC). The Edge Density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging (HARDI). Tract-Based Spatial Statistics (TBSS) was used for voxel-wise comparison and coregistration of ED maps in addition to conventional 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 (SVM), neural networks. For these models, cross-validation was performed with stratified random sampling (×1000 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 to 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.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Centre for Integrative Neuroscience and Neurodynamics (CINN)
Interdisciplinary centres and themes > ASD (Autism Spectrum Disorders) Research Network
Faculty of Life Sciences > School of Psychology and Clinical Language Sciences > Department of Psychology
Faculty of Life Sciences > School of Psychology and Clinical Language Sciences > Development
ID Code:81893
Publisher:Mary Ann Liebert

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