Combining orientation changes and fractional anisotropy of white matter fibers to diagnose autism spectrum disorder based on machine learning

Motivation: Autism spectrum disorder (ASD) lacks sensitive and effective imaging biomarkers. Goal(s): Using diffusion tensor imaging to detect white matter tracts damage and changes in local directional fields in children with ASD and combining machine learning to construct a diagnostic model for preschool-aged children with ASD. Approach: Introducing the novel mathematical framework of director field analysis, we investigate the local geometric structure of white matter tracts using tract-based spatial statistics and automated fiber quantification techniques. Results: Children with ASD have reduced fractional anisotropy and increased twist and distortion values. The machine learning model showed an area under the curve of 0.85 for diagnosing ASD. Impact: The director field analysis parameters fill the gap in previous studies and provide a new perspective for exploring the neuropathological mechanisms of ASD. By combining machine learning, the diagnostic efficiency of ASD is improved.