Investigation of quantitative synthetic MRI in the evaluation of attention-deficit/hyperactivity disorder

Our study demonstrated that multi-parametric quantitative metrics derived from SyMRI combined with machine learning not only achieved good classification accuracy for distinguishing ADHD from HCs but also revealed widespread microscopic changes of gray and white matter underlying ADHD. These findings highlight the potential of using SyMRI to provide a novel perspective for exploring the disease-related microstructural processes and detecting the promising imaging marker for early diagnosis of ADHD.