Identifying texture features from structural magnetic resonance imaging scans associated with Tourette's syndrome using machine learning.

Purpose: Tourette syndrome (TS) is a neurodevelopmental disorder characterized by neurophysiological and neuroanatomical changes, primarily affecting individuals aged 2 to 18. Involuntary motor and vocal tics are common features of this syndrome. Currently, there is no curative therapy for TS, only psychological treatments or medications that temporarily manage the tics. The absence of a definitive diagnostic tool complicates the differentiation of TS from other neurological and psychological conditions. Approach: We aim to enhance the diagnosis of TS through the classification of structural magnetic resonance scans. Our methodology comprises four sequential steps: (1) image acquisition, data were generated for the National Taiwan University, composing images of pediatric magnetic resonance imaging (MRI); (2) pre-processing, involving anatomical structural segmentation using reesurfer software; (3) feature extraction, where texture features in volumetric images are obtained; and (4) image classification, employing support vector machine and naive Bayes classifiers to determine the presence of TS. Results: The analysis indicated significant changes in the regions of the limbic system, such as the thalamus and amygdala, and regions outside the limbic system such as medial orbitofrontal cortex and insula, which are strongly associated with TS. Conclusions: Our findings suggest that texture features derived from sMRI scans can aid in the diagnosis of TS by highlighting critical brain regions involved in the disorder. The proposed method holds promise for improving diagnostic accuracy and understanding the neuroanatomical underpinnings of TS.