Detecting Tourette's Syndrome in Anatomical Regions of the Brain through MRI Analysis and Naive Bayes Classifier

Tourette Syndrome (TS) is an inherited condition represented by involuntary vocal and motor movements (tics). Nowadays, there is no available cure, only psychological treatments to inhibit it, requesting the use of medication in rare cases. The importance of diagnosing Tourette's in childhood enables a range of possible treatments that would decrease the intensity of TS, and in some cases, even stop it. In most cases, the TS diagnosis considers only clinical assessment. Analyzing the brain and its anatomical regions via imaging data can provide relevant information in order to assist doctors. This work aims to propose an approach in order to identify the most affected anatomical region of the brain by TS. The approach consists of three major steps: (i) the brain is segmented in its anatomical regions; (ii) texture patterns are extracted via Gray-level Co-occurrence Matrix for each region; finally, (iii) each brain region is evaluated using Naive Bayes classifier, determining the presence or absence of TS. We use MRI images from 68 subjects around nine years old equally divided whether has TS or not. The regions from the limbic system were relevant in the diagnosis: right-side accumbens reached 68% of accuracy; posterior and central parts of corpus callosum ranked in the top four positions. Combining the top five most predictive regions led our approach to reach 78% of accuracy. The results were significant in detecting the most affected regions in TS and providing a reliable approach to classify the brain regions accordingly.