Questioning the definition of Tourette syndrome—evidence from machine learning. Issue 4 (2nd December 2021)

• Record Type: Journal Article
• Authors: Paulus, Theresa; Schappert, Ronja; Bluschke, Annet; Alvarez-Fischer, Daniel; Naumann, Kim Ezra Robin; Roessner, Veit; Bäumer, Tobias; Beste, Christian; Münchau, Alexander
• Is Part Of: Brain communications. Volume 3:Issue 4(2021)
• Publication Date: 2021-12-02
• Subjects: Tourette syndrome -- machine learning -- video scoring; 616
• DOI: 10.1093/braincomms/fcab282
• Languages: English
• ISSNs: 2632-1297

Abstract

Abstract: Tics in Tourette syndrome are often difficult to discern from single spontaneous movements or vocalizations in healthy people. In this study, videos of patients with Tourette syndrome and healthy controls were taken and independently scored according to the Modified Rush Videotape Rating Scale. We included n = 101 patients with Tourette syndrome (71 males, 30 females, mean age 17.36 years ± 10.46 standard deviation) and n = 109 healthy controls (57 males, 52 females, mean age 17.62 years ± 8.78 standard deviation) in a machine learning-based analysis. The results showed that the severity of motor tics, but not vocal phenomena, is the best predictor to separate and classify patients with Tourette syndrome and healthy controls. This finding questions the validity of current diagnostic criteria for Tourette syndrome requiring the presence of both motor and vocal tics. In addition, the negligible importance of vocalizations has implications for medical practice, because current recommendations for Tourette syndrome probably also apply to the large group with chronic motor tic disorders. Abstract : Paulus et al. report which aspects of Tourette syndrome phenomenology are most useful for diagnosing Tourette syndrome. Using a machine learning-based analysis, they show that the severity of motor tics, but not vocal tics, is the best predictor to separate patients with Tourette syndrome and healthy controls. Graphical Abstract: Video Abstract: