Questioning the definition of Tourette syndrome-evidence from machine learning

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

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 6 10.46 standard deviation) and n 109 healthy controls (57 males, 52 females, mean age 17.62 years 6 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.

Details

Original languageEnglish
Article numberfcab282
JournalBrain Communications
Volume3
Issue number4
Publication statusPublished - 2021
Peer-reviewedYes

External IDs

ORCID /0000-0002-2989-9561/work/160952421

Keywords

Sustainable Development Goals

Keywords

  • machine learning, Tourette syndrome, video scoring