Eye blinking abnormalities in Tourette syndrome: Blink more or blink differently?

Publikation: Beitrag in FachzeitschriftKurzartikel (Letter) / Leserbrief mit OriginaldatenBeigetragenBegutachtung

Beitragende

  • Julius Verrel - , Universität zu Lübeck (Autor:in)
  • Ronja Schappert - , Universität zu Lübeck (Autor:in)
  • Nele Brügge - , Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) (Autor:in)
  • Tina Rawish - , Universität zu Lübeck (Autor:in)
  • Tobias Bäumer - , Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein Campus Lübeck (Autor:in)
  • Yifan Hao - , Universität zu Lübeck (Autor:in)
  • Roland Stenger - , Universität zu Lübeck (Autor:in)
  • Christian Beste - , Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Deutsches Zentrum für Kinder- und Jugendgesundheit (DZKJ) - Standort Leipzig/Dresden (Autor:in)
  • Sebastian Fudickar - , Universität zu Lübeck (Autor:in)
  • Veit Roessner - , Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Deutsches Zentrum für Kinder- und Jugendgesundheit (DZKJ) - Standort Leipzig/Dresden (Autor:in)
  • Alexander Münchau - , Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein Campus Lübeck (Autor:in)

Abstract

Introduction: Blinking abnormalities are among the earliest and most common symptoms in Gilles de la Tourette syndrome (GTS) but have not been studied using precise quantitative methods. Here, we use automated video-based analyses to assess blinking abnormalities in GTS in terms of blink rates as well as alterations in spatiotemporal blink features. Methods: We analyzed 2.5-minute video recordings from age- and sex-matched adult GTS and healthy control (HC) samples (56 participants with 136 videos per group; 18–59 years). Eye aperture time series were used to detect blink events and extract blink features (amplitude, amplitude asymmetry, duration, inter-blink intervals). Individual blinks were categorized as “typical” or “atypical” relative to feature distributions from an independent HC sample. Results: Overall blink rates were twice as high in GTS compared to HC (34.1 vs. 17.3 blinks/minute). This difference was most pronounced for atypical blinks (10.8 vs. 1.9 atypical blinks/minute), even allowing high-accuracy classification of GTS vs. HC videos (83.1 %) using cross-validated logistic regression. Classification based on the rate of typical blinks, average blink features, or single feature deviation rates yielded considerably lower accuracies. On average, 58.7 % of atypical blinks of a given participant shared the same feature deviation, but the feature deviation patterns significantly varied between participants, as confirmed using permutation statistics. Conclusion: Blinking abnormalities in GTS are best characterized by the frequency of atypical blinks, which appear to drive the overall increase in blink rate. Blinking abnormalities were consistent within but heterogeneous across individuals.

Details

OriginalspracheEnglisch
Aufsatznummer108121
FachzeitschriftParkinsonism and Related Disorders
Jahrgang142
Frühes Online-Datum11 Nov. 2025
PublikationsstatusVeröffentlicht - Jan. 2026
Peer-Review-StatusJa

Externe IDs

PubMed 41240675
ORCID /0000-0002-2989-9561/work/203071488

Schlagworte

Schlagwörter

  • Blink features, Blink tics, Blinking, Tourette syndrome, Video analysis