Neural representations of statistical and rule-based predictions in Gilles de la Tourette syndrome

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

Gilles de la Tourette syndrome (GTS) is a disorder characterised by motor and vocal tics, which may represent habitual actions as a result of enhanced learning of associations between stimuli and responses (S-R). In this study, we investigated how adults with GTS and healthy controls (HC) learn two types of regularities in a sequence: statistics (non-adjacent probabilities) and rules (predefined order). Participants completed a visuomotor sequence learning task while EEG was recorded. To understand the neurophysiological underpinnings of these regularities in GTS, multivariate pattern analyses on the temporally decomposed EEG signal as well as sLORETA source localisation method were conducted. We found that people with GTS showed superior statistical learning but comparable rule-based learning compared to HC participants. Adults with GTS had different neural representations for both statistics and rules than HC adults; specifically, adults with GTS maintained the regularity representations longer and had more overlap between them than HCs. Moreover, over different time scales, distinct fronto-parietal structures contribute to statistical learning in the GTS and HC groups. We propose that hyper-learning in GTS is a consequence of the altered sensitivity to encode complex statistics, which might lead to habitual actions.

Details

Original languageEnglish
Article numbere26719
JournalHuman brain mapping
Volume45
Issue number8
Publication statusPublished - 1 Jun 2024
Peer-reviewedYes

External IDs

PubMed 38826009
ORCID /0000-0002-2989-9561/work/169643241

Keywords

Keywords

  • electroencephalography, Gilles de la Tourette syndrome, multivariate pattern analysis, predictive processing, sequence learning, statistical learning, temporal decomposition