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Takacs A, Toth-Faber E, Schubert L, Tarnok Z, Ghorbani F, Trelenberg M, Nemeth D, Münchau A, Beste C. Neural representations of statistical and rule-based predictions in Gilles de la Tourette syndrome. Hum Brain Mapp 2024; 45:e26719. [PMID: 38826009 DOI: 10.1002/hbm.26719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 04/11/2024] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
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.
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Affiliation(s)
- Adam Takacs
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- University Neuropsychology Center, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Eszter Toth-Faber
- Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary
- Brain, Memory and Language Research Group, Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
| | - Lina Schubert
- Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany
| | - Zsanett Tarnok
- Vadaskert Child and Adolescent Psychiatry Hospital and Outpatient Clinic, Budapest, Hungary
| | - Foroogh Ghorbani
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- University Neuropsychology Center, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Madita Trelenberg
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Dezso Nemeth
- INSERM, Université Claude Bernard Lyon 1, CNRS, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Bron, France
- NAP Research Group, Institute of Psychology, Eötvös Loránd University and Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
- Department of Education and Psychology, Faculty of Social Sciences, University of Atlántico Medio, Las Palmas de Gran Canaria, Spain
| | - Alexander Münchau
- Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- University Neuropsychology Center, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
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Measuring statistical learning by eye-tracking. EXPERIMENTAL RESULTS 2022. [DOI: 10.1017/exp.2022.8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Abstract
Statistical learning—the skill to pick up probability-based regularities of the environment—plays a crucial role in adapting to the environment and learning perceptual, motor, and language skills in healthy and clinical populations. Here, we developed a new method to measure statistical learning without any manual responses. We used the Alternating Serial Reaction Time (ASRT) task, adapted to eye-tracker, which, besides measuring reaction times (RTs), enabled us to track learning-dependent anticipatory eye movements. We found robust, interference-resistant learning on RT; moreover, learning-dependent anticipatory eye movements were even more sensitive measures of statistical learning on this task. Our method provides a way to apply the widely used ASRT task to operationalize statistical learning in clinical populations where the use of manual tasks is hindered, such as in Parkinson’s disease. Furthermore, it also enables future basic research to use a more sensitive version of this task to measure predictive processing.
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