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Vékony T, Takács Á, Pedraza F, Haesebaert F, Tillmann B, Mihalecz I, Phelipon R, Beste C, Nemeth D. Modality-specific and modality-independent neural representations work in concert in predictive processes during sequence learning. Cereb Cortex 2023:7081423. [PMID: 36944531 DOI: 10.1093/cercor/bhad079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 03/23/2023] Open
Abstract
Probabilistic sequence learning supports the development of skills and enables predictive processing. It remains contentious whether visuomotor sequence learning is driven by the representation of the visual sequence (perceptual coding) or by the representation of the response sequence (motor coding). Neurotypical adults performed a visuomotor sequence learning task. Learning occurred incidentally as it was evidenced by faster responses to high-probability than to low-probability targets. To uncover the neurophysiology of the learning process, we conducted both univariate analyses and multivariate pattern analyses (MVPAs) on the temporally decomposed EEG signal. Univariate analyses showed that sequence learning modulated the amplitudes of the motor code of the decomposed signal but not in the perceptual and perceptual-motor signals. However, MVPA revealed that all 3 codes of the decomposed EEG contribute to the neurophysiological representation of the learnt probabilities. Source localization revealed the involvement of a wider network of frontal and parietal activations that were distinctive across coding levels. These findings suggest that perceptual and motor coding both contribute to the learning of sequential regularities rather than to a neither-nor distinction. Moreover, modality-specific encoding worked in concert with modality-independent representations, which suggests that probabilistic sequence learning is nonunitary and encompasses a set of encoding principles.
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Affiliation(s)
- Teodóra Vékony
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, 95 Boulevard Pinel, Bron F-69500, France
| | - Ádám Takács
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Fetscherstraße 74, Dresden D-01307, Germany
- Faculty of Medicine, University Neuropsychology Center, TU Dresden, Fetscherstraße 74, Dresden D-01307, Germany
| | - Felipe Pedraza
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, 95 Boulevard Pinel, Bron F-69500, France
- Université de Lyon, Université Lyon 2, Laboratory EMC (EA 3082), Bron F-69500, France
| | - Frederic Haesebaert
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, 95 Boulevard Pinel, PSYR2 Team, Bron F-69500, France
| | - Barbara Tillmann
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, 95 Boulevard Pinel, Bron F-69500, France
- CNRS, UMR5022, Laboratoire d'Etude de l'Apprentissage et du Développement, Université de Bourgogne, Dijon F-21048, France
| | - Imola Mihalecz
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, 95 Boulevard Pinel, Bron F-69500, France
| | - Romane Phelipon
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, 95 Boulevard Pinel, Bron F-69500, France
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Fetscherstraße 74, Dresden D-01307, Germany
- Faculty of Medicine, University Neuropsychology Center, TU Dresden, Fetscherstraße 74, Dresden D-01307, Germany
| | - Dezso Nemeth
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, 95 Boulevard Pinel, Bron F-69500, France
- Institute of Psychology, ELTE Eötvös Loránd University, Kazinczy u. 23-27, Budapest H-1075, Hungary
- Brain, Memory and Language Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest H-1117, Hungary
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Rajeswaran J, Blackstone EH, Barnard J. Evolution of association between renal and liver functions while awaiting heart transplant: An application using a bivariate multiphase nonlinear mixed effects model. Stat Methods Med Res 2018; 27:2216-2230. [PMID: 27856959 PMCID: PMC5433933 DOI: 10.1177/0962280216678022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In many longitudinal follow-up studies, we observe more than one longitudinal outcome. Impaired renal and liver functions are indicators of poor clinical outcomes for patients who are on mechanical circulatory support and awaiting heart transplant. Hence, monitoring organ functions while waiting for heart transplant is an integral part of patient management. Longitudinal measurements of bilirubin can be used as a marker for liver function and glomerular filtration rate for renal function. We derive an approximation to evolution of association between these two organ functions using a bivariate nonlinear mixed effects model for continuous longitudinal measurements, where the two submodels are linked by a common distribution of time-dependent latent variables and a common distribution of measurement errors.
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Affiliation(s)
- Jeevanantham Rajeswaran
- Department of Quantitative Health Sciences, and Heart and Vascular Institute, Cleveland Clinic, Cleveland, USA
| | - Eugene H Blackstone
- Department of Quantitative Health Sciences, and Heart and Vascular Institute, Cleveland Clinic, Cleveland, USA
| | - John Barnard
- Department of Quantitative Health Sciences, and Heart and Vascular Institute, Cleveland Clinic, Cleveland, USA
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Abstract
In medical sciences, we often encounter longitudinal temporal relationships that are non-linear in nature. The influence of risk factors may also change across longitudinal follow-up. A system of multiphase non-linear mixed effects model is presented to model temporal patterns of longitudinal continuous measurements, with temporal decomposition to identify the phases and risk factors within each phase. Application of this model is illustrated using spirometry data after lung transplantation using readily available statistical software. This application illustrates the usefulness of our flexible model when dealing with complex non-linear patterns and time-varying coefficients.
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Affiliation(s)
- Jeevanantham Rajeswaran
- Department of Quantitative Health Sciences, Heart and Vascular Institute, Cleveland Clinic, Cleveland, USA
| | - Eugene H Blackstone
- Department of Quantitative Health Sciences, Heart and Vascular Institute, Cleveland Clinic, Cleveland, USA
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Rajeswaran J, Blackstone EH, Ehrlinger J, Li L, Ishwaran H, Parides MK. Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model. Stat Methods Med Res 2016; 27:126-141. [PMID: 26740575 DOI: 10.1177/0962280215623583] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Atrial fibrillation is an arrhythmic disorder where the electrical signals of the heart become irregular. The probability of atrial fibrillation (binary response) is often time varying in a structured fashion, as is the influence of associated risk factors. A generalized nonlinear mixed effects model is presented to estimate the time-related probability of atrial fibrillation using a temporal decomposition approach to reveal the pattern of the probability of atrial fibrillation and their determinants. This methodology generalizes to patient-specific analysis of longitudinal binary data with possibly time-varying effects of covariates and with different patient-specific random effects influencing different temporal phases. The motivation and application of this model is illustrated using longitudinally measured atrial fibrillation data obtained through weekly trans-telephonic monitoring from an NIH sponsored clinical trial being conducted by the Cardiothoracic Surgery Clinical Trials Network.
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Affiliation(s)
| | | | - John Ehrlinger
- 1 Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Liang Li
- 2 The University of Texas MD Anderson Cancer Center, University of Texas, Houston, TX, USA
| | - Hemant Ishwaran
- 3 Division of Biostatistics, University of Miami, Miami, FL, USA
| | - Michael K Parides
- 4 Mount Sinai Center for Biostatistics, Mount Sinai Hospital, New York, NY, USA
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