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Gaidai R, Goelz C, Mora K, Rudisch J, Reuter E, Godde B, Reinsberger C, Voelcker-Rehage C, Vieluf S. Classification characteristics of fine motor experts based on electroencephalographic and force tracking data. Brain Res 2022; 1792:148001. [DOI: 10.1016/j.brainres.2022.148001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 06/29/2022] [Accepted: 07/01/2022] [Indexed: 11/02/2022]
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Using EEG to study sensorimotor adaptation. Neurosci Biobehav Rev 2022; 134:104520. [PMID: 35016897 DOI: 10.1016/j.neubiorev.2021.104520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/10/2021] [Accepted: 12/30/2021] [Indexed: 11/23/2022]
Abstract
Sensorimotor adaptation, or the capacity to flexibly adapt movements to changes in the body or the environment, is crucial to our ability to move efficiently in a dynamic world. The field of sensorimotor adaptation is replete with rigorous behavioural and computational methods, which support strong conceptual frameworks. An increasing number of studies have combined these methods with electroencephalography (EEG) to unveil insights into the neural mechanisms of adaptation. We review these studies: discussing EEG markers of adaptation in the frequency and the temporal domain, EEG predictors for successful adaptation and how EEG can be used to unmask latent processes resulting from adaptation, such as the modulation of spatial attention. With its high temporal resolution, EEG can be further exploited to deepen our understanding of sensorimotor adaptation.
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Goelz C, Mora K, Stroehlein JK, Haase FK, Dellnitz M, Reinsberger C, Vieluf S. Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults. Cogn Neurodyn 2021; 15:847-859. [PMID: 34603546 PMCID: PMC8448815 DOI: 10.1007/s11571-020-09656-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 11/03/2020] [Accepted: 11/27/2020] [Indexed: 11/26/2022] Open
Abstract
Cardiorespiratory fitness was found to influence age-related changes of resting state brain network organization. However, the influence on dedifferentiated involvement of wider and more unspecialized brain regions during task completion is barely understood. We analyzed EEG data recorded during rest and different tasks (sensory, motor, cognitive) with dynamic mode decomposition, which accounts for topological characteristics as well as temporal dynamics of brain networks. As a main feature the dominant spatio-temporal EEG pattern was extracted in multiple frequency bands per participant. To deduce a pattern’s stability, we calculated its proportion of total variance among all activation patterns over time for each task. By comparing fit (N = 15) and less fit older adults (N = 16) characterized by their performance on a 6-min walking test, we found signs of a lower task specificity of the obtained network features for the less fit compared to the fit group. This was indicated by fewer significant differences between tasks in the theta and high beta frequency band in the less fit group. Repeated measures ANOVA revealed that a significantly lower proportion of total variance can be explained by the main pattern in high beta frequency range for the less fit compared to the fit group [F(1,29) = 12.572, p = .001, partial η2 = .300]. Our results indicate that the dedifferentiation in task-related brain activation is lower in fit compared to less fit older adults. Thus, our study supports the idea that cardiorespiratory fitness influences task-related brain network organization in different task domains.
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Affiliation(s)
- Christian Goelz
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany
| | - Karin Mora
- Department of Mathematics, Paderborn University, Paderborn, Germany
| | - Julia Kristin Stroehlein
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany
| | | | - Michael Dellnitz
- Department of Mathematics, Paderborn University, Paderborn, Germany
| | - Claus Reinsberger
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany
| | - Solveig Vieluf
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany
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Classification of visuomotor tasks based on electroencephalographic data depends on age-related differences in brain activity patterns. Neural Netw 2021; 142:363-374. [PMID: 34116449 DOI: 10.1016/j.neunet.2021.04.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 03/12/2021] [Accepted: 04/22/2021] [Indexed: 11/23/2022]
Abstract
Classification of physiological data provides a data driven approach to study central aspects of motor control, which changes with age. To implement such results in real-life applications for elderly it is important to identify age-specific characteristics of movement classification. We compared task-classification based on EEG derived activity patterns related to brain network characteristics between older and younger adults performing force tracking with two task characteristics (sinusoidal; constant) with the right or left hand. We extracted brain network patterns with dynamic mode decomposition (DMD) and classified the tasks on an individual level using linear discriminant analysis (LDA). Next, we compared the models' performance between the groups. Studying brain activity patterns, we identified signatures of altered motor network function reflecting dedifferentiated and compensational brain activation in older adults. We found that the classification performance of the body side was lower in older adults. However, classification performance with respect to task characteristics was better in older adults. This may indicate a higher susceptibility of brain network mechanisms to task difficulty in elderly. Signatures of dedifferentiation and compensation refer to an age-related reorganization of functional brain networks, which suggests that classification of visuomotor tracking tasks is influenced by age-specific characteristics of brain activity patterns. In addition to insights into central aspects of fine motor control, the results presented here are relevant in application-oriented areas such as brain computer interfaces.
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Strote C, Gölz C, Stroehlein JK, Haase FK, Koester D, Reinsberger C, Vieluf S. Effects of force level and task difficulty on force control performance in elderly people. Exp Brain Res 2020; 238:2179-2188. [PMID: 32661649 PMCID: PMC7496054 DOI: 10.1007/s00221-020-05864-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 06/30/2020] [Indexed: 11/25/2022]
Abstract
As the proportion of people over 60 years of age rises continuously in westernized societies, it becomes increasingly important to better understand aging processes and how to maintain independence in old age. Fine motor tasks are essential in daily living and, therefore, necessary to maintain. This paper extends the existing literature on fine motor control by manipulating the difficulty of a force maintenance task to characterize performance optima for elderly. Thirty-seven elderly (M = 68.00, SD = 4.65) performed a force control task at dynamically varying force levels, i.e. randomly changing every 3 s between 10%, 20%, and 30% of the individual's maximum voluntary contraction (MVC). This task was performed alone or with one or two additional tasks to increase task difficulty. The force control characteristics accuracy, variability, and complexity were analyzed. Lowest variability was observed at 20%. Accuracy and complexity increased with increasing force level. Overall, increased task difficulty had a negative impact on task performance. Results support the assumption, that attention control has a major impact on force control performance in elderly people. We assume different parameters to have their optimum at different force levels, which remain comparably stable when additional tasks are performed. The study contributes to a better understanding of how force control is affected in real-life situations when it is performed simultaneously to other cognitive and sensory active and passive tasks.
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Affiliation(s)
- Caren Strote
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
| | - Christian Gölz
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
| | - Julia Kristin Stroehlein
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
| | | | - Dirk Koester
- Department of Psychology and Sports Science, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany
- Faculty Business and Management, BSP Business School Berlin, Calandrellistr. 1-9, Berlin, 12247, Germany
| | - Claus Reinsberger
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
| | - Solveig Vieluf
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany.
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Gölz C, Voelcker-Rehage C, Mora K, Reuter EM, Godde B, Dellnitz M, Reinsberger C, Vieluf S. Improved Neural Control of Movements Manifests in Expertise-Related Differences in Force Output and Brain Network Dynamics. Front Physiol 2018; 9:1540. [PMID: 30519188 PMCID: PMC6258820 DOI: 10.3389/fphys.2018.01540] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2018] [Accepted: 10/15/2018] [Indexed: 02/05/2023] Open
Abstract
It is well-established that expertise developed through continuous and deliberate practice has the potential to delay age-related decline in fine motor skills. However, less is known about the underlying mechanisms, that is, whether expertise leads to a higher performance level changing the initial status from which age-related decline starts or if expertise-related changes result in qualitatively different motor output and neural processing providing a resource of compensation for age-related changes. Thus, as a first step, this study aims at a better understanding of expertise-related changes in fine motor control with respect to force output and respective electrophysiological correlates. Here, using a multidimensional approach, we investigated fine motor control of experts and novices in precision mechanics during the execution of a dynamic force control task. On the level of force output, we analyzed precision, variability, and complexity. We further used dynamic mode decomposition (DMD) to analyze the electrophysiological correlates of force control to deduce brain network dynamics. Experts’ force output was more precise, less variable, and more complex. Task-related DMD mean mode magnitudes within the α-band at electrodes over sensorimotor relevant areas were reduced in experts, and lower DMD mean mode magnitudes related to the force output in novices. Our results provide evidence for expertise dependent central adaptions with distinct and more complex organization and decentralization of sensorimotor subsystems. Results from our multidimensional approach can be seen as a step forward in understanding expertise-related changes and exploiting their potential as resources for healthy aging.
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Affiliation(s)
- Christian Gölz
- Institute of Sports Medicine, Paderborn University, Paderborn, Germany
| | - Claudia Voelcker-Rehage
- Institute of Human Movement Science and Health, Chemnitz University of Technology, Chemnitz, Germany
| | - Karin Mora
- Department of Mathematics, Paderborn University, Paderborn, Germany
| | - Eva-Maria Reuter
- Centre for Sensorimotor Performance, School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Ben Godde
- Department of Psychology & Methods, Jacobs University Bremen, Bremen, Germany
| | - Michael Dellnitz
- Department of Mathematics, Paderborn University, Paderborn, Germany
| | - Claus Reinsberger
- Institute of Sports Medicine, Paderborn University, Paderborn, Germany
| | - Solveig Vieluf
- Institute of Sports Medicine, Paderborn University, Paderborn, Germany
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