Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan
Abstract The aim of this study was to extend previous findings on selective attention over a lifetime using machine learning procedures. By decoding group membership and stimulus type, we aimed to study differences in the neural representation of inhibitory control across age groups at a single-tria...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-05-01
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| Series: | Brain Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40708-023-00190-y |
| _version_ | 1827947833558302720 |
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| author | Christian Goelz Eva-Maria Reuter Stephanie Fröhlich Julian Rudisch Ben Godde Solveig Vieluf Claudia Voelcker-Rehage |
| author_facet | Christian Goelz Eva-Maria Reuter Stephanie Fröhlich Julian Rudisch Ben Godde Solveig Vieluf Claudia Voelcker-Rehage |
| author_sort | Christian Goelz |
| collection | DOAJ |
| description | Abstract The aim of this study was to extend previous findings on selective attention over a lifetime using machine learning procedures. By decoding group membership and stimulus type, we aimed to study differences in the neural representation of inhibitory control across age groups at a single-trial level. We re-analyzed data from 211 subjects from six age groups between 8 and 83 years of age. Based on single-trial EEG recordings during a flanker task, we used support vector machines to predict the age group as well as to determine the presented stimulus type (i.e., congruent, or incongruent stimulus). The classification of group membership was highly above chance level (accuracy: 55%, chance level: 17%). Early EEG responses were found to play an important role, and a grouped pattern of classification performance emerged corresponding to age structure. There was a clear cluster of individuals after retirement, i.e., misclassifications mostly occurred within this cluster. The stimulus type could be classified above chance level in ~ 95% of subjects. We identified time windows relevant for classification performance that are discussed in the context of early visual attention and conflict processing. In children and older adults, a high variability and latency of these time windows were found. We were able to demonstrate differences in neuronal dynamics at the level of individual trials. Our analysis was sensitive to mapping gross changes, e.g., at retirement age, and to differentiating components of visual attention across age groups, adding value for the diagnosis of cognitive status across the lifespan. Overall, the results highlight the use of machine learning in the study of brain activity over a lifetime. Graphical Abstract |
| first_indexed | 2024-04-09T12:45:21Z |
| format | Article |
| id | doaj.art-9a42b4ec0a6c4feaae1ce319b9f1eaa9 |
| institution | Directory Open Access Journal |
| issn | 2198-4018 2198-4026 |
| language | English |
| last_indexed | 2024-04-09T12:45:21Z |
| publishDate | 2023-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj.art-9a42b4ec0a6c4feaae1ce319b9f1eaa92023-05-14T11:31:51ZengSpringerOpenBrain Informatics2198-40182198-40262023-05-0110111110.1186/s40708-023-00190-yClassification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespanChristian Goelz0Eva-Maria Reuter1Stephanie Fröhlich2Julian Rudisch3Ben Godde4Solveig Vieluf5Claudia Voelcker-Rehage6Institute of Sports Medicine, Paderborn UniversityDepartment of Sport and Health Sciences, Technical University of MunichDepartment of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, University of MünsterDepartment of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, University of MünsterSchool of Business, Social and Decision Sciences, Constructor UniversityInstitute of Sports Medicine, Paderborn UniversityDepartment of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, University of MünsterAbstract The aim of this study was to extend previous findings on selective attention over a lifetime using machine learning procedures. By decoding group membership and stimulus type, we aimed to study differences in the neural representation of inhibitory control across age groups at a single-trial level. We re-analyzed data from 211 subjects from six age groups between 8 and 83 years of age. Based on single-trial EEG recordings during a flanker task, we used support vector machines to predict the age group as well as to determine the presented stimulus type (i.e., congruent, or incongruent stimulus). The classification of group membership was highly above chance level (accuracy: 55%, chance level: 17%). Early EEG responses were found to play an important role, and a grouped pattern of classification performance emerged corresponding to age structure. There was a clear cluster of individuals after retirement, i.e., misclassifications mostly occurred within this cluster. The stimulus type could be classified above chance level in ~ 95% of subjects. We identified time windows relevant for classification performance that are discussed in the context of early visual attention and conflict processing. In children and older adults, a high variability and latency of these time windows were found. We were able to demonstrate differences in neuronal dynamics at the level of individual trials. Our analysis was sensitive to mapping gross changes, e.g., at retirement age, and to differentiating components of visual attention across age groups, adding value for the diagnosis of cognitive status across the lifespan. Overall, the results highlight the use of machine learning in the study of brain activity over a lifetime. Graphical Abstracthttps://doi.org/10.1186/s40708-023-00190-yMachine learningDecodingEEG/ERPFlankerSelective attentionDevelopment |
| spellingShingle | Christian Goelz Eva-Maria Reuter Stephanie Fröhlich Julian Rudisch Ben Godde Solveig Vieluf Claudia Voelcker-Rehage Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan Brain Informatics Machine learning Decoding EEG/ERP Flanker Selective attention Development |
| title | Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan |
| title_full | Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan |
| title_fullStr | Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan |
| title_full_unstemmed | Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan |
| title_short | Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan |
| title_sort | classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan |
| topic | Machine learning Decoding EEG/ERP Flanker Selective attention Development |
| url | https://doi.org/10.1186/s40708-023-00190-y |
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