Aging modulates the resting brain after a memory task: A validation study from multivariate models
Entity
UAM. Departamento de Psicología Biológica y de la SaludPublisher
MDPI, Basel, Switzerland.Date
2019-04-17Citation
10.3390/e21040411
Entropy 21 (2019); 411
ISSN
1099-4300DOI
10.3390/e21040411Funded by
This study belongs to the Project MEMOTION granted by the European Commission under the frame of Marie Skłodowska Curie Actions (H2020-MSCA-IF-2014, Grant agreement ID 660031)Project
info:eu-repo/grantAgreement/EC/H2020/660031/EU//MSCAEditor's Version
https://doi.org/10.3390/e21040411Subjects
Electroencephalogram (EEG); Machine learning; Resting state; Time-frequency analysis; PsicologíaRights
© 2019 by the authors.Abstract
Recent work has demonstrated that aging modulates the resting brain. However, the study of these modulations after cognitive practice, resulting from a memory task, has been scarce. This work aims at examining age-related changes in the functional reorganization of the resting brain after cognitive training, namely, neuroplasticity, by means of the most innovative tools for data analysis. To this end, electroencephalographic activity was recorded in 34 young and 38 older participants. Different methods for data analyses, including frequency, time-frequency and machine learning-based prediction models were conducted. Results showed reductions in Alpha power in old compared to young adults in electrodes placed over posterior and anterior areas of the brain. Moreover, young participants showed Alpha power increases after task performance, while their older counterparts exhibited a more invariant pattern of results. These results were significant in the 140-160 s time window in electrodes placed over anterior regions of the brain. Machine learning analyses were able to accurately classify participants by age, but failed to predict whether resting state scans took place before or after the memory task. These findings greatly contribute to the development of multivariate tools for electroencephalogram (EEG) data analysis and improve our understanding of age-related changes in the functional reorganization of the resting brain.
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Google Scholar:Artola, Garazi
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Isusquiza, Erik
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Errarte, Ane
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Barrenechea, Maitane
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Alberdi, Ane
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Hernández-Lorca, María
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Solesio-Jofre, Elena
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