Next Article in Journal
Geosystemics View of Earthquakes
Next Article in Special Issue
Entropy Rate Superpixel Classification for Automatic Red Lesion Detection in Fundus Images
Previous Article in Journal
Exponential Strong Converse for Successive Refinement with Causal Decoder Side Information
Previous Article in Special Issue
First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning
Open AccessArticle

Aging Modulates the Resting Brain after a Memory Task: A Validation Study from Multivariate Models

1
Biomedical Engineering Department, Mondragon Unibertsitatea, 20500 Mondragón, Gipuzkoa, Spain
2
Departamento de Psicología Biológica y de la salud, Facultad de Psicología, Universidad Autónoma de Madrid, 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(4), 411; https://doi.org/10.3390/e21040411
Received: 28 February 2019 / Revised: 12 April 2019 / Accepted: 16 April 2019 / Published: 17 April 2019
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. View Full-Text
Keywords: electroencephalogram (EEG); resting state; time-frequency analysis; machine learning electroencephalogram (EEG); resting state; time-frequency analysis; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Artola, G.; Isusquiza, E.; Errarte, A.; Barrenechea, M.; Alberdi, A.; Hernández-Lorca, M.; Solesio-Jofre, E. Aging Modulates the Resting Brain after a Memory Task: A Validation Study from Multivariate Models. Entropy 2019, 21, 411. https://doi.org/10.3390/e21040411

AMA Style

Artola G, Isusquiza E, Errarte A, Barrenechea M, Alberdi A, Hernández-Lorca M, Solesio-Jofre E. Aging Modulates the Resting Brain after a Memory Task: A Validation Study from Multivariate Models. Entropy. 2019; 21(4):411. https://doi.org/10.3390/e21040411

Chicago/Turabian Style

Artola, Garazi; Isusquiza, Erik; Errarte, Ane; Barrenechea, Maitane; Alberdi, Ane; Hernández-Lorca, María; Solesio-Jofre, Elena. 2019. "Aging Modulates the Resting Brain after a Memory Task: A Validation Study from Multivariate Models" Entropy 21, no. 4: 411. https://doi.org/10.3390/e21040411

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop