Next Article in Journal
A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches
Next Article in Special Issue
Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning
Previous Article in Journal
A Gaussian Process State Space Model Fusion Physical Model and Residual Analysis for Fatigue Evaluation
Previous Article in Special Issue
CNN-Based Spectral Super-Resolution of Panchromatic Night-Time Light Imagery: City-Size-Associated Neighborhood Effects
 
 
Article

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network

1
Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia
2
Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan 420500, Russia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Danil Prokhorov and Alexander N. Gorban
Sensors 2022, 22(7), 2537; https://doi.org/10.3390/s22072537
Received: 21 February 2022 / Revised: 21 March 2022 / Accepted: 24 March 2022 / Published: 25 March 2022
(This article belongs to the Special Issue Robust and Explainable Neural Intelligence)
Large-scale functional connectivity is an important indicator of the brain’s normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal–parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline. View Full-Text
Keywords: functional connectivity; multilayer perceptron; aging; EEG; generalized synchronization functional connectivity; multilayer perceptron; aging; EEG; generalized synchronization
Show Figures

Figure 1

MDPI and ACS Style

Pitsik, E.N.; Frolov, N.S.; Shusharina, N.; Hramov, A.E. Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network. Sensors 2022, 22, 2537. https://doi.org/10.3390/s22072537

AMA Style

Pitsik EN, Frolov NS, Shusharina N, Hramov AE. Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network. Sensors. 2022; 22(7):2537. https://doi.org/10.3390/s22072537

Chicago/Turabian Style

Pitsik, Elena N., Nikita S. Frolov, Natalia Shusharina, and Alexander E. Hramov. 2022. "Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network" Sensors 22, no. 7: 2537. https://doi.org/10.3390/s22072537

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
Back to TopTop