Dynamic reconstruction of electroencephalogram data using RBF neural networks

Frontiers in Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: March 28, 2025

Electroencephalography (EEG) is widely used for analyzing brain activity; however, the nonlinear and nature of EEG signals presents significant challenges traditional analysis methods. Machine has shown great promise in addressing these limitations. This study proposes a novel approach using Radial Function (RBF) neural networks optimized by Particle Swarm Optimization (PSO) to reconstruct dynamics extract age-related characteristics. recordings were collected from 142 participants spanning multiple age groups. Signals preprocessed through bandpass filtering (1-35 Hz) Independent Component Analysis (ICA) artifact removal. network was trained on time-series data with PSO employed optimize model parameters identify fixed points reconstructed system. Statistical analyses including ANOVA Kruskal-Wallis tests performed assess differences fixed-point coordinates. The RBF demonstrated high accuracy signal reconstruction across different frequency normalized root mean square error (NRMSE) 0.0671 ± 0.0074 Pearson correlation coefficient 0.0678. Spectral time-frequency confirmed s capability accurately capture oscillations. Importantly coordinates revealed distinct age-related. These findings suggest that can serve as quantitative markers aging providing new insights into age-dependent changes dynamics. proposed method offers computationally efficient interpretable potential applications neurological diagnosis cognitive research.

Language: Английский

Artificial Intelligence in Central-Peripheral Interaction Organ Crosstalk: The Future of Drug Discovery and Clinical Trials

Pharmacological Research, Journal Year: 2025, Volume and Issue: unknown, P. 107734 - 107734

Published: April 1, 2025

Drug discovery before the 20th century often focused on single genes, molecules, cells, or organs, failing to capture complexity of biological systems. The emergence protein-protein interaction network studies in 2001 marked a turning point and promoted holistic approach that considers human body as an interconnected system. This is particularly evident study bidirectional interactions between central nervous system (CNS) peripheral which are critical for understanding health disease. Understanding these complex requires integrating multi-scale, heterogeneous data from molecular organ levels, encompassing both omics (e.g., genomics, proteomics, microbiomics) non-omics imaging, clinical phenotypes). Artificial intelligence (AI), multi-modal models, has demonstrated significant potential analyzing CNS-peripheral by processing vast, datasets. Specifically, AI facilitates identification biomarkers, prediction therapeutic targets, simulation drug effects multi-organ systems, thereby paving way novel strategies. review highlights AI's transformative role research, focusing its applications unraveling disease mechanisms, discovering optimizing trials through patient stratification adaptive trial design.

Language: Английский