Cabergoline Impairs Neural Network Efficiency: EEG-Based Evidence from Machine Learning Analysis and Preventive Implications for Cognitive Decline

Abstract

Objective

This study investigates the effects of the D2 receptor agonist cabergoline on neural activity associated with Visual-Spatial Working Memory (VSWM), aiming to enhance understanding of pharmacological modulation of cognitive functions and explore its potential implications for preventive medicine, particularly in cognitive health preservation.

Subjects and Methods

This study uses the OpenNeuro public dataset, which includes data from 27 healthy adults who received either cabergoline or a placebo. Electroencephalography (EEG) data were analyzed to extract key neural network features, such as Power Spectral Density (PSD), Phase Locking Value (PLV), and small-world network properties. Machine learning techniques, including Recursive Feature Elimination (RFE) and random forest classifiers, were applied to assess the impact of cabergoline on neural efficiency and synchronization.

Results

The results indicate significant changes in neural signal synchronicity and brain network efficiency following cabergoline administration, with the PLV matrix showing the most notable effect. The drug appears to disrupt neural synchronization, potentially impairing VSWM capacity. The random forest model achieved 99% test accuracy, indicating robust classification of the drug's effects on cognitive performance.

Conclusions

This study provides new insights into the neural mechanisms of D2 receptor agonists like cabergoline, revealing potential implications for cognitive health. These findings contribute to the development of cognitive health management strategies, offering valuable guidance for early detection and intervention in cognitive decline, particularly in aging or neurodegenerative conditions, with broader applications in preventive medicine.