Prefrontal connectivity alterations and oscillatory dynamics in cannabis use disorder.

Long-term cannabis use alters default mode network (DMN) dynamics and cross-frequency interactions in the brain. Resting-state EEG recordings from 23 participants with cannabis use disorder (CUD) and 23 healthy controls showed that delta-high gamma and beta-high gamma coupling in prefrontal regions distinguished CUD participants from controls, especially with eyes open. Frontal-parietal and frontal-temporal coherence was heightened during eye closure in CUD participants. A multilayer perceptron classifier using these multidimensional EEG features successfully differentiated cannabis users from controls, suggesting that altered DMN function and cross-frequency coupling are potential electrophysiological markers of long-term cannabis use.

Study at a glance

Abstract

Long-term cannabis use can result in the development of cannabis use disorder (CUD) and dependence via the endocannabinoid system pathway. A brain network that has been consistently linked to various stages of addiction and various substances is the default mode network (DMN). By comparing EEG features between cannabis users and healthy participants, this study sought to identify EEG-based markers in altered DMN function. A resting-state EEG recording was conducted with 23 CUD participants and 23 healthy participants for five minutes with their eyes open and closed. Data were analyzed using power spectral density (PSD), phase-amplitude coupling (PAC), functional connectivity, and multidimensional signal analysis to extract features. The results showed that a delta-high gamma and beta-high gamma coupling in the prefrontal regions highly specific to classify the top features of CUD participants when the eyes were open. Signaling adaptive modulation of neural oscillations of cannabis users was relatively stable coupling in both states. In addition, it was found that frontal-parietal and frontal-temporal coherence in CUD participants were linked at heightened levels during eye closure according to functional connectivity analysis. Using these multidimensional EEG features, the Multilayer perceptron (MLP) classifier was able to distinguish cannabis users from controls. Overall, these findings highlight altered DMN dynamics and cross-frequency interactions as potential electrophysiological markers of long-term cannabis use and demonstrate the utility of machine learning approaches for identifying neural signatures of cannabis-related brain changes.