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.
The intrinsic probability density function (iPDF) is a new quantitative method for evaluating dynamic interactions between brain regions that underlie conscious states. The method analyzes EEG signals by decomposing them into intrinsic mode functions and generating scale-dependent probability density functions that capture subtle variations in neural modulation. Testing across general anesthesia, sleep stages (wakefulness, REM, deep sleep), sensory conditions (eyes open vs closed), and between dementia patients and healthy subjects showed that active neural interactions during wakefulness and REM sleep produce super-Gaussian iPDF patterns, while reduced interactions during anesthesia and deep sleep yield near-Gaussian profiles. A classification model using iPDF features achieved approximately 87% accuracy in distinguishing dementia patients from healthy controls, suggesting iPDF as a potential clinical biomarker.