Objective assessment of emotion regulation and resilience in clinical neuropsychology currently relies on self-report, which is subject to bias. This review proposes EEG entropy modulation as a candidate brain-based biomarker. Neural complexity, measured by entropy, reflects the flexible information processing underlying adaptive self-regulation. Evidence shows diminished neural complexity in emotional dysregulation and anxiety, while interventions like mindfulness may restore it. The modulation of entropy during cognitive-emotional tasks, rather than static resting-state measures, provides a more ecologically valid marker of regulatory capacity. Future research should explore task-based entropy modulation in regulatory hubs like the prefrontal cortex and integrate these data with machine learning to identify 'entropy profiles' of dysregulation and predict therapeutic response.
Frequent cannabis use is associated with reduced neural signal complexity in the prefrontal cortex, as measured by multiscale entropy (MSE) of resting-state EEG. In 57 adults—non-users, low-frequency users (≤1x/week), and frequent users (≥2x/week)—MSE increased with coarser temporal scales in all groups, but the slope was significantly flatter in frequent users. From medium to very-coarse scales, their prefrontal entropy slope was 0.12 to 0.16 bits lower than non-users. Across all participants, the prefrontal cortex showed lower MSE than parietal, occipital, and temporal lobes, with larger differences at coarser scales. MSE detects cannabis-related reductions in prefrontal signal complexity at longer temporal scales.