EEG entropy modulation as a biomarker of emotion regulation and resilience.

Summary

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.

Study at a glance

Abstract

A fundamental challenge in clinical neuropsychology is the objective assessment of emotion regulation and resilience. Current reliance on self-report is hampered by bias, creating a critical need for brain-based biomarkers that can capture the dynamics of emotional functioning. This review advances EEG entropy modulation as a theoretically appealing and emerging candidate. Converging research indicates that neural complexity, quantified by entropy measures, reflects the flexible information processing that underpins adaptive self-regulation. We synthesize evidence demonstrating diminished neural complexity in emotional dysregulation and anxiety, while interventions like mindfulness may work by restoring it. Moving beyond static, resting-state analysis, we argue that the modulation of entropy during cognitive-emotional tasks provides a far more powerful and ecologically valid marker of regulatory capacity. This dynamic approach, enhanced by emerging methods for capturing EEG in real-world settings, captures the brain's flexible response to challenges. Future research will benefit from exploring task-based entropy modulation in key regulatory hubs like the prefrontal cortex. Integrating these data with machine and deep learning approaches is a critical frontier, poised to identify distinct 'entropy profiles' of dysregulation and predict therapeutic response with an objectivity current methods lack. This interdisciplinary agenda seeks to bridge the gap between subjective experience and objective physiology, transforming how we assess and improve mental health outcomes.