Awareness- and Arousal-specific Brain Analysis Predicts Spinal Cord Stimulation Effectiveness in Disorders of Consciousness

Spinal cord stimulation (SCS) is an advanced treatment for disorders of consciousness (DoC), but its success rate varies between 30 and 60%, and consciousness-related biomarkers are urgently needed for SCS assessment and optimization. This paper proposes an awareness- and arousal-specific brain analysis (AAA) method to encode these two consciousness dimensions and construct consciousness-related features to predict SCS outcomes of DoC patients. Firstly, electroencephalo gram (EEG) brain signals were collected from twenty-eight DoC patients during SCS treatment. Then, dynamic brain networks are formulated based on sliding-window correlation analysis of weighted phase lag index. Afterwards, a hierarchical network decomposition algorithm was developed to resolve the dynamic networks into consciousness related networks by elaborating data-driven optimization of non-negative matrix factorization and consciousness related variability. Further, consciousness features are designed to quantify the activation, interaction, and stability of awareness- and arousal-specific networks, and a support vector machine is trained for classification of SCS treatment effectiveness. Clinical results showed that our method achieved an overall prediction accuracy of 88%, which is significantly better than clinical accuracy of 50% by doctors. Moreover, our method outperformed existing EEG-based approaches in both prediction accuracy and pathology interpretability. The proposed awareness- and arousal-specific brain analysis method establishes a pivotal framework for precise and reliable SCS treatment of DoC.