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.
Lu et al. (Thu,) studied this question.