Analyzing brain-activation responses to auditory stimuli improves the diagnosis of a disorder of consciousness by non-linear dynamic analysis of the EEG

• Published in: Scientific reports Vol. 14; no. 1; pp. 17446 - 13
• Main Authors: Qu, Sheng, Wu, Xinchun, Tang, Yaxiu, Zhang, Qi, Huang, Laigang, Cui, Baojuan, Jiao, Shengxiu, Sun, Qiangsan, Zeng, Fanshuo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.07.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/617/375/1345; 692/617/375/1399; Acoustic Stimulation - methods; Adult; Aged; Approximate entropy; Auditory discrimination learning; Auditory stimuli; Back propagation; Brain - physiopathology; Cascade forward backpropagation neural network classifier; Cerebral cortex; ChiCTR; ChiCTR2300079310; Classification; Clinical trials; Consciousness; Consciousness - physiology; Consciousness Disorders - diagnosis; Consciousness Disorders - physiopathology; Diagnosis; Disorder of consciousness; EEG; Electroencephalography; Electroencephalography - methods; Female; Hearing; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Male; Middle Aged; multidisciplinary; Music; Neural networks; Non-linear dynamics analysis; Nonlinear Dynamics; Occipital lobe; Persistent Vegetative State - diagnosis; Persistent Vegetative State - physiopathology; Prefrontal cortex; Science; Science (multidisciplinary); Sensory evaluation; Sensory stimuli; Sleep and wakefulness; Somatosensory cortex; Temporal lobe; Young Adult; Approximate entropy; Non-linear dynamics analysis; Electroencephalography; Cascade forward backpropagation neural network classifier; Disorder of consciousness
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-67825-w

Although auditory stimuli benefit patients with disorders of consciousness (DOC), the optimal stimulus remains unclear. We explored the most effective electroencephalography (EEG)-tracking method for eliciting brain responses to auditory stimuli and assessed its potential as a neural marker to improve DOC diagnosis. We collected 58 EEG recordings from patients with DOC to evaluate the classification model’s performance and optimal auditory stimulus. Using non-linear dynamic analysis (approximate entropy [ApEn]), we assessed EEG responses to various auditory stimuli (resting state, preferred music, subject’s own name [SON], and familiar music) in 40 patients. The diagnostic performance of the optimal stimulus-induced EEG classification for vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) was compared with the Coma Recovery Scale-Revision in 18 patients using the machine learning cascade forward backpropagation neural network model. Regardless of patient status, preferred music significantly activated the cerebral cortex. Patients in MCS showed increased activity in the prefrontal pole and central, occipital, and temporal cortices, whereas those in VS/UWS showed activity in the prefrontal and anterior temporal lobes. Patients in VS/UWS exhibited the lowest preferred music-induced ApEn differences in the central, middle, and posterior temporal lobes compared with those in MCS. The resting state ApEn value of the prefrontal pole (0.77) distinguished VS/UWS from MCS with 61.11% accuracy. The cascade forward backpropagation neural network tested for ApEn values in the resting state and preferred music-induced ApEn differences achieved an average of 83.33% accuracy in distinguishing VS/UWS from MCS (based on K-fold cross-validation). EEG non-linear analysis quantifies cortical responses in patients with DOC, with preferred music inducing more intense EEG responses than SON and familiar music. Machine learning algorithms combined with auditory stimuli showed strong potential for improving DOC diagnosis. Future studies should explore the optimal multimodal sensory stimuli tailored for individual patients. Trial registration: The study is registered in the Chinese Registry of Clinical Trials (Approval no: KYLL-2023-414, Registration code: ChiCTR2300079310).