通过脑电波相干性预测难治性精神分裂症患者对耳迷走神经刺激的治疗反应。
Fronto-parietal and fronto-temporal EEG coherence as predictive neuromarkers of transcutaneous auricular vagus nerve stimulation response in treatment-resistant schizophrenia: A machine learning study
- 基于治疗前脑电图相干性特征构建机器学习模型。
- 模型对真实刺激组的疗效预测相关性达0.87，显著优于随机水平。
- 前顶-顶叶和前颞-顶叶相干性是关键预测指标，适合精准神经调控研究者。
难治性精神分裂症（TRS）患者对经皮耳迷走神经刺激（taVNS）治疗负性症状的反应存在显著个体差异，限制了其临床应用。本研究旨在利用脑电图（EEG）数据建立机器学习（ML）模型，以预测个体响应并探索相关神经机制。在50名参与taVNS试验的TRS患者中，采用嵌套交叉验证框架，基于治疗前的脑电特征（功率、相干性及动态功能连接）构建并验证预测模型。参与者接受20次主动或假刺激taVNS（每组25人），持续两周，随后进行两周随访。预测目标为阳性与阴性症状量表负性症状因子得分（PANSS-FSNS）从基线到治疗后的百分比变化，并进一步评估模型特异性和神经生理学意义。最优模型在主动组中准确预测了taVNS反应，预测值与实际变化高度相关（r = 0.87，p < .001）；置换检验确认性能显著高于随机水平（p < .001）。共识别出9个持续保留特征，主要为前顶-顶叶和前颞-顶叶相干性特征。假刺激组预测性能极低，且无法预测阳性症状变化，表明该振荡特征谱具有针对taVNS相关负性症状改善的特异性。两个位于前顶-顶叶-颞叶网络中的相干性特征在治疗后变化与症状改善显著相关，提示其兼具预测与潜在治疗靶点双重作用。脑电振荡神经标记物可实现对TRS患者taVNS反应的准确预测，支持机制驱动的精准神经调控策略。
原文摘要 · Abstract (English)
Response variability limits the clinical utility of transcutaneous auricular vagus nerve stimulation (taVNS) for negative symptoms in treatment-resistant schizophrenia (TRS). This study aimed to develop an electroencephalography (EEG)-based machine learning (ML) model to predict individual response and explore associated neurophysiological mechanisms. We used ML to develop and validate predictive models based on pre-treatment EEG data features (power, coherence, and dynamic functional connectivity) from 50 TRS patients enrolled in the taVNS trial, within a nested cross-validation framework. Participants received 20 sessions of active or sham taVNS (n = 25 each) over two weeks, followed by a two-week follow-up. The prediction target was the percentage change in the positive and negative syndrome scale-factor score for negative symptoms (PANSS-FSNS) from baseline to post-treatment, with further evaluation of model specificity and neurophysiological relevance.The optimal model accurately predicted taVNS response in the active group, with predicted PANSS-FSNS changes strongly correlated with observed changes (r = 0.87, p < .001); permutation testing confirmed performance above chance (p < .001). Nine consistently retained features were identified, predominantly fronto-parietal and fronto-temporal coherence features. Negligible predictive performance in the sham group and failure to predict positive symptom change support the predictive specificity of this oscillatory signature for taVNS-related negative symptom improvement. Two coherence features within fronto-parietal-temporal networks showed post-taVNS changes significantly associated with symptom improvement, suggesting dual roles as predictors and potential therapeutic targets. EEG oscillatory neuromarkers enable accurate prediction of individual taVNS response in TRS, supporting mechanism-informed precision neuromodulation strategies.
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