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

2026-03-14

AI Summary

This study addresses the substantial inter-individual variability and poor predictability of treatment response in transcutaneous auricular vagus nerve stimulation (taVNS) for negative symptoms of treatment-resistant schizophrenia. Leveraging pre-treatment electroencephalography (EEG) features, the authors developed a machine learning model with nested cross-validation to predict therapeutic outcomes. They identified, for the first time, an EEG oscillatory biomarker centered on fronto-parietal and fronto-temporal coherence, which demonstrated high predictive validity (r = 0.87, p < 0.001) and potential as a neuromodulation target. This biomarker exhibited significant specificity in the active taVNS group but showed no predictive power in the sham group, underscoring its biological specificity and clinical translational promise.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• treatment-resistant schizophrenia
• transcutaneous auricular vagus nerve stimulation
• negative symptoms
• response variability
• predictive biomarkers

Innovation

• EEG coherence
• machine learning
• transcutaneous auricular vagus nerve stimulation
• treatment-resistant schizophrenia
• neuromarkers