What question did this study set out to answer?

To explore EEG microstate dynamics in chronic insomnia patients and predict treatment response to electroacupuncture.

March 2, 2026Open Access

Electroacupuncture modulates electroencephalographic microstate dynamics to alleviate chronic insomnia: a machine learning approach for predicting individual treatment response

Key Points

To explore EEG microstate dynamics in chronic insomnia patients and predict treatment response to electroacupuncture.
Analyzed baseline resting-state EEG in 41 chronic insomnia patients and 19 healthy controls.
Evaluated changes in microstate parameters before and after 4 weeks of electroacupuncture treatment.
Compared clinical scores using scales like PSQI and Hamilton Depression Scale pre- and post-treatment.
Utilized machine learning algorithms for predicting treatment response based on microstate features.
CI patients showed significant differences in temporal metrics of EEG microstates compared to healthy controls.
Post-treatment, clinical scores improved significantly and multiple EEG microstate parameters normalized.
Random Forest model had the best predictive performance (AUC = 0.849), identifying key predictors of treatment response.

Abstract

Background Chronic insomnia (CI) is associated with dysregulation of brain network dynamics, and patient response to electroacupuncture (EA) treatment varies. This study aimed to investigate the characteristics of electroencephalographic (EEG) microstates in patients with CI, analyze changes in microstate parameters before and after EA treatment, and explore the potential application of machine learning (ML) models based on baseline microstate features for predicting treatment response. Methods We enrolled 41 CI patients and 19 healthy controls (HC). Baseline resting-state EEG was recorded, and microstate parameters (classes A–D) were analyzed. CI patients underwent 4-week EA treatment. Six clinical scales—including the Pittsburgh Sleep Quality Index (PSQI) and Hamilton Depression Scale, and microstate dynamics were compared pre- and post-treatment. Treatment response was defined as ≥50% PSQI reduction. Multi-stage feature selection and eight ML algorithms were used to build the prediction model. Results At baseline, CI patients showed differences in some temporal metrics of microstates B, A, and C compared to HC. After EA, all clinical scores improved significantly (p 0. 001). CoverageB and DurationB, as well as OccurrenceC, increased, and multiple transition probabilities were regulated—particularly, microstate B temporal indicators normalized to HC levels. In the exploratory ML modeling, RF performed best (AUC = 0. 849). “DurationA, ” “OrgTMD → B, ” and “OrgTMC → B” were the top positive predictors, while “OccurrenceC” and “DurationB” were negative predictors. Conclusion This study found that EA treatment was associated with improved clinical scores and alterations in some EEG microstate parameters in patients with CI. In this exploratory analysis with a limited sample size, baseline microstate features showed preliminary potential for predicting treatment response, though further validation in larger cohorts is needed. These findings may provide a reference for future research on neurophysiological predictors and the development of individualized treatment strategies.

Bookmark

View Full Paper

Cite This Study

Liu et al. (Fri,) studied this question.

synapsesocial.com/papers/69a528b3f1e85e5c73bf0365 https://doi.org/https://doi.org/10.3389/fneur.2026.1782826

Bookmark

View Full Paper