ArticlePsychiatry and clinical neurosciences2025
Advancing treatment response prediction in first-episode psychosis: integrating clinical and electroencephalography features.
Article in Psychiatry and clinical neurosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Autonomic Flexibility and Early Treatment Success: Heart Rate Variability Predicts Remission in First-Episode Psychosis.Schizophrenia bulletin · 2026Article
- Reliability and state-dependency of EEG connectivity, complexity and network characteristics.Scientific reports · 2025Article
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16 authors.
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Abstract
aimsPrompt diagnosis and intervention are crucial for first-episode psychosis (FEP) outcomes, but predicting the response to antipsychotics remains challenging. We studied whether adding electroencephalography (EEG) characteristics improves clinical prediction models for treatment response and whether EEG-based predictors are influenced by initial treatment.
methodsWe included 115 antipsychotic-naïve patients with FEP. Positive and Negative Syndrome Scale (PANSS) and sociodemographic items were included as clinical features. Additionally, we analyzed resting-state EEG data (n = 45) for (relative) power, functional connectivity, and network organization. Treatment response, measured as change in PANSS positive subscale scores (∆PANSS+), was predicted using a random forest regression model. We analyzed whether the most predictive EEG characteristics were influenced after treatment.
resultsThe clinical model explained 12% variance in symptom reduction in the training set and 32% in the validation set. Including EEG variables in the model led to a nonsignificant increase of 2% (total 34%) explained variance in symptom reduction. High hallucination symptom scores and a more hierarchical organization of alpha band networks (tree hierarchy) were associated with ∆PANSS+ reduction. The tree hierarchy in the alpha band decreased after medication. EEG source analysis revealed that this change was driven by alterations in the degree and centrality of frontal and parietal nodes in the functional brain network.
conclusionsBoth clinical and EEG characteristics can inform treatment response prediction in patients with FEP, but the combined model may not be beneficial over a clinical model. Nevertheless, adding a more objective marker such as EEG could be valuable in selected cases.
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