Advancing treatment response prediction in first-episode psychosis: integrating clinical and electroencephalography features.

ABSTRACT:

Aims

Prompt 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.

Methods

We 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 r

SUBMITTER: Dominicus L 

PROVIDER: S-EPMC11962345 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature