Pre-treatment EEG microstates and response to antipsychotic drugs in first episode psychosis
- 1Feinstein Institutes for Medical Research - Northwell
Abstract
Background and HypothesisFirst-line antipsychotics are ineffective in [~]40% of people with first episode psychosis (FEP). Electroencephalogram (EEG)-based predictive biomarkers could be a scalable solution to reduce trial-and-error.
Study DesignWe acquired EEG in 56 minimally treated FEP individuals before treatment initiation, and 34 matched healthy controls (HC). Treatment response was defined as >50% improvement in BPRS scores by 12 weeks of treatment. Modified k-means clustering of resting-state EEG yielded six microstates including canonical topographies (A-D). The first principal component (PC1) of microstate properties derived from HCs served as a normative reference onto which FEP patients were projected. Baseline PC1 was used to predict treatment response in adjusted regression analyses. To test stability of results, a second dataset of HCs (n=92) was fitted using the same microstate solution and compared to our native HC dataset. PC1 scores derived from this independent normative dataset were also used for predicting response in FEP.
Study ResultsIndividual microstates replicated established patient-control differences (e.g., higher presence of microstate C compared to D, p<0.001). PC1 explained 34.8% of variance. Baseline PC1 predicted subsequent treatment response (adjusted OR=0.63, p<0.001), yielding AUC=72.3% in leave-one-out cross-validation (pperm<0.001) when combined with clinical information. PC1 from our HC transported well to the second healthy control EEG dataset (n=92) (r=0.9,p<0.001; r=0.89,p<0.001 ). PC1 in FEP derived using PC1 weights from a second normative dataset also predicted response in FEP (OR=0.70, p=0.03).
ConclusionsEEG microstate architecture differing from the expected norm is associated with lower odds of treatment response in FEP.
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BibTeX
@misc{rubio2026pretreatment,
title = {{Pre-treatment EEG microstates and response to antipsychotic drugs in first episode psychosis}},
author = {J. M. Rubio and M. Murphy and W. Vodovozov and R. Carrion and T. Lencz and M. Baradits and D. Ongur and M. Sadri and J. M. Kane and A. K. Malhotra},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.26.26364042},
url = {https://www.medrxiv.org/content/10.64898/2026.09.26.26364042v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Pre-treatment EEG microstates and response to antipsychotic drugs in first episode psychosis
AU - J. M. Rubio
AU - M. Murphy
AU - W. Vodovozov
AU - R. Carrion
AU - T. Lencz
AU - M. Baradits
AU - D. Ongur
AU - M. Sadri
AU - J. M. Kane
AU - A. K. Malhotra
PY - 2026
DA - 2026/09/26
PB - medRxiv
DO - 10.64898/2026.09.26.26364042
UR - https://www.medrxiv.org/content/10.64898/2026.09.26.26364042v1
AB - Background and HypothesisFirst-line antipsychotics are ineffective in [~]40% of people with first episode psychosis (FEP). Electroencephalogram (EEG)-based predictive biomarkers could be a scalable solution to reduce trial-and-error. Study DesignWe acquired EEG in 56 minimally treated FEP individuals before treatment initiation, and 34 matched healthy controls (HC). Treatment response was defined as >50% improvement in BPRS scores by 12 weeks of treatment. Modified k-means clustering of resting-state EEG yielded six microstates including canonical topographies (A-D). The first principal component (PC1) of microstate properties derived from HCs served as a normative reference onto which FEP patients were projected. Baseline PC1 was used to predict treatment response in adjusted regression analyses. To test stability of results, a second dataset of HCs (n=92) was fitted using the same microstate solution and compared to our native HC dataset. PC1 scores derived from this independent normative dataset were also used for predicting response in FEP. Study ResultsIndividual microstates replicated established patient-control differences (e.g., higher presence of microstate C compared to D, p<0.001). PC1 explained 34.8% of variance. Baseline PC1 predicted subsequent treatment response (adjusted OR=0.63, p<0.001), yielding AUC=72.3% in leave-one-out cross-validation (pperm<0.001) when combined with clinical information. PC1 from our HC transported well to the second healthy control EEG dataset (n=92) (r=0.9,p<0.001; r=0.89,p<0.001 ). PC1 in FEP derived using PC1 weights from a second normative dataset also predicted response in FEP (OR=0.70, p=0.03). ConclusionsEEG microstate architecture differing from the expected norm is associated with lower odds of treatment response in FEP.
ER -