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EEG spectral power and negative symptoms in at-risk individuals predict transition to psychosis.
Schizophr Res. 2010 Nov; 123(2-3):208-16.SR

Abstract

INTRODUCTION

EEG power in the delta, theta and beta1 bands has been shown to be positively correlated with negative symptoms in first episode psychotic patients. The present study investigates this correlation in an "at risk mental state for psychosis" (ARMS) with the aim to improve prediction of transition to psychosis.

METHODS

Thirteen ARMS patients with later transition to psychosis (ARMS-T) and fifteen without (follow-up period of at least 4 years) (ARMS-NT) were investigated using spectral resting EEG data (of 8 electrodes over the fronto-central scalp area placed according to the 10-20 system) and summary score of the Scale for the Assessment of Negative Symptoms (SANS). Linear regressions were used to evaluate the correlation of SANS and EEG power in seven bands (delta, theta, alpha1, alpha2, beta1, beta2, beta3) in both ARMS groups and logistic regressions were used to predict transition to psychosis. Potentially confounding factors were controlled.

RESULTS

ARMS-T and ARMS-NT showed differential correlations of EEG power and SANS in delta, theta, and beta1 bands (p<.05): ARMS-T showed positive and ARMS-NT negative correlations. Logistic regressions showed that neither SANS score nor EEG spectral power alone predicted transition to psychosis. However, SANS score in combination with power in the delta, theta, beta1, and beta2 bands, respectively, predicted transition significantly (p<.03).

CONCLUSIONS

ARMS-T and ARMS-NT show differential correlations of SANS summary score and EEG power in delta, theta, and beta bands. Prediction of transition to psychosis is possible using combined information from a negative symptom scale and EEG spectral data.

Authors+Show Affiliations

University Psychiatric Outpatient Department, Psychiatric University Clinics, Basel, Switzerland. ZimmermannR@uhbs.chNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

20850950

Citation

Zimmermann, Ronan, et al. "EEG Spectral Power and Negative Symptoms in At-risk Individuals Predict Transition to Psychosis." Schizophrenia Research, vol. 123, no. 2-3, 2010, pp. 208-16.
Zimmermann R, Gschwandtner U, Wilhelm FH, et al. EEG spectral power and negative symptoms in at-risk individuals predict transition to psychosis. Schizophr Res. 2010;123(2-3):208-16.
Zimmermann, R., Gschwandtner, U., Wilhelm, F. H., Pflueger, M. O., Riecher-Rössler, A., & Fuhr, P. (2010). EEG spectral power and negative symptoms in at-risk individuals predict transition to psychosis. Schizophrenia Research, 123(2-3), 208-16. https://doi.org/10.1016/j.schres.2010.08.031
Zimmermann R, et al. EEG Spectral Power and Negative Symptoms in At-risk Individuals Predict Transition to Psychosis. Schizophr Res. 2010;123(2-3):208-16. PubMed PMID: 20850950.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - EEG spectral power and negative symptoms in at-risk individuals predict transition to psychosis. AU - Zimmermann,Ronan, AU - Gschwandtner,Ute, AU - Wilhelm,Frank H, AU - Pflueger,Marlon O, AU - Riecher-Rössler,Anita, AU - Fuhr,Peter, Y1 - 2010/09/20/ PY - 2010/01/06/received PY - 2010/08/12/revised PY - 2010/08/22/accepted PY - 2010/9/21/entrez PY - 2010/9/21/pubmed PY - 2011/2/22/medline SP - 208 EP - 16 JF - Schizophrenia research JO - Schizophr Res VL - 123 IS - 2-3 N2 - INTRODUCTION: EEG power in the delta, theta and beta1 bands has been shown to be positively correlated with negative symptoms in first episode psychotic patients. The present study investigates this correlation in an "at risk mental state for psychosis" (ARMS) with the aim to improve prediction of transition to psychosis. METHODS: Thirteen ARMS patients with later transition to psychosis (ARMS-T) and fifteen without (follow-up period of at least 4 years) (ARMS-NT) were investigated using spectral resting EEG data (of 8 electrodes over the fronto-central scalp area placed according to the 10-20 system) and summary score of the Scale for the Assessment of Negative Symptoms (SANS). Linear regressions were used to evaluate the correlation of SANS and EEG power in seven bands (delta, theta, alpha1, alpha2, beta1, beta2, beta3) in both ARMS groups and logistic regressions were used to predict transition to psychosis. Potentially confounding factors were controlled. RESULTS: ARMS-T and ARMS-NT showed differential correlations of EEG power and SANS in delta, theta, and beta1 bands (p<.05): ARMS-T showed positive and ARMS-NT negative correlations. Logistic regressions showed that neither SANS score nor EEG spectral power alone predicted transition to psychosis. However, SANS score in combination with power in the delta, theta, beta1, and beta2 bands, respectively, predicted transition significantly (p<.03). CONCLUSIONS: ARMS-T and ARMS-NT show differential correlations of SANS summary score and EEG power in delta, theta, and beta bands. Prediction of transition to psychosis is possible using combined information from a negative symptom scale and EEG spectral data. SN - 1573-2509 UR - https://www.unboundmedicine.com/medline/citation/20850950/EEG_spectral_power_and_negative_symptoms_in_at_risk_individuals_predict_transition_to_psychosis_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0920-9964(10)01502-1 DB - PRIME DP - Unbound Medicine ER -