Can quantitative EEG measures predict clinical outcome in subjects at Clinical High Risk for psychosis?: A prospective multicenter study

Mirjam J van Tricht; Stephan Ruhrmann; Martijn Arns; Ralf Müller; Mitja Bodatsch; Eva Velthorst; Johannes H T M Koelman; Lo J Bour; Katharina Zurek; Frauke Schultze-Lutter; Joachim Klosterkötter; Don H Linszen; Lieuwe de Haan; Anke Brockhaus-Dumke; Dorien H Nieman

doi:10.1016/j.schres.2014.01.019

Can quantitative EEG measures predict clinical outcome in subjects at Clinical High Risk for psychosis? A prospective multicenter study

Mirjam J van Tricht^*, Stephan Ruhrmann, Martijn Arns, Ralf Müller, Mitja Bodatsch, Eva Velthorst, Johannes H T M Koelman, Lo J Bour, Katharina Zurek, Frauke Schultze-Lutter, Joachim Klosterkötter, Don H Linszen, Lieuwe de Haan, Anke Brockhaus-Dumke, Dorien H Nieman

^*Corresponding author for this work

MHeNs - Mental Health

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

BACKGROUND: Prediction studies in subjects at Clinical High Risk (CHR) for psychosis are hampered by a high proportion of uncertain outcomes. We therefore investigated whether quantitative EEG (QEEG) parameters can contribute to an improved identification of CHR subjects with a later conversion to psychosis.

METHODS: This investigation was a project within the European Prediction of Psychosis Study (EPOS), a prospective multicenter, naturalistic field study with an 18-month follow-up period. QEEG spectral power and alpha peak frequencies (APF) were determined in 113 CHR subjects. The primary outcome measure was conversion to psychosis.

RESULTS: Cox regression yielded a model including frontal theta (HR=1.82; [95% CI 1.00-3.32]) and delta (HR=2.60; [95% CI 1.30-5.20]) power, and occipital-parietal APF (HR=.52; [95% CI .35-.80]) as predictors of conversion to psychosis. The resulting equation enabled the development of a prognostic index with three risk classes (hazard rate 0.057 to 0.81).

CONCLUSIONS: Power in theta and delta ranges and APF contribute to the short-term prediction of psychosis and enable a further stratification of risk in CHR samples. Combined with (other) clinical ratings, EEG parameters may therefore be a useful tool for individualized risk estimation and, consequently, targeted prevention.

Original language	English
Pages (from-to)	42-7
Number of pages	6
Journal	Schizophrenia Research
Volume	153
Issue number	1-3
DOIs	https://doi.org/10.1016/j.schres.2014.01.019
Publication status	Published - Mar 2014

Keywords

Adolescent
Adult
Analysis of Variance
Antipsychotic Agents
Brain Waves
Electroencephalography
Female
Fourier Analysis
Humans
Male
Neuropsychological Tests
Predictive Value of Tests
Prognosis
Prospective Studies
Psychotic Disorders
Retrospective Studies
Risk
Risk Factors
Survival Analysis
Young Adult

Access to Document

10.1016/j.schres.2014.01.019

Cite this

van Tricht, M. J., Ruhrmann, S., Arns, M., Müller, R., Bodatsch, M., Velthorst, E., Koelman, J. H. T. M., Bour, L. J., Zurek, K., Schultze-Lutter, F., Klosterkötter, J., Linszen, D. H., de Haan, L., Brockhaus-Dumke, A., & Nieman, D. H. (2014). Can quantitative EEG measures predict clinical outcome in subjects at Clinical High Risk for psychosis? A prospective multicenter study. Schizophrenia Research, 153(1-3), 42-7. https://doi.org/10.1016/j.schres.2014.01.019

@article{611ba5dc37af4b61b4138a87b4f7cda7,

title = "Can quantitative EEG measures predict clinical outcome in subjects at Clinical High Risk for psychosis?: A prospective multicenter study",

abstract = "BACKGROUND: Prediction studies in subjects at Clinical High Risk (CHR) for psychosis are hampered by a high proportion of uncertain outcomes. We therefore investigated whether quantitative EEG (QEEG) parameters can contribute to an improved identification of CHR subjects with a later conversion to psychosis.METHODS: This investigation was a project within the European Prediction of Psychosis Study (EPOS), a prospective multicenter, naturalistic field study with an 18-month follow-up period. QEEG spectral power and alpha peak frequencies (APF) were determined in 113 CHR subjects. The primary outcome measure was conversion to psychosis.RESULTS: Cox regression yielded a model including frontal theta (HR=1.82; [95% CI 1.00-3.32]) and delta (HR=2.60; [95% CI 1.30-5.20]) power, and occipital-parietal APF (HR=.52; [95% CI .35-.80]) as predictors of conversion to psychosis. The resulting equation enabled the development of a prognostic index with three risk classes (hazard rate 0.057 to 0.81).CONCLUSIONS: Power in theta and delta ranges and APF contribute to the short-term prediction of psychosis and enable a further stratification of risk in CHR samples. Combined with (other) clinical ratings, EEG parameters may therefore be a useful tool for individualized risk estimation and, consequently, targeted prevention.",

keywords = "Adolescent, Adult, Analysis of Variance, Antipsychotic Agents, Brain Waves, Electroencephalography, Female, Fourier Analysis, Humans, Male, Neuropsychological Tests, Predictive Value of Tests, Prognosis, Prospective Studies, Psychotic Disorders, Retrospective Studies, Risk, Risk Factors, Survival Analysis, Young Adult",

author = "{van Tricht}, {Mirjam J} and Stephan Ruhrmann and Martijn Arns and Ralf M{\"u}ller and Mitja Bodatsch and Eva Velthorst and Koelman, {Johannes H T M} and Bour, {Lo J} and Katharina Zurek and Frauke Schultze-Lutter and Joachim Klosterk{\"o}tter and Linszen, {Don H} and {de Haan}, Lieuwe and Anke Brockhaus-Dumke and Nieman, {Dorien H}",

year = "2014",

month = mar,

doi = "10.1016/j.schres.2014.01.019",

language = "English",

volume = "153",

pages = "42--7",

journal = "Schizophrenia Research",

issn = "0920-9964",

publisher = "Elsevier B.V.",

number = "1-3",

}

van Tricht, MJ, Ruhrmann, S, Arns, M, Müller, R, Bodatsch, M, Velthorst, E, Koelman, JHTM, Bour, LJ, Zurek, K, Schultze-Lutter, F, Klosterkötter, J, Linszen, DH, de Haan, L, Brockhaus-Dumke, A & Nieman, DH 2014, 'Can quantitative EEG measures predict clinical outcome in subjects at Clinical High Risk for psychosis? A prospective multicenter study', Schizophrenia Research, vol. 153, no. 1-3, pp. 42-7. https://doi.org/10.1016/j.schres.2014.01.019

TY - JOUR

T1 - Can quantitative EEG measures predict clinical outcome in subjects at Clinical High Risk for psychosis?

T2 - A prospective multicenter study

AU - van Tricht, Mirjam J

AU - Ruhrmann, Stephan

AU - Arns, Martijn

AU - Müller, Ralf

AU - Bodatsch, Mitja

AU - Velthorst, Eva

AU - Koelman, Johannes H T M

AU - Bour, Lo J

AU - Zurek, Katharina

AU - Schultze-Lutter, Frauke

AU - Klosterkötter, Joachim

AU - Linszen, Don H

AU - de Haan, Lieuwe

AU - Brockhaus-Dumke, Anke

AU - Nieman, Dorien H

PY - 2014/3

Y1 - 2014/3

N2 - BACKGROUND: Prediction studies in subjects at Clinical High Risk (CHR) for psychosis are hampered by a high proportion of uncertain outcomes. We therefore investigated whether quantitative EEG (QEEG) parameters can contribute to an improved identification of CHR subjects with a later conversion to psychosis.METHODS: This investigation was a project within the European Prediction of Psychosis Study (EPOS), a prospective multicenter, naturalistic field study with an 18-month follow-up period. QEEG spectral power and alpha peak frequencies (APF) were determined in 113 CHR subjects. The primary outcome measure was conversion to psychosis.RESULTS: Cox regression yielded a model including frontal theta (HR=1.82; [95% CI 1.00-3.32]) and delta (HR=2.60; [95% CI 1.30-5.20]) power, and occipital-parietal APF (HR=.52; [95% CI .35-.80]) as predictors of conversion to psychosis. The resulting equation enabled the development of a prognostic index with three risk classes (hazard rate 0.057 to 0.81).CONCLUSIONS: Power in theta and delta ranges and APF contribute to the short-term prediction of psychosis and enable a further stratification of risk in CHR samples. Combined with (other) clinical ratings, EEG parameters may therefore be a useful tool for individualized risk estimation and, consequently, targeted prevention.

AB - BACKGROUND: Prediction studies in subjects at Clinical High Risk (CHR) for psychosis are hampered by a high proportion of uncertain outcomes. We therefore investigated whether quantitative EEG (QEEG) parameters can contribute to an improved identification of CHR subjects with a later conversion to psychosis.METHODS: This investigation was a project within the European Prediction of Psychosis Study (EPOS), a prospective multicenter, naturalistic field study with an 18-month follow-up period. QEEG spectral power and alpha peak frequencies (APF) were determined in 113 CHR subjects. The primary outcome measure was conversion to psychosis.RESULTS: Cox regression yielded a model including frontal theta (HR=1.82; [95% CI 1.00-3.32]) and delta (HR=2.60; [95% CI 1.30-5.20]) power, and occipital-parietal APF (HR=.52; [95% CI .35-.80]) as predictors of conversion to psychosis. The resulting equation enabled the development of a prognostic index with three risk classes (hazard rate 0.057 to 0.81).CONCLUSIONS: Power in theta and delta ranges and APF contribute to the short-term prediction of psychosis and enable a further stratification of risk in CHR samples. Combined with (other) clinical ratings, EEG parameters may therefore be a useful tool for individualized risk estimation and, consequently, targeted prevention.

KW - Adolescent

KW - Adult

KW - Analysis of Variance

KW - Antipsychotic Agents

KW - Brain Waves

KW - Electroencephalography

KW - Female

KW - Fourier Analysis

KW - Humans

KW - Male

KW - Neuropsychological Tests

KW - Predictive Value of Tests

KW - Prognosis

KW - Prospective Studies

KW - Psychotic Disorders

KW - Retrospective Studies

KW - Risk

KW - Risk Factors

KW - Survival Analysis

KW - Young Adult

U2 - 10.1016/j.schres.2014.01.019

DO - 10.1016/j.schres.2014.01.019

M3 - Article

C2 - 24508483

SN - 0920-9964

VL - 153

SP - 42

EP - 47

JO - Schizophrenia Research

JF - Schizophrenia Research

IS - 1-3

ER -

Can quantitative EEG measures predict clinical outcome in subjects at Clinical High Risk for psychosis? A prospective multicenter study

Abstract

Keywords

Access to Document

Fingerprint

Cite this