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Titre du document / Document title

A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy

Auteur(s) / Author(s)

KHODAYARI-ROSTAMABAD Ahmad (1) ; HASEY Gary M. (2 3 4) ; MACCRIMMON Duncan J. (2 3) ; REILLY James P. (1) ; DE BRUIN Hubert (1 4) ;

Affiliation(s) du ou des auteurs / Author(s) Affiliation(s)

(1) Electrical and Computer Eng. Dept., McMaster University, Hamilton, ON, L8S 4K1, CANADA
(2) Dept. of Psychiatry and Behavioral Neurosciences, Faculty of Health Sciences, McMaster University, Hamilton, ON, L8S 4L8, CANADA
(3) Mood Disorders Program, Centre for Mountain Health Services, St. Joseph Hospital, Hamilton, ON, L8N 3K7, CANADA
(4) School of Biomedical Engineering, McMaster University, Hamilton, ON, L8S 4K1, CANADA

Résumé / Abstract

Objective: To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia. Methods: Pre-treatment EEG data are collected in 23 + 14 schizophrenic adults. Treatment outcome, after at least one year follow-up, is determined using clinical ratings by a trained clinician blind to EEG results. First, a feature selection scheme is employed to select a reduced subset of features extracted from the subjects' EEG that is most statistically relevant to our treatment-response prediction. These features are then entered into a classifier, which is realized in the form of a kernel partial least squares regression method that performs response prediction. Various scales, including the positive and negative syndrome scale (PANSS) are used as treatment-response indicators. Results: We determined that a set of discriminating EEG features do exist. A low-dimensional representation of the feature space showed significant clustering into clozapine responder and non-responder groups. The minimum level of performance of the proposed prediction methodology, tested over a range of conditions using the leave-one-out cross-validation method using the original 23 subjects, with further testing in an independent sample of 14 subjects, was 85%. Conclusions: These findings indicate that analysis of pre-treatment EEG data can predict the clinical response to clozapine in treatment resistant schizophrenia. Significance: If replicated in a larger population, this novel approach to EEG analysis may assist the clinician in determining treatment-efficacy.

Revue / Journal Title

Clinical neurophysiology    ISSN  1388-2457 

Source / Source

2010, vol. 121, no12, pp. 1998-2006 [9 page(s) (article)] (1/2 p.)

Langue / Language

Anglais

Editeur / Publisher

Elsevier, Oxford, ROYAUME-UNI  (1999) (Revue)

Mots-clés anglais / English Keywords

Psychosis

;

Electrophysiology

;

Acquisition process

;

Human

;

Performance

;

Minimum

;

Representation

;

Least squares method

;

Schizophrenia

;

Kernel method

;

Prediction

;

Selection

;

Chronic

;

Electroencephalography

;

Treatment

;

Methodology

;

Learning

;

Mots-clés français / French Keywords

Psychose

;

Electrophysiologie

;

Processus acquisition

;

Homme

;

Performance

;

Minimum

;

Représentation

;

Méthode moindre carré

;

Schizophrénie

;

Méthode noyau

;

Prédiction

;

Sélection

;

Chronique

;

Electroencéphalographie

;

Traitement

;

Méthodologie

;

Apprentissage

;

Mots-clés espagnols / Spanish Keywords

Psicosis

;

Electrofisiología

;

Proceso adquisición

;

Hombre

;

Rendimiento

;

Mínimo

;

Representación

;

Método cuadrado menor

;

Esquizofrenia

;

Método núcleo

;

Predicción

;

Selección

;

Crónico

;

Electroencefalografía

;

Tratamiento

;

Metodología

;

Aprendizaje

;

Mots-clés d'auteur / Author Keywords

Schizophrenia

;

Clozapine

;

EEG

;

Treatment-efficacy prediction

;

Machine learning

;

Psychiatry

;

Localisation / Location

INIST-CNRS, Cote INIST : 5626 E, 35400019343954.0060

Nº notice refdoc (ud4) : 23428783



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