Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-15T23:47:58.787Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigation of electrophysiological markers to predict clinical and functional outcome of schizophrenia using sparse partial least square regression

Published online by Cambridge University Press:  13 August 2021

L. Giuliani ,
D. Popovic ,
N. Koutsouleris ,
G.M. Giordano ,
T. Koenig ,
A. Mucci ,
A. Vignapiano ,
M. Altamura ,
A. Bellomo  and
R. Brugnoli
...Show all authors
L. Giuliani*
Affiliation:
Psychiatry, Università degli Studi della Campania “Luigi Vanvitelli”, Napoli, Italy
D. Popovic
Affiliation:
Department Of Psychiatry And Psychotherapy, LMU Munich, Munich, Germany
N. Koutsouleris
Affiliation:
Department Of Psychiatry And Psychotherapy, LMU Munich, Munich, Germany
G.M. Giordano
Affiliation:
Psychiatry, Università degli Studi della Campania “Luigi Vanvitelli”, Napoli, Italy
T. Koenig
Affiliation:
Psychiatry, University Hospital of Psychiatry, Bern, Bern, Switzerland
A. Mucci
Affiliation:
Psychiatry, Università degli Studi della Campania “Luigi Vanvitelli”, Napoli, Italy
A. Vignapiano
Affiliation:
Psichiatria, ASL Napoli 1 - Centro, Napoli, Italy
M. Altamura
Affiliation:
Psychiatry, University of Foggia, Foggia, Italy
A. Bellomo
Affiliation:
Psychiatry, University of Foggia, Foggia, Italy
R. Brugnoli
Affiliation:
Psychiatry, University of Rome “La Sapienza”, Rome, Italy
G. Corrivetti
Affiliation:
Department Of Psychiatry, 10European Biomedical Research Institute of Salerno (EBRIS), salerno, Italy
G. Di Lorenzo
Affiliation:
Psychiatry, University of Rome “Tor Vergata”, Rome, Italy
P. Girardi
Affiliation:
Psychiatry, University of Rome “La Sapienza”, Rome, Italy
P. Monteleone
Affiliation:
Department Of Medicine, Surgery And Dentistry “scuola Medica Salernitana”, University of Salerno, Baronissi/Salerno, Italy
C. Niolu
Affiliation:
Department Of Systems Medicine, University of Rome “Tor Vergata”, Roma, Italy
S. Galderisi
Affiliation:
Psychiatry, Università degli Studi della Campania “Luigi Vanvitelli”, Napoli, Italy
M. Maj
Affiliation:
Psychiatry, Università degli Studi della Campania “Luigi Vanvitelli”, Napoli, Italy
*
*Corresponding author.

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Introduction

Despite innovative treatments, the impairment in real-life functioning in subjects with schizophrenia (SCZ) remains an unmet need in the care of these patients. Recently, real-life functioning in SCZ was associated with abnormalities in different electrophysiological indices. It is still not clear whether this relationship is mediated by other variables, and how the combination of different EEG abnormalities influences the complex outcome of schizophrenia.

Objectives

The purpose of the study was to find EEG patterns which can predict the outcome of schizophrenia and identify recovered patients.

Methods

Illness-related and functioning-related variables were measured in 61 SCZ at baseline and after four-years follow-up. EEGs were recorded at the baseline in resting-state condition and during two auditory tasks. We performed Sparse Partial Least Square (SPLS) Regression, using EEG features, age and illness duration to predict clinical and functional features at baseline and follow up. Through a Linear Support Vector Machine (Linear SVM) we used electrophysiological and clinical scores derived from SPLS regression, in order to classify recovered patients at follow-up.

Results

We found one significant latent variable (p<0.01) capturing correlations between independent and dependent variables at follow-up (RHO=0.56). Among individual predictors, age and illness-duration showed the highest scores; however, the score for the combination of the EEG features was higher than all other predictors. Within dependent variables, negative symptoms showed the strongest correlation with predictors. Scores resulting from SPLS Regression classified recovered patients with 90.1% of accuracy.

Conclusions

A combination of electrophysiological markers, age and illness-duration might predict clinical and functional outcome of schizophrenia after 4 years of follow-up.

Keywords

schizophréniaEEGOutcome prediction
Type
Abstract
Information
European Psychiatry , Volume 64 , Special Issue S1: Abstracts of the 29th European Congress of Psychiatry , April 2021 , pp. S542
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the European Psychiatric Association
Submit a response

Comments

No Comments have been published for this article.