Critical evaluation of auditory event-related potential deficits in schizophrenia: evidence from large-scale single-subject pattern classification

TY - JOUR

T1 - Critical evaluation of auditory event-related potential deficits in schizophrenia: evidence from large-scale single-subject pattern classification

AU - Neuhaus, Andres H

AU - Popescu, Florin C

AU - Rentzsch, Johannes

AU - Gallinat, Jürgen

N1 - © The Author 2013. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

PY - 2014/9

Y1 - 2014/9

N2 - Event-related potential (ERP) deficits associated with auditory oddball and click-conditioning paradigms are among the most consistent findings in schizophrenia and are discussed as potential biomarkers. However, it is unclear to what extend these ERP deficits distinguish between schizophrenia patients and healthy controls on a single-subject level, which is of high importance for potential translation to clinical routine. Here, we investigated 144 schizophrenia patients and 144 matched controls with an auditory click-conditioning/oddball paradigm. P50 and N1 gating ratios as well as target-locked N1 and P3 components were submitted to conventional general linear models and to explorative machine learning algorithms. Repeated-measures ANOVAs revealed significant between-group differences for the oddball-locked N1 and P3 components but not for any gating measure. Machine learning-assisted analysis achieved 77.7% balanced classification accuracy using a combination of target-locked N1 and P3 amplitudes as classifiers. The superiority of machine learning over repeated-measures analysis for classifying schizophrenia patients was in the range of about 10% as quantified by receiver operating characteristics. For the first time, our study provides large-scale single-subject classification data on auditory click-conditioning and oddball paradigms in schizophrenia. Although our study exemplifies how automated inference may substantially improve classification accuracy, our data also show that the investigated ERP measures show comparably poor discriminatory properties in single subjects, thus illustrating the need to establish either new analytical approaches for these paradigms or other paradigms to investigate the disorder.

AB - Event-related potential (ERP) deficits associated with auditory oddball and click-conditioning paradigms are among the most consistent findings in schizophrenia and are discussed as potential biomarkers. However, it is unclear to what extend these ERP deficits distinguish between schizophrenia patients and healthy controls on a single-subject level, which is of high importance for potential translation to clinical routine. Here, we investigated 144 schizophrenia patients and 144 matched controls with an auditory click-conditioning/oddball paradigm. P50 and N1 gating ratios as well as target-locked N1 and P3 components were submitted to conventional general linear models and to explorative machine learning algorithms. Repeated-measures ANOVAs revealed significant between-group differences for the oddball-locked N1 and P3 components but not for any gating measure. Machine learning-assisted analysis achieved 77.7% balanced classification accuracy using a combination of target-locked N1 and P3 amplitudes as classifiers. The superiority of machine learning over repeated-measures analysis for classifying schizophrenia patients was in the range of about 10% as quantified by receiver operating characteristics. For the first time, our study provides large-scale single-subject classification data on auditory click-conditioning and oddball paradigms in schizophrenia. Although our study exemplifies how automated inference may substantially improve classification accuracy, our data also show that the investigated ERP measures show comparably poor discriminatory properties in single subjects, thus illustrating the need to establish either new analytical approaches for these paradigms or other paradigms to investigate the disorder.

KW - Adolescent

KW - Adult

KW - Aged

KW - Analysis of Variance

KW - Artificial Intelligence

KW - Biomarkers

KW - Data Interpretation, Statistical

KW - Evoked Potentials

KW - Evoked Potentials, Auditory

KW - Female

KW - Humans

KW - Male

KW - Middle Aged

KW - Schizophrenia

KW - Young Adult

U2 - 10.1093/schbul/sbt151

DO - 10.1093/schbul/sbt151

M3 - SCORING: Journal article

C2 - 24150041

VL - 40

SP - 1062

EP - 1071

JO - SCHIZOPHRENIA BULL

JF - SCHIZOPHRENIA BULL

SN - 0586-7614

IS - 5

ER -