Abstract
Precise diagnosis of psychiatric diseases and a comprehensive assessment of a patient's symptom severity are important in order to establish a successful treatment strategy for each patient. Although great efforts have been devoted to searching for diagnostic biomarkers of schizophrenia over the past several decades, no study has yet investigated how accurately these biomarkers are able to estimate an individual patient's symptom severity. In this study, we applied electrophysiological biomarkers obtained from electroencephalography (EEG) analyses to an estimation of symptom severity scores of patients with schizophrenia. EEG signals were recorded from 23 patients while they performed a facial affect discrimination task. Based on the source current density analysis results, we extracted voxels that showed a strong correlation between source activity and symptom scores. We then built a prediction model to estimate the symptom severity scores of each patient using the source activations of the selected voxels. The symptom scores of the Positive and Negative Syndrome Scale (PANSS) were estimated using the linear prediction model. The results of leave-one-out cross validation (LOOCV) showed that the mean errors of the estimated symptom scores were 3.34 ± 2.40 and 3.90 ± 3.01 for the Positive and Negative PANSS scores, respectively. The current pilot study is the first attempt to estimate symptom severity scores in schizophrenia using quantitative EEG features. It is expected that the present method can be extended to other cognitive paradigms or other psychological illnesses.
Highlights
Since patients with schizophrenia have their own unique signs and symptoms that generally show dramatic changes over the progress of the illness or during treatment, it is of great importance to precisely evaluate the symptom severity of each patient
We found that patients with schizophrenia showed significantly decreased neuronal activations during facial affective processing compared to normal controls, and showed significant correlations between such neuronal activations and Positive and Negative Syndrome Scale (PANSS) scores (Kim et al, 2013)
In our previous study that used the same patient data, we have shown that source activation of N170 estimated using sLORETA was decreased in patients with schizophrenia compared to normal controls in multiple brain areas (Jung et al, 2012)
Summary
Since patients with schizophrenia have their own unique signs and symptoms that generally show dramatic changes over the progress of the illness or during treatment, it is of great importance to precisely evaluate the symptom severity of each patient. There is a growing consensus that the precise estimation of symptom severity is of great necessity as it can be used to predict quality of life (Bow-Thomas et al, 1999), long-term outcome (Harrison et al, 1996; Ho et al, 1998), relapse (Birchwood et al, 1989), and functional outcomes (Mueser et al, 1991; Bowie et al, 2008); to the best of our knowledge, no study has yet investigated how accurately such electrophysiological biomarkers can predict symptom severity of each individual patient. We found that patients with schizophrenia showed significantly decreased neuronal activations during facial affective processing compared to normal controls, and showed significant correlations between such neuronal activations and Positive and Negative Syndrome Scale (PANSS) scores (Kim et al, 2013) Based on these findings, we established a mathematical prediction model based on electrophysiological biomarkers to estimate the Positive/Negative PANSS scores of individual patients
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