Kim Do-Won, Lee Seung-Hwan, Shim Miseon, Im Chang-Hwan
Department of Biomedical Engineering, Chonnam National UniversityYeosu, South Korea.
Psychiatry Department, Ilsan Paik Hospital, Inje UniversityGoyang, South Korea.
Front Neurosci. 2017 Aug 3;11:436. doi: 10.3389/fnins.2017.00436. eCollection 2017.
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.
准确诊断精神疾病并全面评估患者症状严重程度对于为每位患者制定成功的治疗策略至关重要。尽管在过去几十年里人们付出了巨大努力来寻找精神分裂症的诊断生物标志物，但尚无研究调查这些生物标志物能够多准确地估计个体患者的症状严重程度。在本研究中，我们将从脑电图（EEG）分析中获得的电生理生物标志物应用于精神分裂症患者症状严重程度评分的估计。在23名患者执行面部表情辨别任务时记录EEG信号。基于源电流密度分析结果，我们提取了在源活动与症状评分之间显示出强相关性的体素。然后我们构建了一个预测模型，使用所选体素的源激活来估计每位患者的症状严重程度评分。使用线性预测模型估计阳性和阴性症状量表（PANSS）的症状评分。留一法交叉验证（LOOCV）结果显示，阳性和阴性PANSS评分的估计症状评分平均误差分别为3.34±2.40和3.90±3.01。当前的初步研究是首次尝试使用定量EEG特征估计精神分裂症患者的症状严重程度评分。预计本方法可扩展到其他认知范式或其他心理疾病。
