背景:目前没有客观的生物标志物可以量化精神分裂症的阴性症状。因此,这项研究探索了声学特征在识别精神分裂症患者阴性症状严重程度中的应用。
方法:我们招募了79名根据《精神障碍诊断和统计手册》诊断为精神分裂症的住院患者,第四版(精神分裂症组)在北京回龙观医院,中国,和来自周围社区的79名健康对照(对照组)。我们使用阳性和阴性症状量表(PANSS)和简短阴性症状量表(BNSS)评估精神分裂症患者的临床症状,并记录每个参与者在情绪上积极阅读时的声音,中性,和负面文本。Praat软件用于分析和提取录音中的声学特征,如抖动,shimmer,和音高。分析两组参与者的声学差异以及患者组声学特征与临床症状的关系。
结果:精神分裂症组和对照组在音高、声音中断,抖动,shimmer,和平均谐波噪声比(p<0.05)。抖动与BNSS的迟钝情感和失语分量表得分呈负相关,在积极和中性的情绪条件下,但在负面情绪状态下相关性消失。然而,在所有三种情绪条件下,shimmer与BNSS的迟钝情绪和失语分量表得分均呈稳定的负相关。线性回归分析表明,抖动,shimmer,和年龄是BNSS子量表得分的统计学显著预测因子。
结论:精神分裂症患者和健康对照组的声学情绪表达不同。一些声学特征与阴性症状的严重程度有关,不管语义情绪如何,因此可能是阴性症状的客观生物标志物。评估声乐声学特征的系统方法可以为评估精神分裂症的阴性症状提供准确可行的方法。
非ASSIGNED:精神分裂症患者和健康对照组之间的声学情绪表达不同。评估声音声学的系统方法可以提供评估精神分裂症阴性症状的准确可行的方法。
BACKGROUND: There are currently no objective biomarkers that allow the quantification of negative symptoms of schizophrenia. This study therefore explored the use of acoustic features in identifying the severity of negative symptoms in patients with schizophrenia.
METHODS: We recruited 79 inpatients who were diagnosed with schizophrenia according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (the schizophrenia group) at the Huilongguan Hospital in Beijing, China, and 79 healthy controls from the surrounding community (the control group). We assessed the clinical symptoms of the patients with schizophrenia using the Positive and Negative Syndrome Scale (PANSS) and the Brief Negative Symptom Scale (BNSS) and recorded the voice of each participant as they read emotionally positive, neutral, and negative texts. The Praat software was used to analyse and extract acoustic characteristics from the recordings, such as jitter,
shimmer, and pitch. The acoustic differences between the two groups of participants and the relationship between acoustic characteristics and clinical symptoms in the patient group were analysed.
RESULTS: There were significant differences between the schizophrenia and control groups in pitch, voice breaks, jitter,
shimmer, and the mean harmonics-to-noise ratio (p < 0.05). Jitter was negatively correlated with the blunted affect and alogia subscale scores of the BNSS, both in the positive and neutral emotion conditions, but the correlation disappeared in the negative emotion condition. However,
shimmer exhibited a stable negative correlation with the blunted affect and alogia subscale scores of the BNSS in all three emotion conditions. A linear regression analysis showed that pitch, jitter,
shimmer, and age were statistically significant predictors of BNSS subscale scores.
CONCLUSIONS: Acoustic emotional expression differs between patients with schizophrenia and healthy controls. Some acoustic characteristics are related to the severity of negative symptoms, regardless of semantic emotions, and may therefore be objective biomarkers of negative symptoms. A systematic method for assessing vocal acoustic characteristics could provide an accurate and feasible means of assessing negative symptoms in schizophrenia.
UNASSIGNED: Acoustic emotional expression differs between patients with schizophrenia and healthy controls. A systematic method for assessing vocal acoustics could provide an accurate and feasible means of assessing negative symptoms in schizophrenia.