School of Software, Beijing Institute of Technology, Beijing, China.
School of Optics and Electronics, Beijing Institute of Technology, Beijing, China.
BMC Med Inform Decis Mak. 2017 Dec 20;17(Suppl 3):166. doi: 10.1186/s12911-017-0559-5.
Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal people and patients, which easily lead to misdiagnosis. In recent years, functional Near-Infrared Spectroscopy (fNIRS) has been widely used in clinical diagnosis, it can get the hemoglobin concentration through the variation of optical intensity.
Firstly, the prefrontal brain networks were constructed based on oxy-Hb signals from 52-channel fNIRS data of schizophrenia and healthy controls. Then, Complex Brain Network Analysis (CBNA) was used to extract features from the prefrontal brain networks. Finally, a classier based on Support Vector Machine (SVM) is designed and trained to discriminate schizophrenia from healthy controls. We recruited a sample which contains 34 healthy controls and 42 schizophrenia patients to do the one-back memory task. The hemoglobin response was measured in the prefrontal cortex during the task using a 52-channel fNIRS system.
The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 85.5%, 92.8% for schizophrenia samples and 76.5% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.
Our results suggested that, using the appropriate classification method, fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.
精神分裂症是一种严重的精神疾病。由于缺乏客观的生理数据支持和统一的数据分析方法，医生只能依靠数据的主观经验来区分正常人和患者，这容易导致误诊。近年来，功能近红外光谱（fNIRS）已广泛应用于临床诊断，它可以通过光强度的变化获得血红蛋白浓度。
首先，基于精神分裂症和健康对照组的 52 通道 fNIRS 数据的氧合血红蛋白信号构建前额叶脑网络。然后，使用复杂脑网络分析（CBNA）从前额叶脑网络中提取特征。最后，设计并训练基于支持向量机（SVM）的分类器，以区分精神分裂症和健康对照组。我们招募了一个包含 34 名健康对照组和 42 名精神分裂症患者的样本，让他们完成 1 次记忆任务。使用 52 通道 fNIRS 系统在任务期间测量前额叶皮层中的血红蛋白反应。
实验结果表明，所提出的方法可以达到令人满意的分类精度，对精神分裂症样本的准确率为 85.5%，92.8%，对健康对照组的准确率为 76.5%。此外，我们的结果表明，fNIRS 具有成为精神分裂症诊断的有效客观生物标志物的潜力。
使用适当的分类方法，fNIRS 具有成为精神分裂症诊断的有效客观生物标志物的潜力。
