Background

Schizophrenia is one of the most serious mental disorders. Currently, the diagnosis of schizophrenia mainly relies on scales and doctors' experience. Recently, functional near infrared spectroscopy (fNIRS) has been used to distinguish schizophrenia from other mental disorders. The conventional classification methods utilized time-course features from single or multiple fNIRS channels.

New method

The fNIRS data were obtained from 52 channels covering the frontotemporal cortices in 200 patients with schizophrenia and 100 healthy subjects during a Chinese verbal fluency task. The channels with significant between-group differences were selected as the seeds. Functional connectivity (FC) was calculated for each seed, and FCs with significant between-group differences were selected as the features for classification.

Results

The proposed method reduced the number of channels to 26 while achieving overall classification accuracy, sensitivity and specificity values as high as 89.67%, 93.00% and 86.00%, respectively, outperforming most of the reported results. The superior performance was attributed to the cross-scale neurological changes related to schizophrenia, which were employed by the classification method. In addition, the method provided multiple classification criteria with similar accuracy, consequently increasing the flexibility and reliability of the results.

Comparison with existing methods

This is the first fNIRS study to classify schizophrenia based on FCs. This method integrated information from regional modulation, segregation and integration. The classification performance outperformed most of the classification methods described in previous studies.

Conclusions

Our findings suggest a reliable method with a high level of accuracy and a low level of instrumental complexity to identify patients with schizophrenia.

中文翻译：

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使用前额颞功能近红外光谱通过基于种子的功能连接对精神分裂症进行分类。

背景

精神分裂症是最严重的精神障碍之一。目前，精神分裂症的诊断主要依靠量表和医生的经验。最近，功能性近红外光谱（fNIRS）已被用于区分精神分裂症和其他精神疾病。常规分类方法利用来自单个或多个fNIRS通道的时程特征。

新方法

fNIRS数据是从200例精神分裂症患者和100名健康受试者的汉语口语流利度任务的额颞叶皮质的52个通道获得的。选择组间差异显着的通道作为种子。计算每个种子的功能连接性（FC），并选择具有显着组间差异的FC作为分类特征。

结果

所提出的方法将通道数减少到26，同时实现了总体分类准确度，灵敏度和特异性值分别高达89.67％，93.00％和86.00％，胜过大多数报道的结果。优异的性能归因于与精神分裂症有关的跨尺度神经系统变化，该变化被分类方法采用。另外，该方法提供了具有相似准确性的多个分类标准，因此增加了结果的灵活性和可靠性。

与现有方法的比较

这是fNIRS首次基于FC对精神分裂症进行分类的研究。这种方法整合了来自区域调制，隔离和整合的信息。分类性能优于先前研究中描述的大多数分类方法。

结论

我们的发现提出了一种可靠的方法，该方法具有较高的准确性和较低的仪器复杂度，可用于识别精神分裂症患者。