Xiaoyu Ji1, Wenxiang Quan2, Lei Yang1, Juan Chen3, Jiuju Wang2, Tongning Wu4. 1. China Academy of Information and Communications Technology, Beijing, China. 2. Peking University Sixth Hospital, Beijing 100191, China; Peking University Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China. 3. Massachusetts General Hospital, Charlestown 02129, USA. 4. China Academy of Information and Communications Technology, Beijing, China. Electronic address: wutongning@caict.ac.cn.
Abstract
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.
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.
Authors: Fangyuan Tian; Hongxia Li; Shuicheng Tian; Chenning Tian; Jiang Shao Journal: Int J Environ Res Public Health Date: 2022-01-03 Impact factor: 3.390
Authors: Fangyuan Tian; Hongxia Li; Shuicheng Tian; Jiang Shao; Chenning Tian Journal: Int J Environ Res Public Health Date: 2022-04-01 Impact factor: 3.390